LongT5
Overview
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. Itβs an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
The abstract from the paper is the following:
Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global} (TGlobal), which mimics ETCβs local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.
This model was contributed by stancld. The original code can be found here.
Usage tips
- LongT5ForConditionalGeneration is an extension of T5ForConditionalGeneration exchanging the traditional encoder self-attention layer with efficient either local attention or transient-global (tglobal) attention.
- Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective inspired by the pre-training of PegasusForConditionalGeneration.
- LongT5 model is designed to work efficiently and very well on long-range sequence-to-sequence tasks where the input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens.
- For Local Attention, the sparse sliding-window local attention operation allows a given token to attend only
r
tokens to the left and right of it (withr=127
by default). Local Attention does not introduce any new parameters to the model. The complexity of the mechanism is linear in input sequence lengthl
:O(l*r)
. - Transient Global Attention is an extension of the Local Attention. It, furthermore, allows each input token to
interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed
length
k
(with a defaultk=16
). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and also every global token like in the case of standard global attention (transient represents the fact the global tokens are constructed dynamically within each attention operation). As a consequence, TGlobal attention introduces a few new parameters β global relative position biases and a layer normalization for global tokenβs embedding. The complexity of this mechanism isO(l(r + l/k))
. - An example showing how to evaluate a fine-tuned LongT5 model on the pubmed dataset is below.
>>> import evaluate
>>> from datasets import load_dataset
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
>>> dataset = load_dataset("scientific_papers", "pubmed", split="validation")
>>> model = (
... LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
... .to("cuda")
... .half()
... )
>>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
>>> def generate_answers(batch):
... inputs_dict = tokenizer(
... batch["article"], max_length=16384, padding="max_length", truncation=True, return_tensors="pt"
... )
... input_ids = inputs_dict.input_ids.to("cuda")
... attention_mask = inputs_dict.attention_mask.to("cuda")
... output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2)
... batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
... return batch
>>> result = dataset.map(generate_answer, batched=True, batch_size=2)
>>> rouge = evaluate.load("rouge")
>>> rouge.compute(predictions=result["predicted_abstract"], references=result["abstract"])
Resources
LongT5Config
class transformers.LongT5Config
< source >( vocab_size = 32128 d_model = 512 d_kv = 64 d_ff = 2048 num_layers = 6 num_decoder_layers = None num_heads = 8 local_radius = 127 global_block_size = 16 relative_attention_num_buckets = 32 relative_attention_max_distance = 128 dropout_rate = 0.1 layer_norm_epsilon = 1e-06 initializer_factor = 1.0 feed_forward_proj = 'relu' is_encoder_decoder = True encoder_attention_type = 'local' use_cache = True pad_token_id = 0 eos_token_id = 1 **kwargs )
Parameters
- vocab_size (
int
, optional, defaults to 32128) — Vocabulary size of the LongT5 model. Defines the number of different tokens that can be represented by theinputs_ids
passed when calling LongT5Model. - d_model (
int
, optional, defaults to 512) — Size of the encoder layers and the pooler layer. - d_kv (
int
, optional, defaults to 64) — Size of the key, query, value projections per attention head.d_kv
has to be equal tod_model // num_heads
. - d_ff (
int
, optional, defaults to 2048) — Size of the intermediate feed forward layer in eachLongT5Block
. - num_layers (
int
, optional, defaults to 6) — Number of hidden layers in the Transformer encoder. - num_decoder_layers (
int
, optional) — Number of hidden layers in the Transformer decoder. Will use the same value asnum_layers
if not set. - num_heads (
int
, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer encoder. - local_radius (
int
, optional, defaults to 127) — Number of tokens to the left/right for each token to locally self-attend in a local attention mechanism. - global_block_size (
int
, optional, defaults to 16) — Lenght of blocks an input sequence is divided into for a global token representation. Used only forencoder_attention_type = "transient-global"
. - relative_attention_num_buckets (
int
, optional, defaults to 32) — The number of buckets to use for each attention layer. - relative_attention_max_distance (
int
, optional, defaults to 128) — The maximum distance of the longer sequences for the bucket separation. - dropout_rate (
float
, optional, defaults to 0.1) — The ratio for all dropout layers. - layer_norm_eps (
float
, optional, defaults to 1e-6) — The epsilon used by the layer normalization layers. - initializer_factor (
float
, optional, defaults to 1) — A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). - feed_forward_proj (
string
, optional, defaults to"relu"
) — Type of feed forward layer to be used. Should be one of"relu"
or"gated-gelu"
. LongT5v1.1 uses the"gated-gelu"
feed forward projection. Original LongT5 implementation uses"gated-gelu"
. - encoder_attention_type (
string
, optional, defaults to"local"
) — Type of encoder attention to be used. Should be one of"local"
or"transient-global"
, which are supported by LongT5 implementation. - use_cache (
bool
, optional, defaults toTrue
) — Whether or not the model should return the last key/values attentions (not used by all models).
This is the configuration class to store the configuration of a LongT5Model or a FlaxLongT5Model. It is used to instantiate a LongT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LongT5 google/long-t5-local-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
LongT5Model
class transformers.LongT5Model
< source >( config: LongT5Config )
Parameters
- config (LongT5Config) — 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.
The bare LONGT5 Model transformer outputting raw hidden-states without any specific head on top.
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. Itβs an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None decoder_head_mask: typing.Optional[torch.FloatTensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.Tensor] = None decoder_inputs_embeds: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None ) β transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
torch.LongTensor
of shape(batch_size, target_sequence_length)
, optional) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
LONGT5 uses the
pad_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).To know more on how to prepare
decoder_input_ids
for pretraining take a look at LONGT5 Training. - decoder_attention_mask (
torch.BoolTensor
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default. - head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- decoder_head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- cross_attn_head_mask (
torch.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- encoder_outputs (
tuple(tuple(torch.FloatTensor)
, optional) — Tuple consists of (last_hidden_state
,optional
: hidden_states,optional
: attentions)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (
tuple(tuple(torch.FloatTensor))
of lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.If
past_key_values
are used, the user can optionally input only the lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
. - inputs_embeds (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. - decoder_inputs_embeds (
torch.FloatTensor
of shape(batch_size, target_sequence_length, hidden_size)
, optional) — Optionally, instead of passingdecoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). This is useful if you want more control over how to convertdecoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
. - use_cache (
bool
, optional) — If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
). - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - cache_position (
torch.LongTensor
of shape(sequence_length)
, optional) — Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache in the correct position and to infer the complete sequence length.
Returns
transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LongT5Config) and inputs.
-
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the decoder of the model.If
past_key_values
is used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)
is output. -
past_key_values (
tuple(tuple(torch.FloatTensor))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(torch.FloatTensor)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and 2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding. -
decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
-
decoder_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
encoder_last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Sequence of hidden-states at the output of the last layer of the encoder of the model. -
encoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
-
encoder_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The LongT5Model forward method, overrides the __call__
special method.
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 pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, LongT5Model
>>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
>>> model = LongT5Model.from_pretrained("google/long-t5-local-base")
>>> # Let's try a very long encoder input.
>>> input_ids = tokenizer(
... 100 * "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
LongT5ForConditionalGeneration
class transformers.LongT5ForConditionalGeneration
< source >( config: LongT5Config )
Parameters
- config (LongT5Config) — 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.
LONGT5 Model with a language modeling
head on top.
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. Itβs an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None decoder_head_mask: typing.Optional[torch.FloatTensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None ) β transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
torch.LongTensor
of shape(batch_size, target_sequence_length)
, optional) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
LONGT5 uses the
pad_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).To know more on how to prepare
decoder_input_ids
for pretraining take a look at LONGT5 Training. - decoder_attention_mask (
torch.BoolTensor
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default. - head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- decoder_head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- cross_attn_head_mask (
torch.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- encoder_outputs (
tuple(tuple(torch.FloatTensor)
, optional) — Tuple consists of (last_hidden_state
,optional
: hidden_states,optional
: attentions)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (
tuple(tuple(torch.FloatTensor))
of lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.If
past_key_values
are used, the user can optionally input only the lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
. - inputs_embeds (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. - decoder_inputs_embeds (
torch.FloatTensor
of shape(batch_size, target_sequence_length, hidden_size)
, optional) — Optionally, instead of passingdecoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). This is useful if you want more control over how to convertdecoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
. - use_cache (
bool
, optional) — If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
). - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - cache_position (
torch.LongTensor
of shape(sequence_length)
, optional) — Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache in the correct position and to infer the complete sequence length. - labels (
torch.LongTensor
of shape(batch_size,)
, optional) — Labels for computing the sequence classification/regression loss. Indices should be in[-100, 0, ..., config.vocab_size - 1]
. All labels set to-100
are ignored (masked), the loss is only computed for labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LongT5Config) and inputs.
-
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Language modeling loss. -
logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
past_key_values (
tuple(tuple(torch.FloatTensor))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(torch.FloatTensor)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and 2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding. -
decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
-
decoder_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
encoder_last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Sequence of hidden-states at the output of the last layer of the encoder of the model. -
encoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
-
encoder_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The LongT5ForConditionalGeneration forward method, overrides the __call__
special method.
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 pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
>>> model = LongT5ForConditionalGeneration.from_pretrained(
... "Stancld/longt5-tglobal-large-16384-pubmed-3k_steps"
... )
>>> # Let's try a very long input.
>>> inputs = tokenizer(100 * "studies have shown that owning a dog is good for you ", return_tensors="pt")
>>> input_ids = inputs.input_ids
>>> outputs = model.generate(input_ids)
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
abstractthe aim of this article is to provide an overview of the literature on the role of dog
LongT5EncoderModel
class transformers.LongT5EncoderModel
< source >( config: LongT5Config )
Parameters
- config (LongT5Config) — 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.
The bare LONGT5 Model transformer outputting encoderβs raw hidden-states without any specific head on top.
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. Itβs an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]
:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- inputs_embeds (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LongT5Config) and inputs.
-
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model. -
hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The LongT5EncoderModel forward method, overrides the __call__
special method.
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 pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
>>> model = LongT5EncoderModel.from_pretrained("google/long-t5-local-base")
>>> input_ids = tokenizer(
... 100 * "Studies have been shown that owning a dog is good for you ", return_tensors="pt"
... ).input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids)
>>> last_hidden_states = outputs.last_hidden_state
FlaxLongT5Model
class transformers.FlaxLongT5Model
< source >( config: LongT5Config input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
__call__
< source >( input_ids: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: Array = None decoder_attention_mask: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
jnp.ndarray
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
LONGT5 uses the
pad_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).To know more on how to prepare
decoder_input_ids
for pretraining take a look at LONGT5 Training. - decoder_attention_mask (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default. - encoder_outputs (
tuple(tuple(jnp.ndarray)
, optional) — Tuple consists of (last_hidden_state
,optional
: hidden_states,optional
: attentions)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (
tuple(tuple(jnp.ndarray))
of lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.If
past_key_values
are used, the user can optionally input only the lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
.
Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LongT5Config) and inputs.
-
logits (
jnp.ndarray
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
past_key_values (
tuple(tuple(jnp.ndarray))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(jnp.ndarray)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and 2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding. -
decoder_hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
-
decoder_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
encoder_last_hidden_state (
jnp.ndarray
of shape(batch_size, sequence_length, hidden_size)
, optional) β Sequence of hidden-states at the output of the last layer of the encoder of the model. -
encoder_hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
-
encoder_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxLongT5PreTrainedModel
forward method, overrides the __call__
special method.
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 pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5Model
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5Model.from_pretrained("google/long-t5-local-base")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="np"
... ).input_ids
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
encode
< source >( input_ids: Array attention_mask: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
jnp.ndarray
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (<class 'transformers.models.longt5.configuration_longt5.LongT5Config'>
) and inputs.
-
last_hidden_state (
jnp.ndarray
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model. -
hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
decode
< source >( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
- decoder_input_ids (
jnp.ndarray
of shape(batch_size, target_sequence_length)
) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
For training,
decoder_input_ids
should be provided. - encoder_outputs (
tuple(tuple(jnp.ndarray)
) — Tuple consists of (last_hidden_state
, optional:hidden_states
, optional:attentions
)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
, optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - encoder_attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_attention_mask (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default.If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
- past_key_values (
Dict[str, np.ndarray]
, optional, returned byinit_cache
or when passing previouspast_key_values
) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (<class 'transformers.models.longt5.configuration_longt5.LongT5Config'>
) and inputs.
-
last_hidden_state (
jnp.ndarray
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model.If
past_key_values
is used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)
is output. -
past_key_values (
tuple(tuple(jnp.ndarray))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(jnp.ndarray)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and optionally ifconfig.is_encoder_decoder=True
2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True
in the cross-attention blocks) that can be used (seepast_key_values
input) to speed up sequential decoding. -
hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
andconfig.add_cross_attention=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
FlaxLongT5ForConditionalGeneration
class transformers.FlaxLongT5ForConditionalGeneration
< source >( config: LongT5Config input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
__call__
< source >( input_ids: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: Array = None decoder_attention_mask: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
jnp.ndarray
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
LONGT5 uses the
pad_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).To know more on how to prepare
decoder_input_ids
for pretraining take a look at LONGT5 Training. - decoder_attention_mask (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default. - encoder_outputs (
tuple(tuple(jnp.ndarray)
, optional) — Tuple consists of (last_hidden_state
,optional
: hidden_states,optional
: attentions)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (
tuple(tuple(jnp.ndarray))
of lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.If
past_key_values
are used, the user can optionally input only the lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
.
Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LongT5Config) and inputs.
-
logits (
jnp.ndarray
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
past_key_values (
tuple(tuple(jnp.ndarray))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(jnp.ndarray)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and 2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding. -
decoder_hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
-
decoder_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
encoder_last_hidden_state (
jnp.ndarray
of shape(batch_size, sequence_length, hidden_size)
, optional) β Sequence of hidden-states at the output of the last layer of the encoder of the model. -
encoder_hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
-
encoder_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxLongT5PreTrainedModel
forward method, overrides the __call__
special method.
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 pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> ARTICLE_TO_SUMMARIZE = "summarize: My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], return_tensors="np")
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"]).sequences
>>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
encode
< source >( input_ids: Array attention_mask: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
jnp.ndarray
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail.
To know more on how to prepare
input_ids
for pretraining take a look a LONGT5 Training. - attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (<class 'transformers.models.longt5.configuration_longt5.LongT5Config'>
) and inputs.
-
last_hidden_state (
jnp.ndarray
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model. -
hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
decode
< source >( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7f8466573640> = None ) β transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters
- decoder_input_ids (
jnp.ndarray
of shape(batch_size, target_sequence_length)
) — Indices of decoder input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
For training,
decoder_input_ids
should be provided. - encoder_outputs (
tuple(tuple(jnp.ndarray)
) — Tuple consists of (last_hidden_state
, optional:hidden_states
, optional:attentions
)last_hidden_state
of shape(batch_size, sequence_length, hidden_size)
, optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - encoder_attention_mask (
jnp.ndarray
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_attention_mask (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids
. Causal mask will also be used by default.If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
- past_key_values (
Dict[str, np.ndarray]
, optional, returned byinit_cache
or when passing previouspast_key_values
) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (<class 'transformers.models.longt5.configuration_longt5.LongT5Config'>
) and inputs.
-
logits (
jnp.ndarray
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(jnp.ndarray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size)
.Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(jnp.ndarray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjnp.ndarray
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
past_key_values (
tuple(tuple(jnp.ndarray))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple ofjnp.ndarray
tuples of lengthconfig.n_layers
, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant ifconfig.is_decoder = True
.Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding.
Example:
>>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> text = "summarize: My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits