makitanikaze
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e7fbb5b
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Parent(s):
2deb56e
Upload modeling_p5.py
Browse files- modeling_p5.py +456 -0
modeling_p5.py
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1 |
+
from dataclasses import dataclass
|
2 |
+
|
3 |
+
from transformers.models.t5.modeling_t5 import (
|
4 |
+
T5Stack, T5Block, T5LayerNorm, T5LayerSelfAttention, T5LayerFF, T5LayerCrossAttention,
|
5 |
+
T5PreTrainedModel, T5ForConditionalGeneration
|
6 |
+
)
|
7 |
+
|
8 |
+
import torch
|
9 |
+
import torch.nn as nn
|
10 |
+
from torch.nn import CrossEntropyLoss
|
11 |
+
|
12 |
+
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
|
13 |
+
import copy
|
14 |
+
|
15 |
+
from transformers.modeling_outputs import ModelOutput, BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
|
16 |
+
from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
|
17 |
+
from transformers.utils import logging
|
18 |
+
from transformers import BeamScorer, BeamSearchScorer
|
19 |
+
|
20 |
+
logger = logging.get_logger(__name__)
|
21 |
+
|
22 |
+
# The encoder for input token sequence
|
23 |
+
class JointEncoder(T5Stack):
|
24 |
+
def __init__(self, config, embed_tokens=None):
|
25 |
+
super(T5Stack, self).__init__(config)
|
26 |
+
self.config = config
|
27 |
+
|
28 |
+
self.embed_tokens = embed_tokens
|
29 |
+
self.is_decoder = self.config.is_decoder
|
30 |
+
assert self.config.is_decoder is False
|
31 |
+
|
32 |
+
self.block = nn.ModuleList(
|
33 |
+
[T5Block(config, has_relative_attention_bias=(i == 0))
|
34 |
+
for i in range(config.num_layers)]
|
35 |
+
)
|
36 |
+
self.final_layer_norm = T5LayerNorm(
|
37 |
+
config.d_model, eps=config.layer_norm_epsilon)
|
38 |
+
self.dropout = nn.Dropout(config.dropout_rate)
|
39 |
+
|
40 |
+
## Set maximum 512 whole words in a source text
|
41 |
+
self.whole_word_embeddings = nn.Embedding(
|
42 |
+
512, config.d_model ## config.d_model is 768 for base
|
43 |
+
)
|
44 |
+
self.init_weights()
|
45 |
+
self.model_parallel = False
|
46 |
+
self.device_map = None
|
47 |
+
|
48 |
+
def set_input_embeddings(self, new_embeddings):
|
49 |
+
self.embed_tokens = new_embeddings
|
50 |
+
|
51 |
+
def forward(
|
52 |
+
self,
|
53 |
+
input_ids=None,
|
54 |
+
whole_word_ids=None,
|
55 |
+
attention_mask=None,
|
56 |
+
inputs_embeds=None,
|
57 |
+
head_mask=None,
|
58 |
+
past_key_values=None,
|
59 |
+
use_cache=None,
|
60 |
+
output_attentions=None,
|
61 |
+
output_hidden_states=None,
|
62 |
+
return_dict=None,
|
63 |
+
):
|
64 |
+
|
65 |
+
if inputs_embeds is None:
|
66 |
+
assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
|
67 |
+
inputs_embeds = self.embed_tokens(input_ids) ### embedding step - add HERE ###
|
68 |
+
if whole_word_ids is not None:
|
69 |
+
whole_word_embeds = self.whole_word_embeddings(whole_word_ids)
|
70 |
+
assert whole_word_embeds.shape[-1] == inputs_embeds.shape[-1]
|
71 |
+
inputs_embeds = inputs_embeds + whole_word_embeds
|
72 |
+
|
73 |
+
B, L = inputs_embeds.size()[:-1]
|
74 |
+
|
75 |
+
if attention_mask is None:
|
76 |
+
attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
|
77 |
+
|
78 |
+
# ourselves in which case we just need to make it broadcastable to all heads.
|
79 |
+
extended_attention_mask = self.get_extended_attention_mask(
|
80 |
+
attention_mask,
|
81 |
+
(B, L),
|
82 |
+
inputs_embeds.device)
|
83 |
+
|
84 |
+
# initialize past_key_values with `None` if past does not exist
|
85 |
+
if past_key_values is None:
|
86 |
+
past_key_values = [None] * len(self.block)
|
87 |
+
|
88 |
+
# Prepare head mask if needed
|
89 |
+
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
|
90 |
+
present_key_value_states = () if use_cache else None
|
91 |
+
all_hidden_states = () if output_hidden_states else None
|
92 |
+
all_attentions = () if output_attentions else None
|
93 |
+
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
|
94 |
+
|
95 |
+
hidden_states = self.dropout(inputs_embeds)
|
96 |
+
|
97 |
+
if self.config.num_layers > 0:
|
98 |
+
|
99 |
+
assert self.block[0].layer[0].SelfAttention.has_relative_attention_bias
|
100 |
+
|
101 |
+
seq_length = L
|
102 |
+
q_len = seq_length
|
103 |
+
k_len = seq_length
|
104 |
+
|
105 |
+
# [1, n_heads, Q_len, K_len]
|
106 |
+
text_position_bias = self.block[0].layer[0].SelfAttention.compute_bias(
|
107 |
+
L, L)
|
108 |
+
num_heads = text_position_bias.size(1)
|
109 |
+
position_bias = text_position_bias.new_zeros(
|
110 |
+
1, num_heads, seq_length, seq_length)
|
111 |
+
position_bias[:, :, :L, :L] = text_position_bias
|
112 |
+
|
113 |
+
position_bias = position_bias + extended_attention_mask
|
114 |
+
|
115 |
+
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
|
116 |
+
layer_head_mask = head_mask[i]
|
117 |
+
layer_outputs = layer_module(
|
118 |
+
hidden_states,
|
119 |
+
attention_mask=extended_attention_mask,
|
120 |
+
position_bias=position_bias,
|
121 |
+
encoder_hidden_states=None,
|
122 |
+
encoder_attention_mask=None,
|
123 |
+
encoder_decoder_position_bias=None,
|
124 |
+
# head_mask=head_mask[i],
|
125 |
+
layer_head_mask=layer_head_mask,
|
126 |
+
past_key_value=past_key_value,
|
127 |
+
use_cache=use_cache,
|
128 |
+
output_attentions=output_attentions,
|
129 |
+
)
|
130 |
+
|
131 |
+
# layer_outputs is a tuple with:
|
132 |
+
# hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
|
133 |
+
hidden_states, present_key_value_state = layer_outputs[:2]
|
134 |
+
|
135 |
+
# We share the position biases between the layers - the first layer store them
|
136 |
+
# layer_outputs = hidden-states, key-value-states (self-attention weights),
|
137 |
+
# (self-attention position bias), (cross-attention weights), (cross-attention position bias)
|
138 |
+
|
139 |
+
# position_bias = layer_outputs[2]
|
140 |
+
|
141 |
+
# append next layer key value states
|
142 |
+
if use_cache:
|
143 |
+
present_key_value_states = present_key_value_states + \
|
144 |
+
(present_key_value_state,)
|
145 |
+
|
146 |
+
hidden_states = self.final_layer_norm(hidden_states)
|
147 |
+
hidden_states = self.dropout(hidden_states)
|
148 |
+
|
149 |
+
# Add last layer
|
150 |
+
if output_hidden_states:
|
151 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
152 |
+
|
153 |
+
if not return_dict:
|
154 |
+
return tuple(
|
155 |
+
v
|
156 |
+
for v in [
|
157 |
+
hidden_states,
|
158 |
+
present_key_value_states,
|
159 |
+
all_hidden_states,
|
160 |
+
all_attentions,
|
161 |
+
all_cross_attentions,
|
162 |
+
]
|
163 |
+
if v is not None
|
164 |
+
)
|
165 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
166 |
+
last_hidden_state=hidden_states,
|
167 |
+
past_key_values=present_key_value_states,
|
168 |
+
hidden_states=all_hidden_states,
|
169 |
+
attentions=all_attentions,
|
170 |
+
cross_attentions=all_cross_attentions,
|
171 |
+
)
|
172 |
+
|
173 |
+
|
174 |
+
class P5(T5ForConditionalGeneration):
|
175 |
+
_keys_to_ignore_on_load_missing = [
|
176 |
+
r"encoder\.embed_tokens\.weight",
|
177 |
+
r"decoder\.embed_tokens\.weight",
|
178 |
+
r"lm_head\.weight",
|
179 |
+
]
|
180 |
+
_keys_to_ignore_on_load_unexpected = [
|
181 |
+
r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
|
182 |
+
]
|
183 |
+
|
184 |
+
def __init__(self, config):
|
185 |
+
super(T5ForConditionalGeneration, self).__init__(config)
|
186 |
+
|
187 |
+
self.config = config
|
188 |
+
|
189 |
+
self.model_dim = config.d_model
|
190 |
+
|
191 |
+
self.shared = nn.Embedding(config.vocab_size, config.d_model)
|
192 |
+
|
193 |
+
encoder_config = copy.deepcopy(config)
|
194 |
+
encoder_config.is_decoder = False
|
195 |
+
encoder_config.use_cache = False
|
196 |
+
encoder_config.is_encoder_decoder = False
|
197 |
+
|
198 |
+
self.encoder = JointEncoder(encoder_config, self.shared)
|
199 |
+
|
200 |
+
decoder_config = copy.deepcopy(config)
|
201 |
+
decoder_config.is_decoder = True
|
202 |
+
decoder_config.is_encoder_decoder = False
|
203 |
+
|
204 |
+
self.decoder = T5Stack(decoder_config, self.shared)
|
205 |
+
|
206 |
+
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
|
207 |
+
|
208 |
+
self.init_weights()
|
209 |
+
|
210 |
+
self.model_parallel = False
|
211 |
+
self.device_map = None
|
212 |
+
|
213 |
+
def set_input_embeddings(self, new_embeddings):
|
214 |
+
self.shared = new_embeddings
|
215 |
+
self.encoder.set_input_embeddings(new_embeddings)
|
216 |
+
self.decoder.set_input_embeddings(new_embeddings)
|
217 |
+
|
218 |
+
def extend_vocab(self, vocab_size):
|
219 |
+
|
220 |
+
new_shared = nn.Embedding(vocab_size, self.config.d_model)
|
221 |
+
old_weight = self.shared.weight.data.detach().clone()
|
222 |
+
old_vocab_size = old_weight.size(0)
|
223 |
+
new_shared.weight.data[:old_vocab_size, :] = old_weight
|
224 |
+
self.shared = new_shared
|
225 |
+
|
226 |
+
new_lm_head = nn.Linear(self.config.d_model, vocab_size, bias=False)
|
227 |
+
old_weight = self.lm_head.weight.data.detach().clone()
|
228 |
+
old_vocab_size = old_weight.size(0)
|
229 |
+
new_lm_head.weight.data[:old_vocab_size, :] = old_weight
|
230 |
+
self.lm_head = new_lm_head
|
231 |
+
|
232 |
+
self.encoder.embed_tokens = self.shared
|
233 |
+
self.decoder.embed_tokens = self.shared
|
234 |
+
|
235 |
+
self.lm_head.weight = self.shared.weight
|
236 |
+
|
237 |
+
self.config.vocab_size = vocab_size
|
238 |
+
self.encoder.config.vocab_size = vocab_size
|
239 |
+
self.decoder.config.vocab_size = vocab_size
|
240 |
+
|
241 |
+
def forward(
|
242 |
+
self,
|
243 |
+
input_ids=None,
|
244 |
+
whole_word_ids=None,
|
245 |
+
attention_mask=None,
|
246 |
+
encoder_outputs=None,
|
247 |
+
decoder_input_ids=None,
|
248 |
+
decoder_attention_mask=None,
|
249 |
+
past_key_values=None,
|
250 |
+
use_cache=None,
|
251 |
+
labels=None,
|
252 |
+
inputs_embeds=None,
|
253 |
+
decoder_inputs_embeds=None,
|
254 |
+
head_mask=None,
|
255 |
+
output_attentions=None,
|
256 |
+
output_hidden_states=None,
|
257 |
+
return_dict=None,
|
258 |
+
reduce_loss=False,
|
259 |
+
|
260 |
+
return_hidden_state=False,
|
261 |
+
|
262 |
+
**kwargs,
|
263 |
+
):
|
264 |
+
|
265 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
266 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
267 |
+
|
268 |
+
if encoder_outputs is None:
|
269 |
+
encoder_outputs = self.encoder(
|
270 |
+
input_ids=input_ids,
|
271 |
+
whole_word_ids=whole_word_ids,
|
272 |
+
attention_mask=attention_mask,
|
273 |
+
inputs_embeds=inputs_embeds,
|
274 |
+
head_mask=head_mask,
|
275 |
+
output_attentions=output_attentions,
|
276 |
+
output_hidden_states=output_hidden_states,
|
277 |
+
return_dict=return_dict,
|
278 |
+
)
|
279 |
+
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
|
280 |
+
encoder_outputs = BaseModelOutput(
|
281 |
+
last_hidden_state=encoder_outputs[0],
|
282 |
+
hidden_states=encoder_outputs[1] if len(
|
283 |
+
encoder_outputs) > 1 else None,
|
284 |
+
attentions=encoder_outputs[2] if len(
|
285 |
+
encoder_outputs) > 2 else None,
|
286 |
+
)
|
287 |
+
|
288 |
+
hidden_states = encoder_outputs[0]
|
289 |
+
|
290 |
+
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
|
291 |
+
# get decoder inputs from shifting lm labels to the right
|
292 |
+
decoder_input_ids = self._shift_right(labels)
|
293 |
+
|
294 |
+
# If decoding with past key value states, only the last tokens
|
295 |
+
# should be given as an input
|
296 |
+
if past_key_values is not None:
|
297 |
+
assert labels is None, "Decoder should not use cached key value states when training."
|
298 |
+
if decoder_input_ids is not None:
|
299 |
+
decoder_input_ids = decoder_input_ids[:, -1:]
|
300 |
+
if decoder_inputs_embeds is not None:
|
301 |
+
decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
|
302 |
+
|
303 |
+
if attention_mask is None:
|
304 |
+
attention_mask = input_ids.ne(self.config.pad_token_id).to(dtype=hidden_states.dtype, device=hidden_states.device)
|
305 |
+
encoder_attention_mask = attention_mask
|
306 |
+
|
307 |
+
# Decode
|
308 |
+
decoder_outputs = self.decoder(
|
309 |
+
input_ids=decoder_input_ids,
|
310 |
+
attention_mask=decoder_attention_mask,
|
311 |
+
inputs_embeds=decoder_inputs_embeds,
|
312 |
+
past_key_values=past_key_values,
|
313 |
+
|
314 |
+
encoder_hidden_states=hidden_states,
|
315 |
+
encoder_attention_mask=encoder_attention_mask,
|
316 |
+
|
317 |
+
head_mask=head_mask,
|
318 |
+
use_cache=use_cache,
|
319 |
+
output_attentions=output_attentions,
|
320 |
+
output_hidden_states=output_hidden_states,
|
321 |
+
return_dict=return_dict,
|
322 |
+
)
|
323 |
+
|
324 |
+
sequence_output = decoder_outputs[0]
|
325 |
+
|
326 |
+
assert self.config.tie_word_embeddings is True
|
327 |
+
|
328 |
+
if self.config.tie_word_embeddings:
|
329 |
+
sequence_output = sequence_output * (self.model_dim ** -0.5)
|
330 |
+
|
331 |
+
if return_hidden_state:
|
332 |
+
return sequence_output
|
333 |
+
|
334 |
+
lm_logits = self.lm_head(sequence_output)
|
335 |
+
|
336 |
+
loss = None
|
337 |
+
if labels is not None:
|
338 |
+
if reduce_loss:
|
339 |
+
loss_fct = CrossEntropyLoss(ignore_index=-100)
|
340 |
+
else:
|
341 |
+
loss_fct = CrossEntropyLoss(ignore_index=-100, reduction='none')
|
342 |
+
loss = loss_fct(
|
343 |
+
lm_logits.view(-1, lm_logits.size(-1)),
|
344 |
+
labels.view(-1))
|
345 |
+
|
346 |
+
return P5Seq2SeqLMOutput(
|
347 |
+
loss=loss,
|
348 |
+
logits=lm_logits,
|
349 |
+
past_key_values=decoder_outputs.past_key_values,
|
350 |
+
decoder_last_hidden_state=decoder_outputs.last_hidden_state,
|
351 |
+
decoder_hidden_states=decoder_outputs.hidden_states,
|
352 |
+
)
|
353 |
+
|
354 |
+
def prepare_inputs_for_generation(
|
355 |
+
self, input_ids, past=None, attention_mask=None, use_cache=None,
|
356 |
+
encoder_outputs=None,
|
357 |
+
**kwargs):
|
358 |
+
|
359 |
+
if past is not None:
|
360 |
+
input_ids = input_ids[:, -1:]
|
361 |
+
|
362 |
+
output = {
|
363 |
+
"decoder_input_ids": input_ids,
|
364 |
+
"past_key_values": past,
|
365 |
+
"encoder_outputs": encoder_outputs,
|
366 |
+
"attention_mask": attention_mask,
|
367 |
+
"use_cache": use_cache,
|
368 |
+
}
|
369 |
+
|
370 |
+
return output
|
371 |
+
|
372 |
+
@staticmethod
|
373 |
+
def _expand_inputs_for_generation(
|
374 |
+
input_ids: torch.LongTensor,
|
375 |
+
expand_size: int = 1,
|
376 |
+
is_encoder_decoder: bool = False,
|
377 |
+
attention_mask: torch.LongTensor = None,
|
378 |
+
encoder_outputs: ModelOutput = None,
|
379 |
+
**model_kwargs
|
380 |
+
) -> Tuple[torch.LongTensor, Dict[str, Any]]:
|
381 |
+
expanded_return_idx = (
|
382 |
+
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1,
|
383 |
+
expand_size).view(-1).to(input_ids.device)
|
384 |
+
)
|
385 |
+
input_ids = input_ids.index_select(0, expanded_return_idx)
|
386 |
+
|
387 |
+
if "token_type_ids" in model_kwargs:
|
388 |
+
token_type_ids = model_kwargs["token_type_ids"]
|
389 |
+
model_kwargs["token_type_ids"] = token_type_ids.index_select(
|
390 |
+
0, expanded_return_idx)
|
391 |
+
|
392 |
+
if attention_mask is not None:
|
393 |
+
model_kwargs["attention_mask"] = attention_mask.index_select(
|
394 |
+
0, expanded_return_idx)
|
395 |
+
|
396 |
+
if is_encoder_decoder:
|
397 |
+
assert encoder_outputs is not None
|
398 |
+
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
|
399 |
+
0, expanded_return_idx
|
400 |
+
)
|
401 |
+
model_kwargs["encoder_outputs"] = encoder_outputs
|
402 |
+
|
403 |
+
return input_ids, model_kwargs
|
404 |
+
|
405 |
+
|
406 |
+
@dataclass
|
407 |
+
class P5Seq2SeqLMOutput(ModelOutput):
|
408 |
+
"""
|
409 |
+
Base class for sequence-to-sequence language models outputs.
|
410 |
+
|
411 |
+
Args:
|
412 |
+
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
|
413 |
+
Languaged modeling loss.
|
414 |
+
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
|
415 |
+
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
416 |
+
past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
|
417 |
+
List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape
|
418 |
+
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
|
419 |
+
|
420 |
+
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
|
421 |
+
used (see ``past_key_values`` input) to speed up sequential decoding.
|
422 |
+
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
|
423 |
+
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
|
424 |
+
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
425 |
+
|
426 |
+
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
|
427 |
+
decoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
|
428 |
+
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
|
429 |
+
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
|
430 |
+
|
431 |
+
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
|
432 |
+
self-attention heads.
|
433 |
+
encoder_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
434 |
+
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
435 |
+
encoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
|
436 |
+
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
|
437 |
+
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
|
438 |
+
|
439 |
+
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
|
440 |
+
encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
|
441 |
+
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
|
442 |
+
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
|
443 |
+
|
444 |
+
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
|
445 |
+
self-attention heads.
|
446 |
+
"""
|
447 |
+
|
448 |
+
loss: Optional[torch.FloatTensor] = None
|
449 |
+
logits: torch.FloatTensor = None
|
450 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None
|
451 |
+
decoder_last_hidden_state: Optional[Tuple[torch.FloatTensor]] = None
|
452 |
+
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
453 |
+
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
|
454 |
+
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
455 |
+
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
456 |
+
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
|