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Browse files- lingconv_t5.py +453 -0
lingconv_t5.py
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1 |
+
import warnings
|
2 |
+
import copy
|
3 |
+
from typing import Optional, Tuple, Union
|
4 |
+
|
5 |
+
import torch
|
6 |
+
from torch.nn import CrossEntropyLoss
|
7 |
+
|
8 |
+
from transformers.modeling_outputs import (
|
9 |
+
BaseModelOutput,
|
10 |
+
Seq2SeqLMOutput,
|
11 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
12 |
+
)
|
13 |
+
from transformers.models.t5.modeling_t5 import T5Stack, T5ForConditionalGeneration, __HEAD_MASK_WARNING_MSG
|
14 |
+
from transformers import T5Config
|
15 |
+
|
16 |
+
from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache
|
17 |
+
from transformers.utils import (
|
18 |
+
is_torchdynamo_compiling,
|
19 |
+
)
|
20 |
+
import logging
|
21 |
+
|
22 |
+
logger = logging.getLogger(__name__)
|
23 |
+
|
24 |
+
class LingConvT5Stack(T5Stack):
|
25 |
+
def __init__(self, config: T5Config, embed_tokens=None):
|
26 |
+
super().__init__(config, embed_tokens)
|
27 |
+
|
28 |
+
# Add new attributes for ling injection
|
29 |
+
self.ling_injection_layer = getattr(config, 'ling_injection_layer', -1)
|
30 |
+
self.ling_injection_type = getattr(config, 'ling_injection_type', 'none') # 'none', 'first', 'all'
|
31 |
+
|
32 |
+
def forward(
|
33 |
+
self,
|
34 |
+
input_ids=None,
|
35 |
+
attention_mask=None,
|
36 |
+
encoder_hidden_states=None,
|
37 |
+
encoder_attention_mask=None,
|
38 |
+
inputs_embeds=None,
|
39 |
+
head_mask=None,
|
40 |
+
cross_attn_head_mask=None,
|
41 |
+
past_key_values=None,
|
42 |
+
use_cache=None,
|
43 |
+
output_attentions=None,
|
44 |
+
output_hidden_states=None,
|
45 |
+
return_dict=None,
|
46 |
+
cache_position=None,
|
47 |
+
ling_embed=None,
|
48 |
+
):
|
49 |
+
# Model parallel
|
50 |
+
if self.model_parallel:
|
51 |
+
torch.cuda.set_device(self.first_device)
|
52 |
+
self.embed_tokens = self.embed_tokens.to(self.first_device)
|
53 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
54 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
55 |
+
output_hidden_states = (
|
56 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
57 |
+
)
|
58 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
59 |
+
|
60 |
+
if input_ids is not None and inputs_embeds is not None:
|
61 |
+
err_msg_prefix = "decoder_" if self.is_decoder else ""
|
62 |
+
raise ValueError(
|
63 |
+
f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
|
64 |
+
)
|
65 |
+
elif input_ids is not None:
|
66 |
+
input_shape = input_ids.size()
|
67 |
+
input_ids = input_ids.view(-1, input_shape[-1])
|
68 |
+
elif inputs_embeds is not None:
|
69 |
+
input_shape = inputs_embeds.size()[:-1]
|
70 |
+
else:
|
71 |
+
err_msg_prefix = "decoder_" if self.is_decoder else ""
|
72 |
+
raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
|
73 |
+
|
74 |
+
if self.gradient_checkpointing and self.training:
|
75 |
+
if use_cache:
|
76 |
+
logger.warning_once(
|
77 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
78 |
+
)
|
79 |
+
use_cache = False
|
80 |
+
|
81 |
+
if inputs_embeds is None:
|
82 |
+
if self.embed_tokens is None:
|
83 |
+
raise ValueError("You have to initialize the model with valid token embeddings")
|
84 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
85 |
+
|
86 |
+
batch_size, seq_length = input_shape
|
87 |
+
|
88 |
+
if use_cache is True:
|
89 |
+
if not self.is_decoder:
|
90 |
+
raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
|
91 |
+
|
92 |
+
# initialize past_key_values
|
93 |
+
return_legacy_cache = False
|
94 |
+
return_self_attention_cache = False
|
95 |
+
if self.is_decoder and (use_cache or past_key_values is not None):
|
96 |
+
if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
|
97 |
+
return_self_attention_cache = True
|
98 |
+
past_key_values = EncoderDecoderCache(past_key_values, DynamicCache())
|
99 |
+
elif not isinstance(past_key_values, EncoderDecoderCache):
|
100 |
+
return_legacy_cache = True
|
101 |
+
logger.warning_once(
|
102 |
+
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. "
|
103 |
+
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
|
104 |
+
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
|
105 |
+
)
|
106 |
+
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
|
107 |
+
elif past_key_values is None:
|
108 |
+
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
|
109 |
+
elif not self.is_decoder:
|
110 |
+
# do not pass cache object down the line for encoder stack
|
111 |
+
# it messes indexing later in decoder-stack because cache object is modified in-place
|
112 |
+
past_key_values = None
|
113 |
+
|
114 |
+
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
115 |
+
if cache_position is None:
|
116 |
+
cache_position = torch.arange(
|
117 |
+
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
|
118 |
+
)
|
119 |
+
|
120 |
+
if attention_mask is None and not is_torchdynamo_compiling():
|
121 |
+
# required mask seq length can be calculated via length of past cache
|
122 |
+
mask_seq_length = past_key_values_length + seq_length
|
123 |
+
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
124 |
+
|
125 |
+
if self.config.is_decoder:
|
126 |
+
causal_mask = self._update_causal_mask(
|
127 |
+
attention_mask,
|
128 |
+
inputs_embeds,
|
129 |
+
cache_position,
|
130 |
+
past_key_values.self_attention_cache if past_key_values is not None else None,
|
131 |
+
output_attentions,
|
132 |
+
)
|
133 |
+
elif attention_mask is not None:
|
134 |
+
causal_mask = attention_mask[:, None, None, :]
|
135 |
+
causal_mask = causal_mask.to(dtype=inputs_embeds.dtype)
|
136 |
+
causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min
|
137 |
+
else:
|
138 |
+
causal_mask = None
|
139 |
+
|
140 |
+
# If a 2D or 3D attention mask is provided for the cross-attention
|
141 |
+
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
142 |
+
if self.is_decoder and encoder_hidden_states is not None:
|
143 |
+
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
144 |
+
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
145 |
+
if encoder_attention_mask is None:
|
146 |
+
encoder_attention_mask = torch.ones(
|
147 |
+
encoder_hidden_shape, device=inputs_embeds.device, dtype=torch.long
|
148 |
+
)
|
149 |
+
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
150 |
+
else:
|
151 |
+
encoder_extended_attention_mask = None
|
152 |
+
|
153 |
+
# Prepare head mask if needed
|
154 |
+
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
|
155 |
+
cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
|
156 |
+
all_hidden_states = () if output_hidden_states else None
|
157 |
+
all_attentions = () if output_attentions else None
|
158 |
+
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
|
159 |
+
position_bias = None
|
160 |
+
encoder_decoder_position_bias = None
|
161 |
+
|
162 |
+
hidden_states = self.dropout(inputs_embeds)
|
163 |
+
|
164 |
+
for i, layer_module in enumerate(self.block):
|
165 |
+
layer_head_mask = head_mask[i]
|
166 |
+
cross_attn_layer_head_mask = cross_attn_head_mask[i]
|
167 |
+
# Model parallel
|
168 |
+
if self.model_parallel:
|
169 |
+
torch.cuda.set_device(hidden_states.device)
|
170 |
+
# Ensure that attention_mask is always on the same device as hidden_states
|
171 |
+
if causal_mask is not None:
|
172 |
+
causal_mask = causal_mask.to(hidden_states.device)
|
173 |
+
if position_bias is not None:
|
174 |
+
position_bias = position_bias.to(hidden_states.device)
|
175 |
+
if encoder_hidden_states is not None:
|
176 |
+
encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
|
177 |
+
if encoder_extended_attention_mask is not None:
|
178 |
+
encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
|
179 |
+
if encoder_decoder_position_bias is not None:
|
180 |
+
encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
|
181 |
+
if layer_head_mask is not None:
|
182 |
+
layer_head_mask = layer_head_mask.to(hidden_states.device)
|
183 |
+
if cross_attn_layer_head_mask is not None:
|
184 |
+
cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
|
185 |
+
if output_hidden_states:
|
186 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
187 |
+
|
188 |
+
if self.gradient_checkpointing and self.training:
|
189 |
+
layer_outputs = self._gradient_checkpointing_func(
|
190 |
+
layer_module.forward,
|
191 |
+
hidden_states,
|
192 |
+
causal_mask,
|
193 |
+
position_bias,
|
194 |
+
encoder_hidden_states,
|
195 |
+
encoder_extended_attention_mask,
|
196 |
+
encoder_decoder_position_bias,
|
197 |
+
layer_head_mask,
|
198 |
+
cross_attn_layer_head_mask,
|
199 |
+
None, # past_key_value is always None with gradient checkpointing
|
200 |
+
use_cache,
|
201 |
+
output_attentions,
|
202 |
+
return_dict,
|
203 |
+
cache_position,
|
204 |
+
)
|
205 |
+
else:
|
206 |
+
layer_outputs = layer_module(
|
207 |
+
hidden_states,
|
208 |
+
attention_mask=causal_mask,
|
209 |
+
position_bias=position_bias,
|
210 |
+
encoder_hidden_states=encoder_hidden_states,
|
211 |
+
encoder_attention_mask=encoder_extended_attention_mask,
|
212 |
+
encoder_decoder_position_bias=encoder_decoder_position_bias,
|
213 |
+
layer_head_mask=layer_head_mask,
|
214 |
+
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
|
215 |
+
past_key_value=past_key_values,
|
216 |
+
use_cache=use_cache,
|
217 |
+
output_attentions=output_attentions,
|
218 |
+
return_dict=return_dict,
|
219 |
+
cache_position=cache_position,
|
220 |
+
)
|
221 |
+
|
222 |
+
# layer_outputs is a tuple with:
|
223 |
+
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
|
224 |
+
if use_cache is False:
|
225 |
+
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
|
226 |
+
|
227 |
+
hidden_states, next_decoder_cache = layer_outputs[:2]
|
228 |
+
|
229 |
+
# Add linguistic embedding injection after specified layer
|
230 |
+
if (self.is_decoder and
|
231 |
+
self.ling_injection_layer == i and
|
232 |
+
ling_embed is not None and
|
233 |
+
self.ling_injection_type != 'none'):
|
234 |
+
|
235 |
+
hidden_states = hidden_states + ling_embed
|
236 |
+
|
237 |
+
# We share the position biases between the layers - the first layer store them
|
238 |
+
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
|
239 |
+
# (cross-attention position bias), (cross-attention weights)
|
240 |
+
position_bias = layer_outputs[2]
|
241 |
+
if self.is_decoder and encoder_hidden_states is not None:
|
242 |
+
encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
|
243 |
+
|
244 |
+
if output_attentions:
|
245 |
+
all_attentions = all_attentions + (layer_outputs[3],)
|
246 |
+
if self.is_decoder:
|
247 |
+
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
|
248 |
+
|
249 |
+
# Model Parallel: If it's the last layer for that device, put things on the next device
|
250 |
+
if self.model_parallel:
|
251 |
+
for k, v in self.device_map.items():
|
252 |
+
if i == v[-1] and "cuda:" + str(k) != self.last_device:
|
253 |
+
hidden_states = hidden_states.to("cuda:" + str(k + 1))
|
254 |
+
|
255 |
+
hidden_states = self.final_layer_norm(hidden_states)
|
256 |
+
hidden_states = self.dropout(hidden_states)
|
257 |
+
|
258 |
+
# Add last layer
|
259 |
+
if output_hidden_states:
|
260 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
261 |
+
|
262 |
+
next_cache = next_decoder_cache if use_cache else None
|
263 |
+
if return_self_attention_cache:
|
264 |
+
next_cache = past_key_values.self_attention_cache
|
265 |
+
if return_legacy_cache:
|
266 |
+
next_cache = past_key_values.to_legacy_cache()
|
267 |
+
|
268 |
+
if not return_dict:
|
269 |
+
return tuple(
|
270 |
+
v
|
271 |
+
for v in [
|
272 |
+
hidden_states,
|
273 |
+
next_cache,
|
274 |
+
all_hidden_states,
|
275 |
+
all_attentions,
|
276 |
+
all_cross_attentions,
|
277 |
+
]
|
278 |
+
if v is not None
|
279 |
+
)
|
280 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
281 |
+
last_hidden_state=hidden_states,
|
282 |
+
past_key_values=next_cache,
|
283 |
+
hidden_states=all_hidden_states,
|
284 |
+
attentions=all_attentions,
|
285 |
+
cross_attentions=all_cross_attentions,
|
286 |
+
)
|
287 |
+
|
288 |
+
class LingConvT5ForConditionalGeneration(T5ForConditionalGeneration):
|
289 |
+
def __init__(self, config):
|
290 |
+
super().__init__(config)
|
291 |
+
# Replace default decoder with our custom decoder
|
292 |
+
decoder_config = copy.deepcopy(config)
|
293 |
+
decoder_config.is_decoder = True
|
294 |
+
decoder_config.is_encoder_decoder = False
|
295 |
+
decoder_config.num_layers = config.num_decoder_layers
|
296 |
+
self.decoder = LingConvT5Stack(decoder_config, embed_tokens=self.shared)
|
297 |
+
|
298 |
+
def forward(
|
299 |
+
self,
|
300 |
+
input_ids: Optional[torch.LongTensor] = None,
|
301 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
302 |
+
decoder_input_ids: Optional[torch.LongTensor] = None,
|
303 |
+
decoder_attention_mask: Optional[torch.BoolTensor] = None,
|
304 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
305 |
+
decoder_head_mask: Optional[torch.FloatTensor] = None,
|
306 |
+
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
307 |
+
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
308 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
309 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
310 |
+
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
311 |
+
labels: Optional[torch.LongTensor] = None,
|
312 |
+
use_cache: Optional[bool] = None,
|
313 |
+
output_attentions: Optional[bool] = None,
|
314 |
+
output_hidden_states: Optional[bool] = None,
|
315 |
+
return_dict: Optional[bool] = None,
|
316 |
+
cache_position: Optional[torch.LongTensor] = None,
|
317 |
+
ling_embed: Optional[torch.FloatTensor] = None,
|
318 |
+
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
|
319 |
+
r"""
|
320 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
321 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
|
322 |
+
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
|
323 |
+
labels in `[0, ..., config.vocab_size]`
|
324 |
+
|
325 |
+
Returns:
|
326 |
+
|
327 |
+
Examples:
|
328 |
+
|
329 |
+
```python
|
330 |
+
>>> from transformers import AutoTokenizer, T5ForConditionalGeneration
|
331 |
+
|
332 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
|
333 |
+
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
334 |
+
|
335 |
+
>>> # training
|
336 |
+
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
|
337 |
+
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
|
338 |
+
>>> outputs = model(input_ids=input_ids, labels=labels)
|
339 |
+
>>> loss = outputs.loss
|
340 |
+
>>> logits = outputs.logits
|
341 |
+
|
342 |
+
>>> # inference
|
343 |
+
>>> input_ids = tokenizer(
|
344 |
+
... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
|
345 |
+
... ).input_ids # Batch size 1
|
346 |
+
>>> outputs = model.generate(input_ids)
|
347 |
+
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
348 |
+
>>> # studies have shown that owning a dog is good for you.
|
349 |
+
```"""
|
350 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
351 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
352 |
+
|
353 |
+
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
354 |
+
if head_mask is not None and decoder_head_mask is None:
|
355 |
+
if self.config.num_layers == self.config.num_decoder_layers:
|
356 |
+
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
|
357 |
+
decoder_head_mask = head_mask
|
358 |
+
|
359 |
+
# Encode if needed (training, first prediction pass)
|
360 |
+
if encoder_outputs is None:
|
361 |
+
# Convert encoder inputs in embeddings if needed
|
362 |
+
encoder_outputs = self.encoder(
|
363 |
+
input_ids=input_ids,
|
364 |
+
attention_mask=attention_mask,
|
365 |
+
inputs_embeds=inputs_embeds,
|
366 |
+
head_mask=head_mask,
|
367 |
+
output_attentions=output_attentions,
|
368 |
+
output_hidden_states=output_hidden_states,
|
369 |
+
return_dict=return_dict,
|
370 |
+
)
|
371 |
+
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
|
372 |
+
encoder_outputs = BaseModelOutput(
|
373 |
+
last_hidden_state=encoder_outputs[0],
|
374 |
+
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
375 |
+
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
376 |
+
)
|
377 |
+
|
378 |
+
hidden_states = encoder_outputs[0]
|
379 |
+
|
380 |
+
if self.model_parallel:
|
381 |
+
torch.cuda.set_device(self.decoder.first_device)
|
382 |
+
|
383 |
+
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
|
384 |
+
# get decoder inputs from shifting lm labels to the right
|
385 |
+
decoder_input_ids = self._shift_right(labels)
|
386 |
+
|
387 |
+
# Set device for model parallelism
|
388 |
+
if self.model_parallel:
|
389 |
+
torch.cuda.set_device(self.decoder.first_device)
|
390 |
+
hidden_states = hidden_states.to(self.decoder.first_device)
|
391 |
+
if decoder_input_ids is not None:
|
392 |
+
decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
|
393 |
+
if attention_mask is not None:
|
394 |
+
attention_mask = attention_mask.to(self.decoder.first_device)
|
395 |
+
if decoder_attention_mask is not None:
|
396 |
+
decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
|
397 |
+
|
398 |
+
# Decode
|
399 |
+
decoder_outputs = self.decoder(
|
400 |
+
input_ids=decoder_input_ids,
|
401 |
+
attention_mask=decoder_attention_mask,
|
402 |
+
inputs_embeds=decoder_inputs_embeds,
|
403 |
+
past_key_values=past_key_values,
|
404 |
+
encoder_hidden_states=hidden_states,
|
405 |
+
encoder_attention_mask=attention_mask,
|
406 |
+
head_mask=decoder_head_mask,
|
407 |
+
cross_attn_head_mask=cross_attn_head_mask,
|
408 |
+
use_cache=use_cache,
|
409 |
+
output_attentions=output_attentions,
|
410 |
+
output_hidden_states=output_hidden_states,
|
411 |
+
return_dict=return_dict,
|
412 |
+
cache_position=cache_position,
|
413 |
+
ling_embed=ling_embed,
|
414 |
+
)
|
415 |
+
|
416 |
+
sequence_output = decoder_outputs[0]
|
417 |
+
|
418 |
+
# Set device for model parallelism
|
419 |
+
if self.model_parallel:
|
420 |
+
torch.cuda.set_device(self.encoder.first_device)
|
421 |
+
self.lm_head = self.lm_head.to(self.encoder.first_device)
|
422 |
+
sequence_output = sequence_output.to(self.lm_head.weight.device)
|
423 |
+
|
424 |
+
if self.config.tie_word_embeddings:
|
425 |
+
# Rescale output before projecting on vocab
|
426 |
+
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
|
427 |
+
sequence_output = sequence_output * (self.model_dim**-0.5)
|
428 |
+
|
429 |
+
lm_logits = self.lm_head(sequence_output)
|
430 |
+
|
431 |
+
loss = None
|
432 |
+
if labels is not None:
|
433 |
+
loss_fct = CrossEntropyLoss(ignore_index=-100)
|
434 |
+
# move labels to correct device to enable PP
|
435 |
+
labels = labels.to(lm_logits.device)
|
436 |
+
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
|
437 |
+
# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
|
438 |
+
|
439 |
+
if not return_dict:
|
440 |
+
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
|
441 |
+
return ((loss,) + output) if loss is not None else output
|
442 |
+
|
443 |
+
return Seq2SeqLMOutput(
|
444 |
+
loss=loss,
|
445 |
+
logits=lm_logits,
|
446 |
+
past_key_values=decoder_outputs.past_key_values,
|
447 |
+
decoder_hidden_states=decoder_outputs.hidden_states,
|
448 |
+
decoder_attentions=decoder_outputs.attentions,
|
449 |
+
cross_attentions=decoder_outputs.cross_attentions,
|
450 |
+
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
451 |
+
encoder_hidden_states=encoder_outputs.hidden_states,
|
452 |
+
encoder_attentions=encoder_outputs.attentions,
|
453 |
+
)
|