472 lines
20 KiB
Python
472 lines
20 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import copy
|
|
import warnings
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import CrossEntropyLoss
|
|
from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
|
|
|
|
from modelscope.metainfo import Models
|
|
from modelscope.models.builder import MODELS
|
|
from modelscope.outputs import (
|
|
AttentionBackboneModelOutput,
|
|
Seq2SeqLMOutput,
|
|
TokenGeneratorOutput,
|
|
)
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.logger import get_logger
|
|
from backbone import T5PreTrainedModel, T5Stack
|
|
from configuration import T5Config
|
|
|
|
logger = get_logger()
|
|
|
|
# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
|
__HEAD_MASK_WARNING_MSG = """
|
|
The input argument `head_mask` was split into two arguments `head_mask` and
|
|
`decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`,
|
|
but this feature is deprecated and will be removed in future versions. If you do
|
|
not want to use any `decoder_head_mask` now, please set `decoder_head_mask =
|
|
torch.ones(num_layers, num_heads)`.
|
|
"""
|
|
|
|
|
|
class T5ForConditionalGeneration(T5PreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [
|
|
r"encoder\.embed_tokens\.weight",
|
|
r"decoder\.embed_tokens\.weight",
|
|
r"lm_head\.weight",
|
|
]
|
|
_keys_to_ignore_on_load_unexpected = [
|
|
r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
|
|
]
|
|
|
|
def __init__(self, config: T5Config, device_map=None, **kwargs):
|
|
super().__init__(config)
|
|
self.model_dim = config.d_model
|
|
|
|
self.shared = nn.Embedding(config.vocab_size, config.d_model)
|
|
|
|
encoder_config = copy.deepcopy(config)
|
|
encoder_config.is_decoder = False
|
|
encoder_config.use_cache = False
|
|
encoder_config.is_encoder_decoder = False
|
|
self.encoder = T5Stack(encoder_config, self.shared)
|
|
|
|
decoder_config = copy.deepcopy(config)
|
|
decoder_config.is_decoder = True
|
|
decoder_config.is_encoder_decoder = False
|
|
decoder_config.num_layers = config.num_decoder_layers
|
|
self.decoder = T5Stack(decoder_config, self.shared)
|
|
|
|
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
if device_map == "auto":
|
|
self.parallelize()
|
|
|
|
def parallelize(self, device_map=None):
|
|
self.device_map = (
|
|
get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
|
|
if device_map is None
|
|
else device_map
|
|
)
|
|
assert_device_map(self.device_map, len(self.encoder.block))
|
|
self.encoder.parallelize(self.device_map)
|
|
self.decoder.parallelize(self.device_map)
|
|
self.lm_head = self.lm_head.to(self.decoder.first_device)
|
|
self.model_parallel = True
|
|
|
|
def deparallelize(self):
|
|
self.encoder.deparallelize()
|
|
self.decoder.deparallelize()
|
|
self.encoder = self.encoder.to("cpu")
|
|
self.decoder = self.decoder.to("cpu")
|
|
self.lm_head = self.lm_head.to("cpu")
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
torch.cuda.empty_cache()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.shared
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.shared = new_embeddings
|
|
self.encoder.set_input_embeddings(new_embeddings)
|
|
self.decoder.set_input_embeddings(new_embeddings)
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
decoder_head_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. T5 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 [`T5Tokenizer`]. See
|
|
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
|
|
for detail.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
|
|
To know more on how to prepare `input_ids` for pretraining take a
|
|
look a [T5 Training](./t5#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**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
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 [`T5Tokenizer`]. See
|
|
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
|
|
for details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
|
|
T5 uses the `pad_token_id` as the starting token for
|
|
`decoder_input_ids` generation. If `past_key_values` is used,
|
|
optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
To know more on how to prepare `decoder_input_ids` for pretraining
|
|
take a look at [T5 Training](./t5#training).
|
|
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Default behavior: generate a tensor that ignores pad tokens in
|
|
`decoder_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 length
|
|
`config.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 last `decoder_input_ids` (those that don't have their past key
|
|
value states given to this model) of shape `(batch_size, 1)` instead
|
|
of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to
|
|
directly pass an embedded representation. This is useful if you want
|
|
more control over how to convert `input_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 passing `decoder_input_ids` you can choose to
|
|
directly pass an embedded representation. If `past_key_values` is
|
|
used, optionally only the last `decoder_inputs_embeds` have to be
|
|
input (see `past_key_values`). This is useful if you want more
|
|
control over how to convert `decoder_input_ids` indices into
|
|
associated vectors than the model's internal embedding lookup
|
|
matrix.
|
|
|
|
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset,
|
|
`decoder_inputs_embeds` takes the value of `inputs_embeds`.
|
|
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned
|
|
and can be used to speed up decoding (see `past_key_values`).
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention
|
|
layers. See `attentions` under returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See
|
|
`hidden_states` under returned tensors for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain
|
|
tuple.
|
|
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:
|
|
|
|
Examples:
|
|
|
|
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
|
|
|
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
|
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
|
|
|
>>> # training
|
|
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
|
|
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
|
|
>>> outputs = model(input_ids=input_ids, labels=labels)
|
|
>>> loss = outputs.loss
|
|
>>> logits = outputs.logits
|
|
|
|
>>> # inference
|
|
>>> input_ids = tokenizer(
|
|
... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
|
|
>>> ).input_ids # Batch size 1
|
|
>>> outputs = model.generate(input_ids)
|
|
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
>>> # studies have shown that owning a dog is good for you.
|
|
"""
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
|
|
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
|
if head_mask is not None and decoder_head_mask is None:
|
|
if self.config.num_layers == self.config.num_decoder_layers:
|
|
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
|
|
decoder_head_mask = head_mask
|
|
|
|
# Encode if needed (training, first prediction pass)
|
|
if encoder_outputs is None:
|
|
# Convert encoder inputs in embeddings if needed
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
elif return_dict and not isinstance(
|
|
encoder_outputs, AttentionBackboneModelOutput
|
|
):
|
|
encoder_outputs = AttentionBackboneModelOutput(
|
|
last_hidden_state=encoder_outputs[0],
|
|
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
|
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
|
)
|
|
|
|
hidden_states = encoder_outputs[0]
|
|
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(self.decoder.first_device)
|
|
|
|
if (
|
|
labels is not None
|
|
and decoder_input_ids is None
|
|
and decoder_inputs_embeds is None
|
|
):
|
|
# get decoder inputs from shifting lm labels to the right
|
|
decoder_input_ids = self._shift_right(labels)
|
|
|
|
# Set device for model parallelism
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(self.decoder.first_device)
|
|
hidden_states = hidden_states.to(self.decoder.first_device)
|
|
if decoder_input_ids is not None:
|
|
decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.to(self.decoder.first_device)
|
|
if decoder_attention_mask is not None:
|
|
decoder_attention_mask = decoder_attention_mask.to(
|
|
self.decoder.first_device
|
|
)
|
|
|
|
# Decode
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
inputs_embeds=decoder_inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
encoder_hidden_states=hidden_states,
|
|
encoder_attention_mask=attention_mask,
|
|
head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = decoder_outputs[0]
|
|
|
|
# Set device for model parallelism
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(self.encoder.first_device)
|
|
self.lm_head = self.lm_head.to(self.encoder.first_device)
|
|
sequence_output = sequence_output.to(self.lm_head.weight.device)
|
|
|
|
if self.config.tie_word_embeddings:
|
|
# Rescale output before projecting on vocab See
|
|
# https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
|
|
sequence_output = sequence_output * (self.model_dim ** -0.5)
|
|
|
|
lm_logits = self.lm_head(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-100)
|
|
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
|
|
# TODO(thom): Add z_loss
|
|
# https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past=None,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
decoder_head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
use_cache=None,
|
|
encoder_outputs=None,
|
|
**kwargs,
|
|
):
|
|
|
|
# cut decoder_input_ids if past is used
|
|
if past is not None:
|
|
input_ids = input_ids[:, -1:]
|
|
|
|
return {
|
|
"decoder_input_ids": input_ids,
|
|
"past_key_values": past,
|
|
"encoder_outputs": encoder_outputs,
|
|
"attention_mask": attention_mask,
|
|
"head_mask": head_mask,
|
|
"decoder_head_mask": decoder_head_mask,
|
|
"cross_attn_head_mask": cross_attn_head_mask,
|
|
"use_cache": use_cache,
|
|
}
|
|
|
|
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
|
|
return self._shift_right(labels)
|
|
|
|
def generate(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
output = super().generate(*args, **kwargs)
|
|
return TokenGeneratorOutput(
|
|
sequences=output if isinstance(output, torch.Tensor) else output[0]
|
|
)
|
|
|
|
def _reorder_cache(self, past, beam_idx):
|
|
# if decoder past is not included in output
|
|
# speedy decoding is disabled and no need to reorder
|
|
if past is None:
|
|
logger.warning(
|
|
"You might want to consider setting `use_cache=True` to speed up decoding"
|
|
)
|
|
return past
|
|
|
|
reordered_decoder_past = ()
|
|
for layer_past_states in past:
|
|
# get the correct batch idx from layer past batch dim
|
|
# batch dim of `past` is at 2nd position
|
|
reordered_layer_past_states = ()
|
|
for layer_past_state in layer_past_states:
|
|
# need to set correct `past` for each of the four key / value states
|
|
reordered_layer_past_states = reordered_layer_past_states + (
|
|
layer_past_state.index_select(
|
|
0, beam_idx.to(layer_past_state.device)
|
|
),
|
|
)
|
|
|
|
assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
|
|
assert len(reordered_layer_past_states) == len(layer_past_states)
|
|
|
|
reordered_decoder_past = reordered_decoder_past + (
|
|
reordered_layer_past_states,
|
|
)
|
|
return reordered_decoder_past
|