#from transformers import RobertaModel, RobertaConfig, RobertaForMaskedLM, RobertaLMHead
#from linformer import LinformerTransformerEncoder, LinformerTransformerEncoderLayer, LinformerTransformerEncoderFS, LinformerTransformerEncoderLayerFS
#import linformer
from .linformer import LinformerTransformerEncoderLayer
from .flaubert2_configuration import Flaubert2Config
from transformers.models.roberta.modeling_roberta import RobertaEncoder, RobertaConfig, RobertaModel, RobertaLMHead, RobertaForMaskedLM, RobertaEmbeddings, RobertaForTokenClassification, RobertaForSequenceClassification
import torch.nn as nn
import math
import torch.nn.functional as F
from torch.nn import LayerNorm
import torch
from typing import List, Optional, Tuple, Union
from fairseq.models.roberta import (
RobertaModel as RobertModel,
RobertaEncoder as RobertaEncoderFS
)
from transformers.modeling_outputs import (
MaskedLMOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
)
class Flaubert2ModelForSequenceClassification(RobertaForSequenceClassification):
config_class = Flaubert2Config
def __init__(self, config, **kwargs):
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
#self.encoder = Flaubert2Model(config, add_pooling_layer=False)
self.roberta = Flaubert2Model(config, add_pooling_layer=False)
#self.encoder = LinformerTransformerEncoder(config)
#self.encoder = LinformerTransformerEncoder(config)
self.sbo_head = self.build_sbo_head(config)
def build_sbo_head(self, config):
return SBOHead(
config,
embedding_weights=(
self.roberta.embeddings.word_embeddings.weight
if not config.untie_weights_roberta
else None
)
)
class Flaubert2ModelForTokenClassification(RobertaForTokenClassification):
config_class = Flaubert2Config
def __init__(self, config, **kwargs):
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
#self.encoder = Flaubert2Model(config, add_pooling_layer=False)
self.roberta = Flaubert2Model(config, add_pooling_layer=False)
#self.encoder = LinformerTransformerEncoder(config)
#self.encoder = LinformerTransformerEncoder(config)
self.sbo_head = self.build_sbo_head(config)
def build_sbo_head(self, config):
return SBOHead(
config,
embedding_weights=(
self.roberta.embeddings.word_embeddings.weight
if not config.untie_weights_roberta
else None
)
)
class Flaubert2ModelForMaskedLM(RobertaForMaskedLM):
config_class = Flaubert2Config
def __init__(self, config, **kwargs):
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
#self.encoder = Flaubert2Model(config, add_pooling_layer=False)
self.roberta = Flaubert2Model(config, add_pooling_layer=False)
#self.encoder = LinformerTransformerEncoder(config)
#self.encoder = LinformerTransformerEncoder(config)
self.sbo_head = self.build_sbo_head(config)
def build_sbo_head(self, config):
return SBOHead(
config,
embedding_weights=(
self.roberta.embeddings.word_embeddings.weight
if not config.untie_weights_roberta
else None
)
)
class Flaubert2ModelForMaskedLMFS(RobertaForMaskedLM):
def __init__(self, config, dictionary, **kwargs):
config_class = Flaubert2Config
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
#self.encoder = Flaubert2Model(config, add_pooling_layer=False)
#self.roberta = Flaubert2ModelFS(config, dictionary, add_pooling_layer=False)
self.roberta =FlaubertEncoder(config, dictionary)
#self.encoder =
#self.encoder = LinformerTransformerEncoder(config)
#self.sbo_head = self.build_sbo_head(config)
def build_sbo_head(self, config):
return SBOHead(
config,
embedding_weights=(
self.roberta.embeddings.word_embeddings.weight
if not config.untie_weights_roberta
else None
)
)
class Flaubert2Embeddings(RobertaEmbeddings):
def __init__(self, config, **kwargs):
config_class = Flaubert2Config
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
#if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
#else:
embeddings += position_embeddings
#embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class Flaubert2Encoder(RobertaEncoder):
def __init__(self, args):
compress_layer = None
if args.shared_layer_kv_compressed == 1 and compress_layer is None:
compress_layer = nn.Linear(
args.max_positions,
args.max_positions // args.compressed
)
# intialize parameters for compressed layer
nn.init.xavier_uniform_(compress_layer.weight, gain=1 / math.sqrt(2))
if args.freeze_compress == 1:
compress_layer.weight.requires_grad = False
compress_layer = compress_layer
super().__init__(args)
self.layer = nn.ModuleList([LinformerTransformerEncoderLayer(args, compress_layer) for _ in range(args.num_layers)])
self.compress_layer = compress_layer
if args.encoder_normalize_before:
self.layer_norm = LayerNorm(args.embed_dim)
else:
self.layer_norm = None
self.lm_head = None
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
x = super().forward(hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict)
if self.layer_norm is not None:
x.last_hidden_state = self.layer_norm(x.last_hidden_state)
return x
def build_encoder(self, args, dictionary, embed_tokens):
encoder = LinformerTransformerEncoder(args)
return encoder
if args.use_linformer:
encoder = LinformerTransformerEncoder(args, dictionary, embed_tokens)
elif args.use_fft:
encoder = FourierTransformerEncoder(args, dictionary, embed_tokens)
else:
encoder = TransformerEncoder(args, dictionary, embed_tokens)
encoder.apply(init_bert_params)
return encoder
def output_layer(self, features, masked_tokens=None, pairs=None, **unused):
lm_out = self.lm_head(features, masked_tokens)
if pairs is not None:
sbo_out = self.sbo_head(features, pairs)
return lm_out, sbo_out
else:
return lm_out
class Flaubert2Model(RobertaModel):
def __init__(self, config, **kwargs):
onfig_class = Flaubert2Config
base_model_prefix = "flaubert2"
super().__init__(config, **kwargs)
self.embeddings = Flaubert2Embeddings(config)
self.encoder = Flaubert2Encoder(config)
# Copied from modeling_roberta.py
# Add transpose of embeddings as implemented in fairseq
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
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)`.
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 = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
embedding_output = embedding_output.transpose(0,1)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = encoder_outputs[0].transpose(0,1)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
class SBOLayer(nn.Module):
def __init__(self, input_size, hidden_size, activation, export):
super().__init__()
self.layer = nn.Linear(input_size, hidden_size)
self.activ = get_activation_fn(activation)
self.norm = LayerNorm(hidden_size)
def forward(self, x):
return self.norm(self.activ(self.layer(x)))
class SBONetwork(nn.Module):
def __init__(self, input_size, hidden_size, activation, export):
super().__init__()
self.layers = nn.ModuleList([
self.build_sbo_layer(input_size, hidden_size, activation, export),
self.build_sbo_layer(hidden_size, hidden_size, activation, export)
])
self.layers = nn.Sequential(*self.layers)
def build_sbo_layer(self, input_size, output_size, activation, export):
return SBOLayer(input_size, output_size, activation, export)
def forward(self, x):
return self.layers(x)
class SBOHead(nn.Module):
def __init__(self, args, embedding_weights, max_targets=20, position_embedding_size=200):
super().__init__()
self.position_embeddings = nn.Embedding(max_targets, position_embedding_size)
export = getattr(args, "export", False)
hidden_size = args.embed_dim
input_size = hidden_size * 2 + position_embedding_size
activation = getattr(args, "activation_fn", "relu") or "relu"
self.mlp_layer_norm = self.build_sbo_network(input_size, hidden_size, activation, export)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(
embedding_weights.size(1),
embedding_weights.size(0),
bias=False
)
if embedding_weights is not None:
self.decoder.weight = embedding_weights
self.bias = nn.Parameter(torch.zeros(embedding_weights.size(0)))
self.max_targets = max_targets
def build_sbo_network(self, input_size, hidden_size, activation, export):
return SBONetwork(input_size, hidden_size, activation, export)
def forward(self, hidden_states, pairs):
bs, num_pairs, _ = pairs.size()
bs, seq_len, dim = hidden_states.size()
# pair indices: (bs, num_pairs)
left, right = pairs[:,:, 0], pairs[:, :, 1]
# (bs, num_pairs, dim)
left_hidden = torch.gather(hidden_states, 1, left.unsqueeze(2).repeat(1, 1, dim))
# pair states: bs * num_pairs, max_targets, dim
left_hidden = left_hidden.contiguous().view(bs * num_pairs, dim).unsqueeze(1).repeat(1, self.max_targets, 1)
right_hidden = torch.gather(hidden_states, 1, right.unsqueeze(2).repeat(1, 1, dim))
# bs * num_pairs, max_targets, dim
right_hidden = right_hidden.contiguous().view(bs * num_pairs, dim).unsqueeze(1).repeat(1, self.max_targets, 1)
# (max_targets, dim)
position_embeddings = self.position_embeddings.weight
z = torch.cat((left_hidden, right_hidden, position_embeddings.unsqueeze(0).repeat(bs * num_pairs, 1, 1)), -1)
hidden_states = self.mlp_layer_norm(torch.cat((left_hidden, right_hidden, position_embeddings.unsqueeze(0).repeat(bs * num_pairs, 1, 1)), -1))
# target scores : bs * num_pairs, max_targets, vocab_size
target_scores = self.decoder(hidden_states) + self.bias
return target_scores
def get_activation_fn(activation):
"""Returns the activation function corresponding to `activation`"""
if activation == "relu":
return F.relu
elif activation == "relu_squared":
return F.relu_squared
elif activation == "gelu":
return F.gelu
elif activation == "gelu_fast":
deprecation_warning(
"--activation-fn=gelu_fast has been renamed to gelu_accurate"
)
return F.gelu_accurate
elif activation == "gelu_accurate":
return F.gelu_accurate
elif activation == "tanh":
return torch.tanh
elif activation == "linear":
return lambda x: x
elif activation == "swish":
return torch.nn.SiLU
else:
raise RuntimeError("--activation-fn {} not supported".format(activation))
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx