# coding=utf-8
# Copyright 2019-present, Facebook, Inc and the 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.
"""
PyTorch XLM model.
"""
import itertools
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu
from ...file_utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import (
PreTrainedModel,
SequenceSummary,
SQuADHead,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ...utils import logging
from .configuration_xlm import XLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "xlm-mlm-en-2048"
_CONFIG_FOR_DOC = "XLMConfig"
_TOKENIZER_FOR_DOC = "XLMTokenizer"
XLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"xlm-mlm-en-2048",
"xlm-mlm-ende-1024",
"xlm-mlm-enfr-1024",
"xlm-mlm-enro-1024",
"xlm-mlm-tlm-xnli15-1024",
"xlm-mlm-xnli15-1024",
"xlm-clm-enfr-1024",
"xlm-clm-ende-1024",
"xlm-mlm-17-1280",
"xlm-mlm-100-1280",
# See all XLM models at https://huggingface.co/models?filter=xlm
]
def create_sinusoidal_embeddings(n_pos, dim, out):
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
out.requires_grad = False
def get_masks(slen, lengths, causal, padding_mask=None):
"""
Generate hidden states mask, and optionally an attention mask.
"""
alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
if padding_mask is not None:
mask = padding_mask
else:
assert lengths.max().item() <= slen
mask = alen < lengths[:, None]
# attention mask is the same as mask, or triangular inferior attention (causal)
bs = lengths.size(0)
if causal:
attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
else:
attn_mask = mask
# sanity check
assert mask.size() == (bs, slen)
assert causal is False or attn_mask.size() == (bs, slen, slen)
return mask, attn_mask
class MultiHeadAttention(nn.Module):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config):
super().__init__()
self.layer_id = next(MultiHeadAttention.NEW_ID)
self.dim = dim
self.n_heads = n_heads
self.dropout = config.attention_dropout
assert self.dim % self.n_heads == 0
self.q_lin = nn.Linear(dim, dim)
self.k_lin = nn.Linear(dim, dim)
self.v_lin = nn.Linear(dim, dim)
self.out_lin = nn.Linear(dim, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
attention_head_size = self.dim // self.n_heads
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, attention_head_size, self.pruned_heads)
# Prune linear layers
self.q_lin = prune_linear_layer(self.q_lin, index)
self.k_lin = prune_linear_layer(self.k_lin, index)
self.v_lin = prune_linear_layer(self.v_lin, index)
self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
# Update hyper params
self.n_heads = self.n_heads - len(heads)
self.dim = attention_head_size * self.n_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, input, mask, kv=None, cache=None, head_mask=None, output_attentions=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = input.size()
if kv is None:
klen = qlen if cache is None else cache["slen"] + qlen
else:
klen = kv.size(1)
# assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
n_heads = self.n_heads
dim_per_head = self.dim // n_heads
mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)
def shape(x):
"""projection"""
return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
def unshape(x):
"""compute context"""
return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head)
v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, qlen, dim_per_head)
scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, qlen, klen)
mask = (mask == 0).view(mask_reshape).expand_as(scores) # (bs, n_heads, qlen, klen)
scores.masked_fill_(mask, -float("inf")) # (bs, n_heads, qlen, klen)
weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen)
weights = nn.functional.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, qlen, dim)
outputs = (self.out_lin(context),)
if output_attentions:
outputs = outputs + (weights,)
return outputs
class TransformerFFN(nn.Module):
def __init__(self, in_dim, dim_hidden, out_dim, config):
super().__init__()
self.dropout = config.dropout
self.lin1 = nn.Linear(in_dim, dim_hidden)
self.lin2 = nn.Linear(dim_hidden, out_dim)
self.act = gelu if config.gelu_activation else nn.functional.relu
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
def forward(self, input):
return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)
def ff_chunk(self, input):
x = self.lin1(input)
x = self.act(x)
x = self.lin2(x)
x = nn.functional.dropout(x, p=self.dropout, training=self.training)
return x
class XLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMConfig
load_tf_weights = None
base_model_prefix = "transformer"
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
@property
def dummy_inputs(self):
inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
if self.config.use_lang_emb and self.config.n_langs > 1:
langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
else:
langs_list = None
return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, nn.Embedding):
if self.config is not None and self.config.embed_init_std is not None:
nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
if isinstance(module, nn.Linear):
if self.config is not None and self.config.init_std is not None:
nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
if module.bias is not None:
nn.init.constant_(module.bias, 0.0)
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@dataclass
class XLMForQuestionAnsweringOutput(ModelOutput):
"""
Base class for outputs of question answering models using a `SquadHead`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.
start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the top config.start_n_top start token possibilities (beam-search).
start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top config.start_n_top start token possibilities (beam-search).
end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
(beam-search).
end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the `is_impossible` label of the answers.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (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(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.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.
"""
loss: Optional[torch.FloatTensor] = None
start_top_log_probs: Optional[torch.FloatTensor] = None
start_top_index: Optional[torch.LongTensor] = None
end_top_log_probs: Optional[torch.FloatTensor] = None
end_top_index: Optional[torch.LongTensor] = None
cls_logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
XLM_START_DOCSTRING = r"""
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](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model
weights.
"""
XLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`XLMTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
langs (`torch.LongTensor` of shape `({0})`, *optional*):
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
languages ids which can be obtained from the language names by using two conversion mappings provided in
the configuration of the model (only provided for multilingual models). More precisely, the *language name
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
See usage examples detailed in the [multilingual documentation](../multilingual).
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
`[0, ..., input_ids.size(-1)]`.
cache (`Dict[str, torch.FloatTensor]`, *optional*):
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up
sequential decoding.
The dictionary object will be modified in-place during the forward pass to add newly computed
hidden-states.
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 `({0}, 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.
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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
XLM_START_DOCSTRING,
)
class XLMModel(XLMPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
# encoder / decoder, output layer
self.is_encoder = config.is_encoder
self.is_decoder = not config.is_encoder
if self.is_decoder:
raise NotImplementedError("Currently XLM can only be used as an encoder")
# self.with_output = with_output
self.causal = config.causal
# dictionary / languages
self.n_langs = config.n_langs
self.use_lang_emb = config.use_lang_emb
self.n_words = config.n_words
self.eos_index = config.eos_index
self.pad_index = config.pad_index
# self.dico = dico
# self.id2lang = config.id2lang
# self.lang2id = config.lang2id
# assert len(self.dico) == self.n_words
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
# model parameters
self.dim = config.emb_dim # 512 by default
self.hidden_dim = self.dim * 4 # 2048 by default
self.n_heads = config.n_heads # 8 by default
self.n_layers = config.n_layers
self.dropout = config.dropout
self.attention_dropout = config.attention_dropout
assert self.dim % self.n_heads == 0, "transformer dim must be a multiple of n_heads"
# embeddings
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
if config.sinusoidal_embeddings:
create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
if config.n_langs > 1 and config.use_lang_emb:
self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
# transformer layers
self.attentions = nn.ModuleList()
self.layer_norm1 = nn.ModuleList()
self.ffns = nn.ModuleList()
self.layer_norm2 = nn.ModuleList()
# if self.is_decoder:
# self.layer_norm15 = nn.ModuleList()
# self.encoder_attn = nn.ModuleList()
for _ in range(self.n_layers):
self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config))
self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# if self.is_decoder:
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
if hasattr(config, "pruned_heads"):
pruned_heads = config.pruned_heads.copy().items()
config.pruned_heads = {}
for layer, heads in pruned_heads:
if self.attentions[int(layer)].n_heads == config.n_heads:
self.prune_heads({int(layer): list(map(int, heads))})
# Initialize weights and apply final processing
self.post_init()
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.attentions[layer].prune_heads(heads)
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
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 input_ids is not None:
bs, slen = input_ids.size()
else:
bs, slen = inputs_embeds.size()[:-1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if lengths is None:
if input_ids is not None:
lengths = (input_ids != self.pad_index).sum(dim=1).long()
else:
lengths = torch.tensor([slen] * bs, device=device)
# mask = input_ids != self.pad_index
# check inputs
assert lengths.size(0) == bs
assert lengths.max().item() <= slen
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
# assert (src_enc is None) == (src_len is None)
# if src_enc is not None:
# assert self.is_decoder
# assert src_enc.size(0) == bs
# generate masks
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
# if self.is_decoder and src_enc is not None:
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
# position_ids
if position_ids is None:
position_ids = self.position_ids[:, :slen]
else:
assert position_ids.size() == (bs, slen) # (slen, bs)
# position_ids = position_ids.transpose(0, 1)
# langs
if langs is not None:
assert langs.size() == (bs, slen) # (slen, bs)
# langs = langs.transpose(0, 1)
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.n_layers)
# do not recompute cached elements
if cache is not None and input_ids is not None:
_slen = slen - cache["slen"]
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
if langs is not None:
langs = langs[:, -_slen:]
mask = mask[:, -_slen:]
attn_mask = attn_mask[:, -_slen:]
# embeddings
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
if langs is not None and self.use_lang_emb and self.n_langs > 1:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
tensor = tensor + self.embeddings(token_type_ids)
tensor = self.layer_norm_emb(tensor)
tensor = nn.functional.dropout(tensor, p=self.dropout, training=self.training)
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
# transformer layers
hidden_states = () if output_hidden_states else None
attentions = () if output_attentions else None
for i in range(self.n_layers):
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# self attention
attn_outputs = self.attentions[i](
tensor,
attn_mask,
cache=cache,
head_mask=head_mask[i],
output_attentions=output_attentions,
)
attn = attn_outputs[0]
if output_attentions:
attentions = attentions + (attn_outputs[1],)
attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
# encoder attention (for decoder only)
# if self.is_decoder and src_enc is not None:
# attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
# attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
# tensor = tensor + attn
# tensor = self.layer_norm15[i](tensor)
# FFN
tensor = tensor + self.ffns[i](tensor)
tensor = self.layer_norm2[i](tensor)
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
# Add last hidden state
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# update cache length
if cache is not None:
cache["slen"] += tensor.size(1)
# move back sequence length to dimension 0
# tensor = tensor.transpose(0, 1)
if not return_dict:
return tuple(v for v in [tensor, hidden_states, attentions] if v is not None)
return BaseModelOutput(last_hidden_state=tensor, hidden_states=hidden_states, attentions=attentions)
class XLMPredLayer(nn.Module):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config):
super().__init__()
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_index
dim = config.emb_dim
if config.asm is False:
self.proj = nn.Linear(dim, config.n_words, bias=True)
else:
self.proj = nn.AdaptiveLogSoftmaxWithLoss(
in_features=dim,
n_classes=config.n_words,
cutoffs=config.asm_cutoffs,
div_value=config.asm_div_value,
head_bias=True, # default is False
)
def forward(self, x, y=None):
"""Compute the loss, and optionally the scores."""
outputs = ()
if self.asm is False:
scores = self.proj(x)
outputs = (scores,) + outputs
if y is not None:
loss = nn.functional.cross_entropy(
scores.view(-1, self.n_words), y.view(-1), reduction="elementwise_mean"
)
outputs = (loss,) + outputs
else:
scores = self.proj.log_prob(x)
outputs = (scores,) + outputs
if y is not None:
_, loss = self.proj(x, y)
outputs = (loss,) + outputs
return outputs
@add_start_docstrings(
"""
The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
XLM_START_DOCSTRING,
)
class XLMWithLMHeadModel(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.pred_layer = XLMPredLayer(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.pred_layer.proj
def set_output_embeddings(self, new_embeddings):
self.pred_layer.proj = new_embeddings
def prepare_inputs_for_generation(self, input_ids, **kwargs):
mask_token_id = self.config.mask_token_id
lang_id = self.config.lang_id
effective_batch_size = input_ids.shape[0]
mask_token = torch.full((effective_batch_size, 1), mask_token_id, dtype=torch.long, device=input_ids.device)
input_ids = torch.cat([input_ids, mask_token], dim=1)
if lang_id is not None:
langs = torch.full_like(input_ids, lang_id)
else:
langs = None
return {"input_ids": input_ids, "langs": langs}
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<special1>",
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to
`-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
output = transformer_outputs[0]
outputs = self.pred_layer(output, labels) # (loss, logits) or (logits,) depending on if labels are provided.
if not return_dict:
return outputs + transformer_outputs[1:]
return MaskedLMOutput(
loss=outputs[0] if labels is not None else None,
logits=outputs[0] if labels is None else outputs[1],
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
""",
XLM_START_DOCSTRING,
)
class XLMForSequenceClassification(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
XLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_START_DOCSTRING,
)
class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
XLM Model with a beam-search span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_START_DOCSTRING,
)
class XLMForQuestionAnswering(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = SQuADHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=XLMForQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
is_impossible=None,
cls_index=None,
p_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels whether a question has an answer or no answer (SQuAD 2.0)
cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the classification token to use as input for computing plausibility of the
answer.
p_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...). 1.0 means token should be
masked. 0.0 mean token is not masked.
Returns:
Example:
```python
>>> from transformers import XLMTokenizer, XLMForQuestionAnswering
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
>>> model = XLMForQuestionAnswering.from_pretrained('xlm-mlm-en-2048')
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
output = transformer_outputs[0]
outputs = self.qa_outputs(
output,
start_positions=start_positions,
end_positions=end_positions,
cls_index=cls_index,
is_impossible=is_impossible,
p_mask=p_mask,
return_dict=return_dict,
)
if not return_dict:
return outputs + transformer_outputs[1:]
return XLMForQuestionAnsweringOutput(
loss=outputs.loss,
start_top_log_probs=outputs.start_top_log_probs,
start_top_index=outputs.start_top_index,
end_top_log_probs=outputs.end_top_log_probs,
end_top_index=outputs.end_top_index,
cls_logits=outputs.cls_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
XLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
XLM_START_DOCSTRING,
)
class XLMForTokenClassification(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = XLMModel(config)
self.dropout = nn.Dropout(config.dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
XLM Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
XLM_START_DOCSTRING,
)
class XLMForMultipleChoice(XLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.num_labels, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
langs = langs.view(-1, langs.size(-1)) if langs is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
if lengths is not None:
logger.warning(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead."
)
lengths = None
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
logits = self.logits_proj(logits)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)