# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors 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 BART model, ported from the fairseq repo."""
import logging
import math
import random
import warnings
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss
from .activations import ACT2FN
from .configuration_bart import BartConfig
from .file_utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
)
from .modeling_utils import PreTrainedModel
logger = logging.getLogger(__name__)
_TOKENIZER_FOR_DOC = "BartTokenizer"
BART_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/bart-base",
"facebook/bart-large",
"facebook/bart-large-mnli",
"facebook/bart-large-cnn",
"facebook/bart-large-xsum",
"facebook/mbart-large-en-ro",
# See all BART models at https://huggingface.co/models?filter=bart
]
BART_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use it as a regular PyTorch Module and
refer to the PyTorch documentation for all matters related to general usage and behavior.
Parameters:
config (:class:`~transformers.BartConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
BART_GENERATION_EXAMPLE = r"""
Summarization example::
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
# see ``examples/summarization/bart/run_eval.py`` for a longer example
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
# Generate Summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
"""
BART_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Use BartTokenizer.encode to produce them.
Padding will be ignored by default should you provide it.
Indices can be obtained using :class:`transformers.BartTokenizer.encode(text)`.
attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices in input_ids.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`, defaults to :obj:`None`):
Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`) is a sequence of hidden-states at the output of the last layer of the encoder.
Used in the cross-attention of the decoder.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids right, following the paper.
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
See diagram 1 in the paper for more info on the default strategy
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
"""
def invert_mask(attention_mask):
"""Turns 1->0, 0->1, False->True, True-> False"""
assert attention_mask.dim() == 2
return attention_mask.eq(0)
class PretrainedBartModel(PreTrainedModel):
config_class = BartConfig
base_model_prefix = "model"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, SinusoidalPositionalEmbedding):
pass
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
def _make_linear_from_emb(emb):
vocab_size, emb_size = emb.weight.shape
lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
lin_layer.weight.data = emb.weight.data
return lin_layer
# Helper Functions, mostly for making masks
def _check_shapes(shape_1, shape2):
if shape_1 != shape2:
raise AssertionError("shape mismatch: {} != {}".format(shape_1, shape2))
def shift_tokens_right(input_ids, pad_token_id):
"""Shift input ids one token to the right, and wrap the last non pad token (usually <eos>)."""
prev_output_tokens = input_ids.clone()
index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
prev_output_tokens[:, 1:] = input_ids[:, :-1]
return prev_output_tokens
def make_padding_mask(input_ids, padding_idx=1):
"""True for pad tokens"""
padding_mask = input_ids.eq(padding_idx)
if not padding_mask.any():
padding_mask = None
return padding_mask
# Helper Modules
class EncoderLayer(nn.Module):
def __init__(self, config: BartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SelfAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout,
)
self.normalize_before = config.normalize_before
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
def forward(self, x, encoder_padding_mask, output_attentions=False):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
for t_tgt, t_src is excluded (or masked out), =0 means it is
included in attention
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, attn_weights = self.self_attn(
query=x, key=x, key_padding_mask=encoder_padding_mask, output_attentions=output_attentions
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if not self.normalize_before:
x = self.final_layer_norm(x)
return x, attn_weights
class BartEncoder(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer
is a :class:`EncoderLayer`.
Args:
config: BartConfig
"""
def __init__(self, config: BartConfig, embed_tokens):
super().__init__()
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = embed_tokens.embedding_dim
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.padding_idx = embed_tokens.padding_idx
self.max_source_positions = config.max_position_embeddings
self.embed_tokens = embed_tokens
if config.static_position_embeddings:
self.embed_positions = SinusoidalPositionalEmbedding(
config.max_position_embeddings, embed_dim, self.padding_idx
)
else:
self.embed_positions = LearnedPositionalEmbedding(
config.max_position_embeddings, embed_dim, self.padding_idx, config.extra_pos_embeddings,
)
self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = LayerNorm(embed_dim) if config.normalize_embedding else nn.Identity()
# mbart has one extra layer_norm
self.layer_norm = LayerNorm(config.d_model) if config.normalize_before else None
def forward(self, input_ids, attention_mask=None, output_attentions=False, output_hidden_states=False):
"""
Args:
input_ids (LongTensor): tokens in the source language of shape
`(batch, src_len)`
attention_mask (torch.LongTensor): indicating which indices are padding tokens.
Returns:
Tuple comprised of:
- **x** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *output_hidden_states:* is True.
- **all_attentions** (List[Tensor]): Attention weights for each layer.
During training might not be of length n_layers because of layer dropout.
"""
# check attention mask and invert
if attention_mask is not None:
attention_mask = invert_mask(attention_mask)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_ids)
x = inputs_embeds + embed_pos
x = self.layernorm_embedding(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states, all_attentions = [], []
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states.append(x)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop): # skip the layer
attn = None
else:
x, attn = encoder_layer(x, attention_mask, output_attentions=output_attentions)
if output_attentions:
all_attentions.append(attn)
if self.layer_norm:
x = self.layer_norm(x)
if output_hidden_states:
encoder_states.append(x)
# T x B x C -> B x T x C
encoder_states = [hidden_state.transpose(0, 1) for hidden_state in encoder_states]
x = x.transpose(0, 1)
return x, encoder_states, all_attentions
class DecoderLayer(nn.Module):
def __init__(self, config: BartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SelfAttention(
embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.normalize_before = config.normalize_before
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.encoder_attn = SelfAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
def forward(
self,
x,
encoder_hidden_states,
encoder_attn_mask=None,
layer_state=None,
causal_mask=None,
decoder_padding_mask=None,
output_attentions=False,
):
residual = x
if layer_state is None:
layer_state = {}
if self.normalize_before:
x = self.self_attn_layer_norm(x)
# Self Attention
x, self_attn_weights = self.self_attn(
query=x,
key=x,
layer_state=layer_state, # adds keys to layer state
key_padding_mask=decoder_padding_mask,
attn_mask=causal_mask,
output_attentions=output_attentions,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
# Cross attention
residual = x
assert self.encoder_attn.cache_key != self.self_attn.cache_key
if self.normalize_before:
x = self.encoder_attn_layer_norm(x)
x, _ = self.encoder_attn(
query=x,
key=encoder_hidden_states,
key_padding_mask=encoder_attn_mask,
layer_state=layer_state, # mutates layer state
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if not self.normalize_before:
x = self.encoder_attn_layer_norm(x)
# Fully Connected
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if not self.normalize_before:
x = self.final_layer_norm(x)
return (
x,
self_attn_weights,
layer_state,
) # just self_attn weights for now, following t5, layer_state = cache for decoding
class BartDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer
is a :class:`DecoderLayer`.
Args:
config: BartConfig
embed_tokens (torch.nn.Embedding): output embedding
"""
def __init__(self, config: BartConfig, embed_tokens: nn.Embedding):
super().__init__()
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = embed_tokens.padding_idx
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
if config.static_position_embeddings:
self.embed_positions = SinusoidalPositionalEmbedding(
config.max_position_embeddings, config.d_model, config.pad_token_id
)
else:
self.embed_positions = LearnedPositionalEmbedding(
config.max_position_embeddings, config.d_model, self.padding_idx, config.extra_pos_embeddings,
)
self.layers = nn.ModuleList(
[DecoderLayer(config) for _ in range(config.decoder_layers)]
) # type: List[DecoderLayer]
self.layernorm_embedding = LayerNorm(config.d_model) if config.normalize_embedding else nn.Identity()
self.layer_norm = LayerNorm(config.d_model) if config.add_final_layer_norm else None
def forward(
self,
input_ids,
encoder_hidden_states,
encoder_padding_mask,
decoder_padding_mask,
decoder_causal_mask,
decoder_cached_states=None,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
**unused,
):
"""
Includes several features from "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Args:
input_ids (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_hidden_states: output from the encoder, used for
encoder-side attention
encoder_padding_mask: for ignoring pad tokens
decoder_cached_states (dict or None): dictionary used for storing state during generation
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- hidden states
- attentions
"""
# check attention mask and invert
if encoder_padding_mask is not None:
encoder_padding_mask = invert_mask(encoder_padding_mask)
# embed positions
positions = self.embed_positions(input_ids, use_cache=use_cache)
if use_cache:
input_ids = input_ids[:, -1:]
positions = positions[:, -1:] # happens after we embed them
# assert input_ids.ne(self.padding_idx).any()
x = self.embed_tokens(input_ids) * self.embed_scale
x += positions
x = self.layernorm_embedding(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# Convert to Bart output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
x = x.transpose(0, 1)
encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
# decoder layers
all_hidden_states = ()
all_self_attns = ()
next_decoder_cache = []
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (x,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
layer_state = decoder_cached_states[idx] if decoder_cached_states is not None else None
x, layer_self_attn, layer_past = decoder_layer(
x,
encoder_hidden_states,
encoder_attn_mask=encoder_padding_mask,
decoder_padding_mask=decoder_padding_mask,
layer_state=layer_state,
causal_mask=decoder_causal_mask,
output_attentions=output_attentions,
)
if use_cache:
next_decoder_cache.append(layer_past.copy())
if self.layer_norm and (idx == len(self.layers) - 1): # last layer of mbart
x = self.layer_norm(x)
if output_attentions:
all_self_attns += (layer_self_attn,)
# Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
all_hidden_states = [hidden_state.transpose(0, 1) for hidden_state in all_hidden_states]
x = x.transpose(0, 1)
encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
if use_cache:
next_cache = ((encoder_hidden_states, encoder_padding_mask), next_decoder_cache)
else:
next_cache = None
return x, next_cache, all_hidden_states, list(all_self_attns)
def _reorder_buffer(attn_cache, new_order):
for k, input_buffer_k in attn_cache.items():
if input_buffer_k is not None:
attn_cache[k] = input_buffer_k.index_select(0, new_order)
return attn_cache
class SelfAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
encoder_decoder_attention=False, # otherwise self_attention
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.encoder_decoder_attention = encoder_decoder_attention
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"
def _shape(self, tensor, dim_0, bsz):
return tensor.contiguous().view(dim_0, bsz * self.num_heads, self.head_dim).transpose(0, 1)
def forward(
self,
query,
key: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
attn_mask: Optional[Tensor] = None,
output_attentions=False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time(SeqLen) x Batch x Channel"""
static_kv: bool = self.encoder_decoder_attention
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
# get here for encoder decoder cause of static_kv
if layer_state is not None: # reuse k,v and encoder_padding_mask
saved_state = layer_state.get(self.cache_key, {})
if "prev_key" in saved_state:
# previous time steps are cached - no need to recompute key and value if they are static
if static_kv:
key = None
else:
saved_state = None
layer_state = {}
q = self.q_proj(query) * self.scaling
if static_kv:
if key is None:
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
k = self.k_proj(query)
v = self.v_proj(query)
q = self._shape(q, tgt_len, bsz)
if k is not None:
k = self._shape(k, -1, bsz)
if v is not None:
v = self._shape(v, -1, bsz)
if saved_state is not None:
k, v, key_padding_mask = self._use_saved_state(k, v, saved_state, key_padding_mask, static_kv, bsz)
# Update cache
layer_state[self.cache_key] = {
"prev_key": k.view(bsz, self.num_heads, -1, self.head_dim),
"prev_value": v.view(bsz, self.num_heads, -1, self.head_dim),
"prev_key_padding_mask": key_padding_mask if not static_kv else None,
}
assert k is not None
src_len = k.size(1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)
if attn_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# This is part of a workaround to get around fork/join parallelism not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.dim() == 0:
key_padding_mask = None
assert key_padding_mask is None or key_padding_mask.size()[:2] == (bsz, src_len,)
if key_padding_mask is not None: # don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training,)
assert v is not None
attn_output = torch.bmm(attn_probs, v)
assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = self.out_proj(attn_output)
if output_attentions:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
else:
attn_weights = None
return attn_output, attn_weights
def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
assert k is not None and v is not None
prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
key_padding_mask = self._cat_prev_key_padding_mask(
key_padding_mask, prev_key_padding_mask, bsz, k.size(1), static_kv
)
return k, v, key_padding_mask
@staticmethod
def _cat_prev_key_padding_mask(
key_padding_mask: Optional[Tensor],
prev_key_padding_mask: Optional[Tensor],
batch_size: int,
src_len: int,
static_kv: bool,
) -> Optional[Tensor]:
# saved key padding masks have shape (bsz, seq_len)
if prev_key_padding_mask is not None:
if static_kv:
new_key_padding_mask = prev_key_padding_mask
else:
new_key_padding_mask = torch.cat([prev_key_padding_mask, key_padding_mask], dim=1)
elif key_padding_mask is not None:
filler = torch.zeros(
batch_size,
src_len - key_padding_mask.size(1),
dtype=key_padding_mask.dtype,
device=key_padding_mask.device,
)
new_key_padding_mask = torch.cat([filler, key_padding_mask], dim=1)
else:
new_key_padding_mask = prev_key_padding_mask
return new_key_padding_mask
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
# This can trivially be shared with RobertaClassificationHead
def __init__(
self, input_dim, inner_dim, num_classes, pooler_dropout,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, x):
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring that the appropriate
position ids are passed to the forward function.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, offset):
# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models dont have this hack
self.offset = offset
assert padding_idx is not None
num_embeddings += offset
super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
def forward(self, input_ids, use_cache=False):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input_ids.shape[:2]
if use_cache:
positions = input_ids.data.new(1, 1).fill_(seq_len - 1) # called before slicing
else:
# starts at 0, ends at 1-seq_len
positions = torch.arange(seq_len, dtype=torch.long, device=self.weight.device)
return super().forward(positions + self.offset)
def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True):
if torch.cuda.is_available():
try:
from apex.normalization import FusedLayerNorm
return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
except ImportError:
pass
return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a input_ids with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
def _filter_out_falsey_values(tup) -> Tuple:
"""Remove entries that are None or [] from an iterable."""
return tuple(x for x in tup if isinstance(x, torch.Tensor) or x)
# Public API
def _get_shape(t):
return getattr(t, "shape", None)
[docs]@add_start_docstrings(
"The bare BART Model outputting raw hidden-states without any specific head on top.", BART_START_DOCSTRING,
)
class BartModel(PretrainedBartModel):
def __init__(self, config: BartConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = BartEncoder(config, self.shared)
self.decoder = BartDecoder(config, self.shared)
self.init_weights()
[docs] @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="facebook/bart-large")
def forward(
self,
input_ids,
attention_mask=None,
decoder_input_ids=None,
encoder_outputs: Optional[Tuple] = None,
decoder_attention_mask=None,
decoder_cached_states=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
):
if decoder_input_ids is None:
use_cache = False
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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
# make masks if user doesn't supply
if not use_cache:
decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_bart_decoder_inputs(
self.config,
input_ids,
decoder_input_ids=decoder_input_ids,
decoder_padding_mask=decoder_attention_mask,
causal_mask_dtype=self.shared.weight.dtype,
)
else:
decoder_padding_mask, causal_mask = None, None
assert decoder_input_ids is not None
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
assert isinstance(encoder_outputs, tuple)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
decoder_input_ids,
encoder_outputs[0],
attention_mask,
decoder_padding_mask,
decoder_causal_mask=causal_mask,
decoder_cached_states=decoder_cached_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
)
# Attention and hidden_states will be [] or None if they aren't needed
decoder_outputs: Tuple = _filter_out_falsey_values(decoder_outputs)
assert isinstance(decoder_outputs[0], torch.Tensor)
encoder_outputs: Tuple = _filter_out_falsey_values(encoder_outputs)
return decoder_outputs + encoder_outputs
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_output_embeddings(self):
return _make_linear_from_emb(self.shared) # make it on the fly
[docs]@add_start_docstrings(
"The BART Model with a language modeling head. Can be used for summarization.", BART_START_DOCSTRING
)
class BartForConditionalGeneration(PretrainedBartModel):
base_model_prefix = "model"
def __init__(self, config: BartConfig):
super().__init__(config)
base_model = BartModel(config)
self.model = base_model
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
old_num_tokens = self.model.shared.num_embeddings
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self.model.shared = new_embeddings
self._resize_final_logits_bias(new_num_tokens, old_num_tokens)
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int, old_num_tokens: int) -> None:
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
[docs] @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_end_docstrings(BART_GENERATION_EXAMPLE)
def forward(
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_cached_states=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
**unused,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Labels for computing the masked language modeling loss.
Indices should either be in ``[0, ..., config.vocab_size]`` or -100 (see ``input_ids`` docstring).
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens
with labels
in ``[0, ..., config.vocab_size]``.
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(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.
Conditional generation example::
# Mask filling only works for bart-large
from transformers import BartTokenizer, BartForConditionalGeneration
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
TXT = "My friends are <mask> but they eat too many carbs."
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
logits = model(input_ids)[0]
masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
probs = logits[0, masked_index].softmax(dim=0)
values, predictions = probs.topk(5)
tokenizer.decode(predictions).split()
# ['good', 'great', 'all', 'really', 'very']
"""
if "lm_labels" in unused:
warnings.warn(
"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
DeprecationWarning,
)
labels = unused.pop("lm_labels")
if labels is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_cached_states=decoder_cached_states,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
lm_logits = F.linear(outputs[0], self.model.shared.weight, bias=self.final_logits_bias)
outputs = (lm_logits,) + outputs[1:] # Add cache, hidden states and attention if they are here
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
# TODO(SS): do we need to ignore pad tokens in labels?
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
outputs = (masked_lm_loss,) + outputs
return outputs
def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs):
assert past is not None, "past has to be defined for encoder_outputs"
encoder_outputs, decoder_cached_states = past
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"decoder_cached_states": decoder_cached_states,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def adjust_logits_during_generation(self, logits, cur_len, max_length):
if cur_len == 1:
self._force_token_ids_generation(logits, self.config.bos_token_id)
if cur_len == max_length - 1 and self.config.eos_token_id is not None:
self._force_token_ids_generation(logits, self.config.eos_token_id)
return logits
def _force_token_ids_generation(self, scores, token_ids) -> None:
"""force one of token_ids to be generated by setting prob of all other tokens to 0"""
if isinstance(token_ids, int):
token_ids = [token_ids]
all_but_token_ids_mask = torch.tensor(
[x for x in range(self.config.vocab_size) if x not in token_ids],
dtype=torch.long,
device=next(self.parameters()).device,
)
assert len(scores.shape) == 2, "scores should be of rank 2 with shape: [batch_size, vocab_size]"
scores[:, all_but_token_ids_mask] = -float("inf")
@staticmethod
def _reorder_cache(past, beam_idx):
((enc_out, enc_mask), decoder_cached_states) = past
reordered_past = []
for layer_past in decoder_cached_states:
# get the correct batch idx from decoder layer's batch dim for cross and self-attn
layer_past_new = {
attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()
}
reordered_past.append(layer_past_new)
new_enc_out = enc_out if enc_out is None else enc_out.index_select(0, beam_idx)
new_enc_mask = enc_mask if enc_mask is None else enc_mask.index_select(0, beam_idx)
past = ((new_enc_out, new_enc_mask), reordered_past)
return past
def get_encoder(self):
return self.model.encoder
def get_output_embeddings(self):
return _make_linear_from_emb(self.model.shared) # make it on the fly
[docs]@add_start_docstrings(
"""Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """,
BART_START_DOCSTRING,
)
class BartForSequenceClassification(PretrainedBartModel):
def __init__(self, config: BartConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BartModel(config)
self.classification_head = BartClassificationHead(
config.d_model, config.d_model, config.num_labels, config.classif_dropout,
)
self.model._init_weights(self.classification_head.dense)
self.model._init_weights(self.classification_head.out_proj)
[docs] @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="facebook/bart-large")
def forward(
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
use_cache=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for computing the sequence classification/regression loss.
Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BartConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
Classification loss (cross entropy)
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(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.
"""
if labels is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
)
x = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id)
if len(torch.unique(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = x[eos_mask, :].view(x.size(0), -1, x.size(-1))[:, -1, :]
logits = self.classification_head(sentence_representation)
# Prepend logits
outputs = (logits,) + outputs[1:] # Add hidden states and attention if they are here
if labels is not None: # prepend loss to output,
loss = F.cross_entropy(logits.view(-1, self.config.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs
[docs]@add_start_docstrings(
"""BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of
the hidden-states output to compute `span start logits` and `span end logits`). """,
BART_START_DOCSTRING,
)
class BartForQuestionAnswering(PretrainedBartModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = BartModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.model._init_weights(self.qa_outputs)
[docs] @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="facebook/bart-large")
def forward(
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
use_cache=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
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 (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
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.
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BartConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax).
end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(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.
"""
if start_positions is not None and end_positions is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + outputs[1:]
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.clamp_(0, ignored_index)
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
outputs = (total_loss,) + outputs
return outputs # return outputs # (loss), start_logits, end_logits, encoder_outputs, (hidden_states), (attentions)
class SinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions, embedding_dim, padding_idx=None):
super().__init__(num_positions, embedding_dim)
if embedding_dim % 2 != 0:
raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(out: nn.Parameter):
"""Identical to the XLM create_sinusoidal_embeddings except features are not interleaved.
The cos features are in the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
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 : dim // 2] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) # This line breaks for odd n_pos
out[:, dim // 2 :] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
out.requires_grad = False
return out
@torch.no_grad()
def forward(self, input_ids, use_cache=False):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input_ids.shape[:2]
if use_cache:
positions = input_ids.data.new(1, 1).fill_(seq_len - 1) # called before slicing
else:
# starts at 0, ends at 1-seq_len
positions = torch.arange(seq_len, dtype=torch.long, device=self.weight.device)
return super().forward(positions)