Source code for transformers.modeling_bart

# 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch BART model, ported from the fairseq repo."""
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,
    replace_return_docstrings,
)
from .modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPast,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
    Seq2SeqQuestionAnsweringModelOutput,
    Seq2SeqSequenceClassifierOutput,
)
from .modeling_utils import PreTrainedModel
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BartConfig"
_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",
]
# This list is incomplete. 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`):
            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`):
            Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
            `last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`) 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`):
            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`):
            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
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains pre-computed 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
            :obj:`(batch_size, 1)` instead of all ``decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
            If `use_cache` is True, ``past_key_values`` are returned and can be used to speed up decoding (see
            ``past_key_values``).
        output_attentions (:obj:`bool`, `optional`):
            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`):
            If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
            plain tuple.
"""


def invert_mask(attention_mask):
    """Turns 1->0, 0->1, False->True, True-> False"""
    assert attention_mask.dim() == 2
    return attention_mask.eq(0)


[docs]def _prepare_bart_decoder_inputs( config, input_ids, decoder_input_ids=None, decoder_padding_mask=None, causal_mask_dtype=torch.float32 ): """Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if none are provided. This mimics the default behavior in fairseq. To override it pass in masks. Note: this is not called during generation """ pad_token_id = config.pad_token_id if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(input_ids, pad_token_id) bsz, tgt_len = decoder_input_ids.size() if decoder_padding_mask is None: decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id) else: decoder_padding_mask = invert_mask(decoder_padding_mask) if decoder_padding_mask is not None and decoder_padding_mask.shape[1] > 1: # never mask leading token, even if it is pad decoder_padding_mask[:, 0] = decoder_padding_mask[:, 1] tmp = fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)) mask = torch.arange(tmp.size(-1)) tmp.masked_fill_(mask < (mask + 1).view(tmp.size(-1), 1), 0) causal_mask = tmp.to(dtype=causal_mask_dtype, device=decoder_input_ids.device) return decoder_input_ids, decoder_padding_mask, causal_mask
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 = Attention(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, return_dict=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: BaseModelOutput or Tuple comprised of: - **x** (Tensor): the last encoder layer's output of shape `(src_len, batch, embed_dim)` - **encoder_states** (tuple(torch.FloatTensor)): all intermediate hidden states of shape `(src_len, batch, embed_dim)`. Only populated if *output_hidden_states:* is True. - **all_attentions** (tuple(torch.FloatTensor)): 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 = [] if output_hidden_states else None all_attentions = () if output_attentions else None 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 = all_attentions + (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 = tuple(hidden_state.transpose(0, 1) for hidden_state in encoder_states) # T x B x C -> B x T x C x = x.transpose(0, 1) if not return_dict: return tuple(v for v in [x, encoder_states, all_attentions] if v is not None) return BaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions) class DecoderLayer(nn.Module): def __init__(self, config: BartConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = Attention( 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 = Attention( 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, past_key_values=None, use_cache=False, output_attentions=False, output_hidden_states=False, return_dict=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 past_key_values (dict or None): dictionary used for storing state during generation Returns: BaseModelOutputWithPast or tuple: - the decoder's features of shape `(batch, tgt_len, embed_dim)` - the cache - hidden states - attentions """ if "decoder_cached_states" in unused: warnings.warn( "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = unused.pop("decoder_cached_states") if "decoder_past_key_values" in unused: warnings.warn( "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = unused.pop("decoder_past_key_values") # 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 = () if output_hidden_states else None all_self_attns = () if output_attentions else None 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 = past_key_values[idx] if past_key_values 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): # if config.add_final_layer_norm (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) if output_hidden_states: all_hidden_states = tuple(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) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple(v for v in [x, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=x, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=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 Attention(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, seq_len, bsz): return tensor.contiguous().view(seq_len, 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 and static_kv: # previous time steps are cached - no need to recompute key and value if they are static 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) 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) else: new_key_padding_mask = key_padding_mask return k, v, 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) # 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", output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids, attention_mask=None, decoder_input_ids=None, encoder_outputs: Optional[Tuple] = None, decoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs, ): if "decoder_past_key_values" in kwargs: warnings.warn( "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = kwargs.pop("decoder_past_key_values") 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 return_dict = return_dict if return_dict is not None else self.config.use_return_dict # 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, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOuput when return_dict=False elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # 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, 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 not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, )
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" authorized_missing_keys = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"] 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) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @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, past_key_values=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **unused, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): 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: 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).logits >>> 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.", FutureWarning, ) labels = unused.pop("lm_labels") if "decoder_cached_states" in unused: warnings.warn( "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = unused.pop("decoder_cached_states") if "decoder_past_key_values" in unused: warnings.warn( "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = unused.pop("decoder_past_key_values") return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id) 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, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = F.linear(outputs[0], self.model.shared.weight, bias=self.final_logits_bias) masked_lm_loss = None if labels is not None: loss_fct = 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)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return Seq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, )
def prepare_inputs_for_generation( self, decoder_input_ids, past, attention_mask, use_cache, encoder_outputs, **kwargs ): return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past, "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 and self.config.force_bos_token_to_be_generated: self._force_token_ids_generation(logits, self.config.bos_token_id) elif 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_id) -> None: """force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))""" scores[:, [x for x in range(self.config.vocab_size) if x != token_id]] = -float("inf") @staticmethod def _reorder_cache(past, beam_idx): reordered_past = [] for layer_past in past: # 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) return reordered_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", output_type=Seq2SeqSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=None, decoder_attention_mask=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): 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). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) 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) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return Seq2SeqSequenceClassifierOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, )
[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", output_type=Seq2SeqQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) 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, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(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 (:obj:`torch.LongTensor` of shape :obj:`(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 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, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) 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) 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.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 if not return_dict: output = ( start_logits, end_logits, ) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return Seq2SeqQuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_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)