long_models/longformer_bart.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""

This code is in part adapted from AllenAI's Longformer:
    https://github.com/allenai/longformer/
and in part adapted from:
    https://github.com/huggingface/transformers

Author: Annette Rios (rios@cl.uzh.ch)

"""
from typing import List, Optional, Tuple, Dict, Union
from torch import nn, Tensor, zeros
import torch
import math
import random
from .longformer import LongformerSelfAttention
from transformers.models.bart.modeling_bart import BartConfig, BartForConditionalGeneration, BartEncoder, BartLearnedPositionalEmbedding, BartEncoderLayer, BartDecoder, BartModel, _expand_mask
from transformers.modeling_outputs import BaseModelOutput

class LongformerEncoderDecoderForConditionalGeneration(BartForConditionalGeneration):
    def __init__(self, config):
        super(BartForConditionalGeneration, self).__init__(config)

        self.model = LongBartModel(config)
        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
        #print(self)

        if config.attention_mode == 'n2':
            pass  # do nothing, use BartSelfAttention instead
        else:
            for i, layer in enumerate(self.model.encoder.layers):
                layer.self_attn = LongformerSelfAttentionForBart(config, layer_id=i)
        # Initialize weights and apply final processing
        self.post_init()


class LongformerEncoderDecoderConfig(BartConfig):
    def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
                 autoregressive: bool = False, attention_mode: str = 'sliding_chunks',
                 gradient_checkpointing: bool = False, **kwargs):
        """
        Args:
            attention_window: list of attention window sizes of length = number of layers.
                window size = number of attention locations on each side.
                For an affective window size of 512, use `attention_window=[256]*num_layers`
                which is 256 on each side.
            attention_dilation: list of attention dilation of length = number of layers.
                attention dilation of `1` means no dilation.
            autoregressive: do autoregressive attention or have attention of both sides
            attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
                selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
        """
        super().__init__(**kwargs)
        self.attention_window = attention_window
        self.attention_dilation = attention_dilation
        self.autoregressive = autoregressive
        self.attention_mode = attention_mode
        self.gradient_checkpointing = gradient_checkpointing
        assert self.attention_mode in ['tvm', 'sliding_chunks', 'n2']

class LongformerSelfAttentionForBart(nn.Module):
    def __init__(self, config, layer_id):
        super().__init__()
        self.embed_dim = config.d_model
        self.longformer_self_attn = LongformerSelfAttention(config, layer_id=layer_id)
        self.output = nn.Linear(self.embed_dim, self.embed_dim)

    def forward(
        self,
        hidden_states: Tensor, # shape (batch_size, q_len, model_size)
        key_value_states: Optional[Tensor] = None, # cross-attention in transformers.models.bart.modeling_bart
        past_key_value: Optional[Tuple[Tensor]] = None, # only for decoder
        attention_mask: Optional[Tensor] = None, # shape (batch_size, k_len) -> changed in transformers.models.modeling_bart.BartEncoder and BartEncoderLayer (new mask uses bool -> global attention positions are lost, need to use the inverted orignal mask
        layer_head_mask: Optional[Tensor] = None, # head dropout?
        output_attentions: bool = False
    ) -> Tuple[Tensor, Optional[Tensor]]:

        bsz, tgt_len, embed_dim = hidden_states.size()
        assert embed_dim == self.embed_dim
        assert list(hidden_states.size()) == [bsz, tgt_len, embed_dim]

        outputs = self.longformer_self_attn(
            hidden_states,
            attention_mask=attention_mask * -1, # shape (batch_size, 1, 1, key_len)
            head_mask=None,
            encoder_hidden_states=None,
            encoder_attention_mask=None,
            output_attentions=output_attentions,
        )

        ## new: Bart encoder expects shape (seq_len, bsz, embed_dim), no transpose needed
        attn_output = self.output(outputs[0])
        # new return in BartAttention has attn_output, attn_weights_reshaped, past_key_value (only for decoder), need to return 3 values (None for past_key_value)
        return (attn_output, outputs[1:] ,None) if len(outputs) == 2 else (attn_output, None, None)


class LongBartEncoder(BartEncoder):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    [`BartEncoderLayer`].

    Args:
        config: BartConfig
        embed_tokens (nn.Embedding): output embedding
    """

    def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)

        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop

        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_encoder_position_embeddings
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

        self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

        if embed_tokens is not None:
            self.embed_tokens.weight = embed_tokens.weight

        self.embed_positions = BartLearnedPositionalEmbedding(
            self.max_source_positions,
            embed_dim,
        )
        self.layers = nn.ModuleList([LongBartEncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(embed_dim)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input = input_ids
            input_ids = input_ids.view(-1, input_ids.shape[-1])
        elif inputs_embeds is not None:
            input = inputs_embeds[:, :, -1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        embed_pos = self.embed_positions(input)
        embed_pos = embed_pos.to(inputs_embeds.device)

        hidden_states = inputs_embeds + embed_pos
        hidden_states = self.layernorm_embedding(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        # expand attention_mask
        longformer_attention_mask = None
        if attention_mask is not None:
            # need to return original, inverted mask for longformer attention, else value for global attention (=2 in given mask, will be -1) is lost
            longformer_attention_mask = 1 - attention_mask
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)


        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
                    f" {head_mask.size()[0]}."
                )

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # 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
                layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training:

                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

                    layer_outputs = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(encoder_layer),
                        hidden_states,
                        attention_mask,
                        longformer_attention_mask,
                        (head_mask[idx] if head_mask is not None else None),
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        attention_mask,
                        longformer_attention_mask,
                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                        output_attentions=output_attentions,
                    )

                hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
        )


class LongBartModel(BartModel):
    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 = LongBartEncoder(config, self.shared)
        self.decoder = BartDecoder(config, self.shared)

        # Initialize weights and apply final processing
        self.post_init()


class LongBartEncoderLayer(BartEncoderLayer):
    def __init__(self, config: BartConfig):
        super().__init__(config)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: torch.FloatTensor,
        longformer_attention_mask: torch.Tensor,
        layer_head_mask: torch.FloatTensor,
        output_attentions: bool = False,
    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
         # if longformer attention instead of bart self attention: use special mask
        if isinstance(self.self_attn, LongformerSelfAttentionForBart):
            attention_mask = longformer_attention_mask
        residual = hidden_states
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )

        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        if hidden_states.dtype == torch.float16 and (
            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs