longformer_enc_dec_custom.py

"""

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 transformers.models.mbart.modeling_mbart import MBartConfig, MBartForConditionalGeneration, MBartEncoder, MBartLearnedPositionalEmbedding, MBartEncoderLayer, MBartDecoder, MBartModel, _expand_mask
from transformers.modeling_outputs import BaseModelOutput,Seq2SeqModelOutput
from transformers.configuration_utils import PretrainedConfig
from transformers import GPT2Model, GPT2Config, AutoModelForCausalLM,AutoConfig
from transformers.activations import ACT2FN

import torch.nn.functional as F
from transformers.models.roberta.modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM

from functools import lru_cache
import os.path


class MLongformerEncoderDecoderForConditionalGenerationCustom(MBartForConditionalGeneration):
    def __init__(self, config):
        super(MBartForConditionalGeneration, self).__init__(config)
        self.decoder_config = GPT2Config.from_dict(config.decoder_config)
        self.decoder_config.add_cross_attention=True
        self.config.eos_token_id = self.decoder_config.eos_token_id
        #self.config.bos_token_id = 0

        self.model = LongMBartModelCustom(config)
        #self.register_buffer("final_logits_bias", torch.zeros((1, self.decoder_config.vocab_size)))

        if self.config.from_mbart:
            self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
            self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
        else:
            self.lm_head = nn.Linear(self.decoder_config.n_embd, self.decoder_config.vocab_size, bias=False)
            self.register_buffer("final_logits_bias", torch.zeros((1, self.decoder_config.vocab_size)))

        self.model.decoder = GPT2Model(self.decoder_config)
        if config.attention_mode == 'n2':
            pass  # do nothing, use MBartSelfAttention instead
        else:
            for i, layer in enumerate(self.model.encoder.layers):
                layer.self_attn = LongformerSelfAttentionForMBart(config, layer_id=i)
        # Initialize weights and apply final processing
        self.post_init()

    def post_init(self):
        super().post_init()
        if not self.config.from_mbart:
            self.lm_head = nn.Linear(self.decoder_config.n_embd, self.decoder_config.vocab_size, bias=False)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (MBartDecoder)):
            module.gradient_checkpointing = value
        self.model.decoder._set_gradient_checkpointing(module, value=value)

    @classmethod
    def from_encoder_decoder_pretrained(
        cls,
        mbart_pretrained_model_name_or_path: str = None,
        decoder_pretrained_model_name_or_path: str = None,
        *model_args,
        **kwargs
    ) -> MBartForConditionalGeneration:
        config = MLongformerEncoderDecoderConfigCustom.from_pretrained(mbart_pretrained_model_name_or_path)
        config.from_mbart = True
        config.tie_word_embeddings = False
        config.decoder_config = GPT2Config.from_pretrained(decoder_pretrained_model_name_or_path).to_dict()

        mbart = super().from_pretrained(mbart_pretrained_model_name_or_path, config=config)
        decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, add_cross_attention=True)

        mbart.model.decoder = decoder.transformer
        mbart.lm_head = decoder.lm_head
        mbart.register_buffer("final_logits_bias", torch.zeros((1, decoder.config.vocab_size)))

        #reinit cross attention layers
        mbart.model.enc_to_dec_proj.apply(mbart.model._init_weights)
        for layer in mbart.model.decoder.h:
            layer.crossattention.c_attn.apply(mbart.model.decoder._init_weights)

        del mbart.model.shared
        return mbart


class MLongformerEncoderDecoderConfigCustom(MBartConfig):
    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.from_mbart = False
        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 ['sliding_chunks', 'n2']


class LongMBartModelCustom(MBartModel):
    def __init__(self, config: MBartConfig):
        super().__init__(config)
        del self.shared
        decoder_config = GPT2Config.from_dict(config.decoder_config)

        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        if self.config.from_mbart:
            self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

        self.encoder = LongMBartEncoder(config)
        self.enc_to_dec_proj = torch.nn.Linear(config.d_model, decoder_config.n_embd)
        self.act = ACT2FN[decoder_config.activation_function]
        self.decoder = GPT2Model(decoder_config)

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

    def get_input_embeddings(self):
        return self.encoder.embed_tokens

    def set_input_embeddings(self, value):
        self.encoder.embed_tokens = value

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        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

        # different to other models, MBart automatically creates decoder_input_ids from
        # input_ids if no decoder_input_ids are provided
        if decoder_input_ids is None and decoder_inputs_embeds is None:
            decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)

        #print("input_ids: ", input_ids)
        #print("input_embeds: ", inputs_embeds)
        #print("decoder_input_ids: ", decoder_input_ids.shape)
        #print("attention_mask: ",attention_mask.shape)

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
                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 BaseModelOutput when return_dict=True
        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,
            )

        encoder_hidden_states = encoder_outputs[0]

        #remove uneccessary padding spaces
        non_empty_mask = attention_mask.abs().sum(dim=0).bool()
        encoder_hidden_states = encoder_hidden_states[:,non_empty_mask]
        encoder_attention_mask = attention_mask[:,non_empty_mask]

        #to remove global attention tokens (2)
        encoder_attention_mask = torch.clamp(encoder_attention_mask, min=0, max=1)

        encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
        encoder_hidden_states = self.act(encoder_hidden_states)
        encoder_hidden_states = torch.nn.Dropout(p=0.1)(encoder_hidden_states)

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=decoder_head_mask,
            #cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            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,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )
        
class MLongformerEncoderDecoderForConditionalGeneration(MBartForConditionalGeneration):
    def __init__(self, config):
        super(MBartForConditionalGeneration, self).__init__(config)

        self.model = LongMBartModel(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 MBartSelfAttention instead
        else:
            for i, layer in enumerate(self.model.encoder.layers):
                layer.self_attn = LongformerSelfAttentionForMBart(config, layer_id=i)
        # Initialize weights and apply final processing
        self.post_init()


class MLongformerEncoderDecoderConfig(MBartConfig):
    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 ['sliding_chunks', 'n2']

class LongformerSelfAttentionForMBart(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.mbart.modeling_mbart
        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_mbart.MBartEncoder and MBartEncoderLayer (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: MBart encoder expects shape (seq_len, bsz, embed_dim), no transpose needed
        attn_output = self.output(outputs[0])
        # new return in MBartAttention 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 LongMBartEncoder(MBartEncoder):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    [`MBartEncoderLayer`].

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

    def __init__(self, config: MBartConfig, 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

        if embed_tokens is not None:
            self.embed_tokens = embed_tokens
        else:
            self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

        self.embed_positions = MBartLearnedPositionalEmbedding(
            self.max_source_positions,
            embed_dim,
        )
        self.layers = nn.ModuleList([LongMBartEncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(embed_dim)
        self.layer_norm = nn.LayerNorm(config.d_model)

        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 [`MBartTokenizer`]. 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_shape = input.shape
            input_ids = input_ids.view(-1, input_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)

        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],)

        hidden_states = self.layer_norm(hidden_states)
        #print("Encoder output: ",hidden_states.shape)

        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 LongMBartModel(MBartModel):
    def __init__(self, config: MBartConfig):
        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 = LongMBartEncoder(config, self.shared)
        self.decoder = MBartDecoder(config, self.shared)

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

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Seq2SeqModelOutput, Tuple[torch.FloatTensor]]:
        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

        # different to other models, MBart automatically creates decoder_input_ids from
        # input_ids if no decoder_input_ids are provided
        if decoder_input_ids is None and decoder_inputs_embeds is None:
            decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
                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 BaseModelOutput when return_dict=True
        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, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            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,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

class LongMBartEncoderLayer(MBartEncoderLayer):
    def __init__(self, config: MBartConfig):
        super().__init__(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        longformer_attention_mask: torch.Tensor,
        layer_head_mask: torch.Tensor,
        output_attentions: bool = False,
    ) -> torch.Tensor:
        """
        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.
            longformer_attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                `(batch, src_len)` where 0=local, -1=global, 1=padding.
            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 mbart self attention: use special mask
        if isinstance(self.self_attn, LongformerSelfAttentionForMBart):
            attention_mask = longformer_attention_mask
        residual = hidden_states
        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

        residual = hidden_states
        hidden_states = self.final_layer_norm(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

        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
        
class Longformer(RobertaModel):
    def __init__(self, config):
        super(Longformer, self).__init__(config)
        if config.attention_mode == 'n2':
            pass  # do nothing, use BertSelfAttention instead
        else:
            for i, layer in enumerate(self.encoder.layer):
                layer.attention.self = LongformerSelfAttention(config, layer_id=i)


class LongformerForMaskedLM(RobertaForMaskedLM):
    def __init__(self, config):
        super(LongformerForMaskedLM, self).__init__(config)
        if config.attention_mode == 'n2':
            pass  # do nothing, use BertSelfAttention instead
        else:
            for i, layer in enumerate(self.roberta.encoder.layer):
                layer.attention.self = LongformerSelfAttention(config, layer_id=i)


class LongformerConfig(RobertaConfig):
    def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
                 autoregressive: bool = False, attention_mode: str = 'sliding_chunks', **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
        assert self.attention_mode in ['sliding_chunks', 'n2', 'sliding_chunks_no_overlap']


class LongformerSelfAttention(nn.Module):
    def __init__(self, config, layer_id):
        super(LongformerSelfAttention, self).__init__()
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
        self.num_heads = config.num_attention_heads
        self.head_dim = int(config.hidden_size / config.num_attention_heads)
        self.embed_dim = config.hidden_size

        self.query = nn.Linear(config.hidden_size, self.embed_dim)
        self.key = nn.Linear(config.hidden_size, self.embed_dim)
        self.value = nn.Linear(config.hidden_size, self.embed_dim)

        self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
        self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
        self.value_global = nn.Linear(config.hidden_size, self.embed_dim)

        self.dropout = config.attention_probs_dropout_prob

        self.layer_id = layer_id
        self.attention_window = config.attention_window[self.layer_id]
        self.attention_dilation = config.attention_dilation[self.layer_id]
        self.attention_mode = config.attention_mode
        self.autoregressive = config.autoregressive
        assert self.attention_window > 0
        assert self.attention_dilation > 0
        assert self.attention_mode in ['sliding_chunks', 'sliding_chunks_no_overlap']
        if self.attention_mode in ['sliding_chunks', 'sliding_chunks_no_overlap']:
            assert not self.autoregressive  # not supported
            assert self.attention_dilation == 1  # dilation is not supported

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=False,
    ):
        '''
        The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
            -ve: no attention
              0: local attention
            +ve: global attention
        '''
        assert encoder_hidden_states is None, "`encoder_hidden_states` is not supported and should be None"
        assert encoder_attention_mask is None, "`encoder_attention_mask` is not supported and should be None"

        if attention_mask is not None:
            key_padding_mask = attention_mask < 0
            extra_attention_mask = attention_mask > 0
            remove_from_windowed_attention_mask = attention_mask != 0

            num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
            max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
            if max_num_extra_indices_per_batch <= 0:
                extra_attention_mask = None
            else:
                # To support the case of variable number of global attention in the rows of a batch,
                # we use the following three selection masks to select global attention embeddings
                # in a 3d tensor and pad it to `max_num_extra_indices_per_batch`
                # 1) selecting embeddings that correspond to global attention
                extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
                zero_to_max_range = torch.arange(0, max_num_extra_indices_per_batch,
                                                 device=num_extra_indices_per_batch.device)
                # mask indicating which values are actually going to be padding
                selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
                # 2) location of the non-padding values in the selected global attention
                selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
                # 3) location of the padding values in the selected global attention
                selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
        else:
            remove_from_windowed_attention_mask = None
            extra_attention_mask = None
            key_padding_mask = None

        hidden_states = hidden_states.transpose(0, 1)
        seq_len, bsz, embed_dim = hidden_states.size()
        assert embed_dim == self.embed_dim
        q = self.query(hidden_states)
        k = self.key(hidden_states)
        v = self.value(hidden_states)
        q /= math.sqrt(self.head_dim)

        q = q.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
        k = k.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
        # attn_weights = (bsz, seq_len, num_heads, window*2+1)
        if self.attention_mode == "sliding_chunks":
            attn_weights = sliding_chunks_matmul_qk(q, k, self.attention_window, padding_value=0)
        elif self.attention_mode == "sliding_chunks_no_overlap":
            attn_weights = sliding_chunks_no_overlap_matmul_qk(q, k, self.attention_window, padding_value=0)
        else:
            raise False
        mask_invalid_locations(attn_weights, self.attention_window, self.attention_dilation, False)
        if remove_from_windowed_attention_mask is not None:
            # This implementation is fast and takes very little memory because num_heads x hidden_size = 1
            # from (bsz x seq_len) to (bsz x seq_len x num_heads x hidden_size)
            remove_from_windowed_attention_mask = remove_from_windowed_attention_mask.unsqueeze(dim=-1).unsqueeze(dim=-1)
            # cast to float/half then replace 1's with -inf
            float_mask = remove_from_windowed_attention_mask.type_as(q).masked_fill(remove_from_windowed_attention_mask, -10000.0)
            repeat_size = 1 if isinstance(self.attention_dilation, int) else len(self.attention_dilation)
            float_mask = float_mask.repeat(1, 1, repeat_size, 1)
            ones = float_mask.new_ones(size=float_mask.size())  # tensor of ones
            # diagonal mask with zeros everywhere and -inf inplace of padding
            if self.attention_mode == "sliding_chunks":
                d_mask = sliding_chunks_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)
            elif self.attention_mode == "sliding_chunks_no_overlap":
                d_mask = sliding_chunks_no_overlap_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)

            attn_weights += d_mask
        assert list(attn_weights.size())[:3] == [bsz, seq_len, self.num_heads]
        assert attn_weights.size(dim=3) in [self.attention_window * 2 + 1, self.attention_window * 3]

        # the extra attention
        if extra_attention_mask is not None:
            selected_k = k.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
            selected_k[selection_padding_mask_nonzeros] = k[extra_attention_mask_nonzeros]
            # (bsz, seq_len, num_heads, max_num_extra_indices_per_batch)
            selected_attn_weights = torch.einsum('blhd,bshd->blhs', (q, selected_k))
            selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
            # concat to attn_weights
            # (bsz, seq_len, num_heads, extra attention count + 2*window+1)
            attn_weights = torch.cat((selected_attn_weights, attn_weights), dim=-1)
        attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
        if key_padding_mask is not None:
            # softmax sometimes inserts NaN if all positions are masked, replace them with 0
            attn_weights_float = torch.masked_fill(attn_weights_float, key_padding_mask.unsqueeze(-1).unsqueeze(-1), 0.0)
        attn_weights = attn_weights_float.type_as(attn_weights)
        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
        v = v.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
        attn = 0
        if extra_attention_mask is not None:
            selected_attn_probs = attn_probs.narrow(-1, 0, max_num_extra_indices_per_batch)
            selected_v = v.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
            selected_v[selection_padding_mask_nonzeros] = v[extra_attention_mask_nonzeros]
            # use `matmul` because `einsum` crashes sometimes with fp16
            # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
            attn = torch.matmul(selected_attn_probs.transpose(1, 2), selected_v.transpose(1, 2).type_as(selected_attn_probs)).transpose(1, 2)
            attn_probs = attn_probs.narrow(-1, max_num_extra_indices_per_batch, attn_probs.size(-1) - max_num_extra_indices_per_batch).contiguous()

        if self.attention_mode == "sliding_chunks":
            attn += sliding_chunks_matmul_pv(attn_probs, v, self.attention_window)
        elif self.attention_mode == "sliding_chunks_no_overlap":
            attn += sliding_chunks_no_overlap_matmul_pv(attn_probs, v, self.attention_window)
        else:
            raise False

        attn = attn.type_as(hidden_states)
        assert list(attn.size()) == [bsz, seq_len, self.num_heads, self.head_dim]
        attn = attn.transpose(0, 1).reshape(seq_len, bsz, embed_dim).contiguous()

        # For this case, we'll just recompute the attention for these indices
        # and overwrite the attn tensor. TODO: remove the redundant computation
        if extra_attention_mask is not None:
            selected_hidden_states = hidden_states.new_zeros(max_num_extra_indices_per_batch, bsz, embed_dim)
            selected_hidden_states[selection_padding_mask_nonzeros[::-1]] = hidden_states[extra_attention_mask_nonzeros[::-1]]

            q = self.query_global(selected_hidden_states)
            k = self.key_global(hidden_states)
            v = self.value_global(hidden_states)
            q /= math.sqrt(self.head_dim)

            q = q.contiguous().view(max_num_extra_indices_per_batch, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # (bsz*self.num_heads, max_num_extra_indices_per_batch, head_dim)
            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # bsz * self.num_heads, seq_len, head_dim)
            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # bsz * self.num_heads, seq_len, head_dim)
            attn_weights = torch.bmm(q, k.transpose(1, 2))
            assert list(attn_weights.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len]

            attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
            attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
            if key_padding_mask is not None:
                attn_weights = attn_weights.masked_fill(
                    key_padding_mask.unsqueeze(1).unsqueeze(2),
                    -10000.0,
                )
            attn_weights = attn_weights.view(bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len)
            attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
            attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
            selected_attn = torch.bmm(attn_probs, v)
            assert list(selected_attn.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, self.head_dim]

            selected_attn_4d = selected_attn.view(bsz, self.num_heads, max_num_extra_indices_per_batch, self.head_dim)
            nonzero_selected_attn = selected_attn_4d[selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]]
            attn[extra_attention_mask_nonzeros[::-1]] = nonzero_selected_attn.view(len(selection_padding_mask_nonzeros[0]), -1).type_as(hidden_states)

        context_layer = attn.transpose(0, 1) # attn shape: (seq_len, bsz, embed_dim), context_layer shape: (bsz, seq_len, embed_dim)
        if output_attentions:
            if extra_attention_mask is not None:
                # With global attention, return global attention probabilities only
                # batch_size x num_heads x max_num_global_attention_tokens x sequence_length
                # which is the attention weights from tokens with global attention to all tokens
                # It doesn't not return local attention
                # In case of variable number of global attantion in the rows of a batch,
                # attn_weights are padded with -10000.0 attention scores
                attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
            else:
                # without global attention, return local attention probabilities
                # batch_size x num_heads x sequence_length x window_size
                # which is the attention weights of every token attending to its neighbours
                attn_weights = attn_weights.permute(0, 2, 1, 3)
        outputs = (context_layer, attn_weights) if output_attentions else (context_layer,)
        return outputs
        
def _skew(x, direction, padding_value):
    '''Convert diagonals into columns (or columns into diagonals depending on `direction`'''
    x_padded = F.pad(x, direction, value=padding_value)
    x_padded = x_padded.view(*x_padded.size()[:-2], x_padded.size(-1), x_padded.size(-2))
    return x_padded


def _skew2(x, padding_value):
    '''shift every row 1 step to right converting columns into diagonals'''
    # X = B x C x M x L
    B, C, M, L = x.size()
    x = F.pad(x, (0, M + 1), value=padding_value)  # B x C x M x (L+M+1)
    x = x.view(B, C, -1)  # B x C x ML+MM+M
    x = x[:, :, :-M]  # B x C x ML+MM
    x = x.view(B, C, M, M + L)  # B x C, M x L+M
    x = x[:, :, :, :-1]
    return x


def _chunk(x, w):
    '''convert into overlapping chunkings. Chunk size = 2w, overlap size = w'''

    # non-overlapping chunks of size = 2w
    x = x.view(x.size(0), x.size(1) // (w * 2), w * 2, x.size(2))

    # use `as_strided` to make the chunks overlap with an overlap size = w
    chunk_size = list(x.size())
    chunk_size[1] = chunk_size[1] * 2 - 1

    chunk_stride = list(x.stride())
    chunk_stride[1] = chunk_stride[1] // 2
    return x.as_strided(size=chunk_size, stride=chunk_stride)


def sliding_chunks_matmul_qk(q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float):
    '''Matrix multiplicatio of query x key tensors using with a sliding window attention pattern.
    This implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer)
    with an overlap of size w'''
    bsz, seqlen, num_heads, head_dim = q.size()
    assert seqlen % (w * 2) == 0
    assert q.size() == k.size()

    chunks_count = seqlen // w - 1

    # group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size w * 2
    q = q.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)
    k = k.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)

    chunk_q = _chunk(q, w)
    chunk_k = _chunk(k, w)

    # matrix multipication
    # bcxd: bsz*num_heads x chunks x 2w x head_dim
    # bcyd: bsz*num_heads x chunks x 2w x head_dim
    # bcxy: bsz*num_heads x chunks x 2w x 2w
    chunk_attn = torch.einsum('bcxd,bcyd->bcxy', (chunk_q, chunk_k))  # multiply

    # convert diagonals into columns
    diagonal_chunk_attn = _skew(chunk_attn, direction=(0, 0, 0, 1), padding_value=padding_value)

    # allocate space for the overall attention matrix where the chunks are compined. The last dimension
    # has (w * 2 + 1) columns. The first (w) columns are the w lower triangles (attention from a word to
    # w previous words). The following column is attention score from each word to itself, then
    # followed by w columns for the upper triangle.

    diagonal_attn = diagonal_chunk_attn.new_empty((bsz * num_heads, chunks_count + 1, w, w * 2 + 1))

    # copy parts from diagonal_chunk_attn into the compined matrix of attentions
    # - copying the main diagonal and the upper triangle
    diagonal_attn[:, :-1, :, w:] = diagonal_chunk_attn[:, :, :w, :w + 1]
    diagonal_attn[:, -1, :, w:] = diagonal_chunk_attn[:, -1, w:, :w + 1]
    # - copying the lower triangle
    diagonal_attn[:, 1:, :, :w] = diagonal_chunk_attn[:, :, - (w + 1):-1, w + 1:]
    diagonal_attn[:, 0, 1:w, 1:w] = diagonal_chunk_attn[:, 0, :w - 1, 1 - w:]

    # separate bsz and num_heads dimensions again
    diagonal_attn = diagonal_attn.view(bsz, num_heads, seqlen, 2 * w + 1).transpose(2, 1)

    mask_invalid_locations(diagonal_attn, w, 1, False)
    return diagonal_attn


def sliding_chunks_matmul_pv(prob: torch.Tensor, v: torch.Tensor, w: int):
    '''Same as sliding_chunks_matmul_qk but for prob and value tensors. It is expecting the same output
    format from sliding_chunks_matmul_qk'''
    bsz, seqlen, num_heads, head_dim = v.size()
    assert seqlen % (w * 2) == 0
    assert prob.size()[:3] == v.size()[:3]
    assert prob.size(3) == 2 * w + 1
    chunks_count = seqlen // w - 1
    # group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size 2w
    chunk_prob = prob.transpose(1, 2).reshape(bsz * num_heads, seqlen // w, w, 2 * w + 1)

    # group bsz and num_heads dimensions into one
    v = v.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)

    # pad seqlen with w at the beginning of the sequence and another w at the end
    padded_v = F.pad(v, (0, 0, w, w), value=-1)

    # chunk padded_v into chunks of size 3w and an overlap of size w
    chunk_v_size = (bsz * num_heads, chunks_count + 1, 3 * w, head_dim)
    chunk_v_stride = padded_v.stride()
    chunk_v_stride = chunk_v_stride[0], w * chunk_v_stride[1], chunk_v_stride[1], chunk_v_stride[2]
    chunk_v = padded_v.as_strided(size=chunk_v_size, stride=chunk_v_stride)

    skewed_prob = _skew2(chunk_prob, padding_value=0)

    context = torch.einsum('bcwd,bcdh->bcwh', (skewed_prob, chunk_v))
    return context.view(bsz, num_heads, seqlen, head_dim).transpose(1, 2)


def pad_to_window_size(input_ids: torch.Tensor, attention_mask: torch.Tensor,
                       one_sided_window_size: int, pad_token_id: int):
    '''A helper function to pad tokens and mask to work with the sliding_chunks implementation of Longformer selfattention.
    Input:
        input_ids = torch.Tensor(bsz x seqlen): ids of wordpieces
        attention_mask = torch.Tensor(bsz x seqlen): attention mask
        one_sided_window_size = int: window size on one side of each token
        pad_token_id = int: tokenizer.pad_token_id
    Returns
        (input_ids, attention_mask) padded to length divisible by 2 * one_sided_window_size
    '''
    w = int(2 * one_sided_window_size)
    seqlen = input_ids.size(1)
    padding_len = (w - seqlen % w) % w
    input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
    attention_mask = F.pad(attention_mask, (0, padding_len), value=False)  # no attention on the padding tokens
    return input_ids, attention_mask


# ========= "sliding_chunks_no_overlap": alternative implemenation of the sliding window attention =========
# This implementation uses non-overlapping chunks (or blocks) of size `w` with number of local attention = 3xw
# To make this implemenation comparable to "sliding_chunks" set w such that
#       w_of_sliding_chunks_no_overlap = w_of_sliding_chunks * 2 / 3
# For example,
#    w_of_sliding_chunks = 256 (this is one sided. Total attention size = 512)
#    w_of_sliding_chunks_no_overlap = 170 (Total attention size = 510)
# Performance:
# - Speed: 30% faster than "sliding_chunks"
# - Memory: 95% of the memory usage of "sliding_chunks"
# The windows are asymmetric where number of attention on each side of a token ranges between w to 2w
# while "sliding_chunks" has a symmetric window around each token.


def sliding_chunks_no_overlap_matmul_qk(q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float):
    bsz, seqlen, num_heads, head_dim = q.size()
    assert seqlen % w == 0
    assert q.size() == k.size()
    # chunk seqlen into non-overlapping chunks of size w
    chunk_q = q.view(bsz, seqlen // w, w, num_heads, head_dim)
    chunk_k = k.view(bsz, seqlen // w, w, num_heads, head_dim)
    chunk_k_expanded = torch.stack((
        F.pad(chunk_k[:, :-1], (0, 0, 0, 0, 0, 0, 1, 0), value=0.0),
        chunk_k,
        F.pad(chunk_k[:, 1:], (0, 0, 0, 0, 0, 0, 0, 1), value=0.0),
    ), dim=-1)
    diagonal_attn = torch.einsum('bcxhd,bcyhde->bcxhey', (chunk_q, chunk_k_expanded))  # multiply
    return diagonal_attn.reshape(bsz, seqlen, num_heads, 3 * w)


def sliding_chunks_no_overlap_matmul_pv(prob: torch.Tensor, v: torch.Tensor, w: int):
    bsz, seqlen, num_heads, head_dim = v.size()
    chunk_prob = prob.view(bsz, seqlen // w, w, num_heads, 3, w)
    chunk_v = v.view(bsz, seqlen // w, w, num_heads, head_dim)
    chunk_v_extended = torch.stack((
        F.pad(chunk_v[:, :-1], (0, 0, 0, 0, 0, 0, 1, 0), value=0.0),
        chunk_v,
        F.pad(chunk_v[:, 1:], (0, 0, 0, 0, 0, 0, 0, 1), value=0.0),
    ), dim=-1)
    context = torch.einsum('bcwhpd,bcdhep->bcwhe', (chunk_prob, chunk_v_extended))
    return context.reshape(bsz, seqlen, num_heads, head_dim)

def _get_invalid_locations_mask_fixed_dilation(seq_len: int, w: int, d: int):
    diagonals_list = []
    for j in range(-d * w, d, d):
        diagonal_mask = torch.zeros(seq_len, device='cpu', dtype=torch.uint8)
        diagonal_mask[:-j] = 1
        diagonals_list.append(diagonal_mask)
    return torch.stack(diagonals_list, dim=-1)

@lru_cache()
def _get_invalid_locations_mask(w: int, d: Union[torch.Tensor,int], autoregressive: bool, device: str):
    if isinstance(d, int):
        affected_seq_len = w * d
        mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
        mask = mask[None, :, None, :]
    else:
        affected_seq_len = w * d.max()
        head_masks = []
        d_list = d.cpu().numpy().tolist()
        for d in d_list:
            one_head_mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
            head_masks.append(one_head_mask)
        mask = torch.stack(head_masks, dim=-2)
        mask = mask[None, :, :, :]

    ending_mask = None if autoregressive else mask.flip(dims=(1, 3)).bool().to(device)
    return affected_seq_len, mask.bool().to(device), ending_mask

def mask_invalid_locations(input_tensor: torch.Tensor, w: int, d: Union[torch.Tensor, int], autoregressive: bool) -> torch.Tensor:
    affected_seq_len, beginning_mask, ending_mask = _get_invalid_locations_mask(w, d, autoregressive, input_tensor.device)
    seq_len = input_tensor.size(1)
    beginning_input = input_tensor[:, :affected_seq_len, :, :w+1]
    beginning_mask = beginning_mask[:, :seq_len].expand(beginning_input.size())
    beginning_input.masked_fill_(beginning_mask, -float('inf'))
    if not autoregressive:
        ending_input = input_tensor[:, -affected_seq_len:, :, -(w+1):]
        ending_mask = ending_mask[:, -seq_len:].expand(ending_input.size())
        ending_input.masked_fill_(ending_mask, -float('inf'))