modeling_lsg_bart.py

from logging import warn
import torch
from transformers.models.bart.modeling_bart import *
from transformers.models.bart.modeling_bart import _expand_mask
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss
import sys

AUTO_MAP = {
        "AutoModel": "modeling_lsg_bart.LSGBartModel",
        "AutoModelForCausalLM": "modeling_lsg_bart.LSGBartForCausalLM",
        "AutoModelForQuestionAnswering": "modeling_lsg_bart.LSGBartForQuestionAnswering",
        "AutoModelForSequenceClassification": "modeling_lsg_bart.LSGBartForSequenceClassification",
        "AutoModelForSeq2SeqLM": "modeling_lsg_bart.LSGBartForConditionalGeneration"
    }

class LSGBartConfig(BartConfig):
    """
    This class overrides :class:`~transformers.RobertaConfig`. Please check the superclass for the appropriate
    documentation alongside usage examples.
    """

    base_model_prefix = "lsg"
    model_type = "bart"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(
        self,
        adaptive=True,
        base_model_prefix="lsg",
        block_size=128,
        lsh_num_pre_rounds=1,
        num_global_tokens=1,
        pass_global_tokens_to_decoder=True,
        pool_with_global=True,
        sparse_block_size=128,
        sparsity_factor=2,
        sparsity_type="norm",
        **kwargs
        ):
        """Constructs LSGConfig."""
        super().__init__(**kwargs)
        
        self.adaptive = adaptive
        self.auto_map = AUTO_MAP
        self.base_model_prefix = base_model_prefix
        self.block_size = block_size
        self.lsh_num_pre_rounds = lsh_num_pre_rounds
        self.num_global_tokens = num_global_tokens
        self.pass_global_tokens_to_decoder = pass_global_tokens_to_decoder
        self.pool_with_global = pool_with_global
        self.sparse_block_size = sparse_block_size
        self.sparsity_factor = sparsity_factor
        self.sparsity_type = sparsity_type

        if sparsity_type not in [None, "none", "norm", "lsh", "pooling", "stride"]:
            logger.warning(
                "[WARNING CONFIG]: sparsity_mode not in [None, 'none', 'norm', 'lsh', 'pooling', 'stride'], setting sparsity_type=None, computation will skip sparse attention")
            self.sparsity_type = None

        if self.sparsity_type == "stride":
            if self.sparsity_factor > self.encoder_attention_heads:
                logger.warning(
                "[WARNING CONFIG]: sparsity_factor > encoder_attention_heads is not recommended for stride sparsity"
            )
        
        if self.num_global_tokens < 1:
            logger.warning(
                "[WARNING CONFIG]: num_global_tokens < 1 is not compatible, setting num_global_tokens=1"
            )
            self.num_global_tokens = 1
        elif self.num_global_tokens > 512:
            logger.warning(
                "[WARNING CONFIG]: num_global_tokens > 512 is not compatible, setting num_global_tokens=512"
            )
            self.num_global_tokens = 512
        
        if self.sparsity_factor > 0:
            assert self.block_size % self.sparsity_factor == 0, "[ERROR CONFIG]: block_size must be divisible by sparsity_factor"
            assert self.block_size//self.sparsity_factor >= 1, "[ERROR CONFIG]: make sure block_size >= sparsity_factor"
            
        
def shift_tokens_right(input_ids, pad_token_id, decoder_start_token_id):
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("self.model.config.pad_token_id has to be defined.")
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

    return shifted_input_ids


def _make_causal_mask(input_ids_shape, dtype, past_key_values_length=0):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), float("-inf"))
    mask_cond = torch.arange(mask.size(-1))
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


def _expand_mask(mask, dtype, tgt_len=None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)


class BaseSelfAttention(nn.Module):

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        is_decoder=False,
        bias=True,
        ):

        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim ** -0.5
        self.is_decoder = is_decoder

        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)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_heads,
            self.head_dim,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def reshape_output(self, context_layer):
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
        return context_layer.view(*new_context_layer_shape)

    def project_QKV(self, hidden_states):

        query_layer = self.transpose_for_scores(self.q_proj(hidden_states))
        key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
        value_layer = self.transpose_for_scores(self.v_proj(hidden_states))
        return query_layer, key_layer, value_layer

    
class BaseAttentionProduct(nn.Module):

    def __init__(self, config):
        """
        Compute attention: softmax(Q @ K.T) @ V
        """
        super().__init__()
        self.dropout = nn.Dropout(config.attention_dropout)

    def forward(self, query_layer, key_layer, value_layer, attention_mask=None):
        
        d = query_layer.shape[-1]

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = query_layer @ key_layer.transpose(-1, -2) / math.sqrt(d)

        del query_layer
        del key_layer

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
            attention_scores = attention_scores + attention_mask
            del attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        context_layer = self.dropout(attention_probs) @ value_layer

        return context_layer


class LSGAttentionProduct(nn.Module):

    def __init__(self, config, block_size=None, sparse_block_size=None, sparsity_factor=4):
        """
        Compute block or overlapping blocks attention products
        """
        super().__init__()
 
        self.block_size = block_size
        self.sparse_block_size = sparse_block_size
        self.sparsity_factor = sparsity_factor

        if self.block_size is None:
            self.block_size = config.block_size

        if self.sparse_block_size is None:
            self.sparse_block_size = config.sparse_block_size

        # Shape of blocks
        self.local_shapes = (self.block_size*3, self.block_size)
        if self.sparse_block_size and self.sparsity_factor > 0:
            self.sparse_shapes = (self.sparse_block_size*3, self.block_size//self.sparsity_factor)

        self.attention = BaseAttentionProduct(config)
        
    def build_lsg_inputs(self, hidden_states, sparse_hidden_states, global_hidden_states, is_attn_mask=False):
        
        # Build local tokens
        local_hidden_states = self.reshape_to_local_block(hidden_states, is_attn_mask)
        del hidden_states

        # Build sparse tokens
        if sparse_hidden_states is not None:
            sparse_hidden_states = self.reshape_to_sparse_block(sparse_hidden_states, is_attn_mask)
        
        return self.cat_global_sparse_local_tokens(global_hidden_states, sparse_hidden_states, local_hidden_states)

    def forward(
        self, 
        query_layer, 
        key_layer, 
        value_layer, 
        attention_mask=None, 
        sparse_key=None,
        sparse_value=None, 
        sparse_mask=None, 
        global_key=None, 
        global_value=None, 
        global_mask=None
        ):

        # Input batch, heads, length, hidden_size
        n, h, t, d = query_layer.size()
        n_blocks = t // self.block_size
        assert t % self.block_size == 0

        key_layer = self.build_lsg_inputs(
            key_layer, 
            sparse_key, 
            global_key
            )
        del sparse_key
        del global_key

        value_layer = self.build_lsg_inputs(
            value_layer, 
            sparse_value, 
            global_value
            )
        del sparse_value
        del global_value

        attention_mask = self.build_lsg_inputs(
            attention_mask, 
            sparse_mask, 
            global_mask.transpose(-1, -2), 
            is_attn_mask=True
            ).transpose(-1, -2)
        del sparse_mask
        del global_mask

        # expect (..., t, d) shape
        # Compute attention
        context_layer = self.attention(
                query_layer=self.chunk(query_layer, n_blocks), 
                key_layer=key_layer,
                value_layer=value_layer,
                attention_mask=attention_mask
                )
                
        return context_layer.reshape(n, h, -1, d)
    
    def reshape_to_local_block(self, hidden_states, is_attn_mask=False):
        
        size, step = self.local_shapes
        s = (size - step) // 2

        # Pad before block reshaping
        if is_attn_mask:
            pad_value = -10000  
            hidden_states = hidden_states.transpose(-1, -2)
        else: 
            pad_value = 0

        hidden_states = torch.nn.functional.pad(
            hidden_states.transpose(-1, -2), 
            pad=(s, s),
            value=pad_value
            ).transpose(-1, -2)

        # Make blocks
        hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2)

        return hidden_states

    def reshape_to_sparse_block(self, hidden_states, is_attn_mask=False):
        
        size, step = self.sparse_shapes

        # In case of odd case
        odd_offset = (step % 2)

        # n, h, t, d*2 + 1
        size = size*2 
        s = (size - step) // 2 + odd_offset

        # Pad before block reshaping
        if is_attn_mask:
            pad_value = -10000  
            hidden_states = hidden_states.transpose(-1, -2)
        else: 
            pad_value = 0

        hidden_states = torch.nn.functional.pad(
            hidden_states.transpose(-1, -2), 
            pad=(s, s),
            value=pad_value
            ).transpose(-1, -2)

        # Make blocks
        hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2)

        # Fix case where block_size == sparsify_factor
        if odd_offset: 
            hidden_states = hidden_states[..., :-1, :, :]

        # Indexes for selection
        u = (size - self.block_size * 3 // self.sparsity_factor) // 2 + odd_offset
        s = self.sparse_block_size

        u_ = u + odd_offset
        return torch.cat([hidden_states[..., u-s:u, :], hidden_states[..., -u_:-u_+s, :]], dim=-2)

    def cat_global_sparse_local_tokens(self, x_global, x_sparse=None, x_local=None, dim=-2):

        n, h, b, t, d = x_local.size()
        x_global = x_global.unsqueeze(-3).expand(-1, -1, b, -1, -1)
        if x_sparse is not None:
            return torch.cat([x_global, x_sparse, x_local], dim=dim)
        return torch.cat([x_global, x_local], dim=dim)

    def chunk(self, x, n_blocks):

        t, d = x.size()[-2:]
        return x.reshape(*x.size()[:-2], n_blocks, -1, d)


class LSGBartEncoderAttention(BaseSelfAttention):
    '''
    Compute local attention with overlapping blocs
    Use global attention for tokens with highest norm
    '''
    def __init__(
        self, 
        config, 
        embed_dim,
        num_heads,
        dropout
        ):

        super().__init__(embed_dim, num_heads, dropout)

        self.block_size = config.block_size
        self.sparse_block_size = config.sparse_block_size
        self.num_global_tokens = config.num_global_tokens
        self.sparsity_factor = config.sparsity_factor

        self.attention = LSGAttentionProduct(
            config, 
            block_size=config.block_size, 
            sparse_block_size=config.sparse_block_size, 
            sparsity_factor=self.sparsity_factor, 
            )

        self.full_attention = BaseAttentionProduct(config)

        sparse_functions = {
            "norm": self.get_sparse_tokens_with_norm, 
            "pooling": self.get_sparse_tokens_with_pooling,
            "lsh": self.get_sparse_tokens_with_lsh,
            "stride": self.get_sparse_tokens_with_stride,
            }
        
        self.sparsity_type = config.sparsity_type
        self.get_sparse_elements = sparse_functions.get(self.sparsity_type, lambda x, y, z: (None, None, None))
            
        if config.sparsity_type == "lsh":
            self.lsh_num_pre_rounds = config.lsh_num_pre_rounds
        
    def get_sparse_tokens_with_norm(self, keys, values, mask):
        
        if self.sparsity_factor == 1:
            return keys, values, mask.expand(-1, keys.size()[1], -1, -1)

        with torch.no_grad():

            block_size = min(self.block_size, self.sparse_block_size)
            key_norm = keys.detach().norm(dim=-1, keepdim=True)
            key_norm = key_norm * ~mask.transpose(-1, -2).bool()
            key_norm = self.chunk(key_norm, block_size)

            n, h, b, t, d = key_norm.size()
            
            idx = key_norm.argsort(dim=-2) 
            del key_norm
            idx += (torch.arange(b, device=keys.device)*t).reshape(1, 1, b, 1, 1)

            split = (t - block_size // self.sparsity_factor, block_size // self.sparsity_factor)
            sparse_idx = idx.split(split, -2)[-1].reshape(n, h, -1, 1)
        
        d = keys.size()[-1]
        keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
        values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
        mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2)

        return keys, values, mask

    def get_sparse_tokens_with_pooling(self, keys, values, mask):
        
        if self.sparsity_factor == 1:
            return keys, values, mask.expand(-1, keys.size()[1], -1, -1)

        keys = self.chunk(keys, self.sparsity_factor)
        values = self.chunk(values, self.sparsity_factor)

        n, h, b, t, d = keys.size()
        mask = mask.reshape(n, 1, b, 1, t)
        mask = ~mask.transpose(-1, -2).bool()

        keys = keys * mask
        values = values * mask

        mask = mask.sum(dim=-2)
        keys = keys.sum(dim=-2) / (mask + 1e-6)
        values = values.sum(dim=-2) / (mask + 1e-6)

        mask = - (1. - mask.clamp(0, 1)) * 1e4
        return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.expand(-1, h, -1, -1).transpose(-1, -2)

    def get_sparse_tokens_with_stride(self, keys, values, mask):

        if self.sparsity_factor == 1:
            return keys, values, mask.expand(-1, keys.size()[1], -1, -1)

        n, h, t, d = keys.size()
        sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device) * self.sparsity_factor
        sparse_idx = sparse_idx.reshape(1, 1, -1, 1) + (torch.arange(h, device=keys.device) % self.sparsity_factor).reshape(1, h, 1, 1)
        sparse_idx = sparse_idx.expand(n, h, -1, 1)

        """
        t, b = self.block_size, t // self.block_size
        sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device) * self.sparsity_factor
        sparse_idx = sparse_idx.reshape(1, 1, 1, -1, 1) + (torch.arange(h, device=keys.device) % self.sparsity_factor).reshape(1, h, 1, 1, 1)
        sparse_idx = sparse_idx + torch.arange(b, device=keys.device).reshape(1, 1, -1, 1, 1) * t
        sparse_idx = sparse_idx.reshape(1, h, -1, 1).expand(n, h, -1, 1)

        
        t, b = self.block_size, t // self.block_size
        sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device)
        sparse_idx = sparse_idx.reshape(1, 1, 1, -1, 1) + torch.arange(h, device=keys.device).reshape(1, h, 1, 1, 1) * (t // self.sparsity_factor)
        sparse_idx = (sparse_idx % t) 
        #sparse_idx[..., -t//2:, :] = (sparse_idx[..., -t//2:, :] + t//2) % t
        sparse_idx = sparse_idx + torch.arange(b, device=keys.device).reshape(1, 1, -1, 1, 1) * t
        sparse_idx = sparse_idx.reshape(1, h, -1, 1).expand(n, h, -1, 1)
        """

        keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
        values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
        mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2)

        return keys, values, mask

    def get_sparse_tokens_with_lsh(self, keys, values, mask):
        
        if self.sparsity_factor == 1:
            return keys, values, mask.expand(-1, keys.size()[1], -1, -1)

        block_size = min(self.block_size, self.sparse_block_size)
        keys = self.chunk(keys, block_size)
        values = self.chunk(values, block_size)

        n, h, b, t, d = keys.size()
        mask = mask.reshape(n, 1, b, 1, t)
        mask = ~mask.transpose(-1, -2).bool()

        keys = keys * mask
        values = values * mask
        mask = mask.expand(-1, h, -1, -1, -1).float()

        extra_factor = 1
        
        for _ in range(self.lsh_num_pre_rounds):
            keys, values, mask = self.lsh_round(keys, values, mask, t*extra_factor)

        keys, values, mask = self.lsh_round(keys, values, mask, t//self.sparsity_factor)
        keys /= mask + 1e-8
        values /= mask + 1e-8

        mask = -10000 * (1. - mask.clamp(0, 1))

        return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.transpose(-1, -2).reshape(n, h, 1, -1)

    def lsh_round(self, keys, values, mask, output_size):

        with torch.no_grad():

            n_hashes = output_size // 2
            n, h, b, t, d = keys.size()
            binary_mask = mask.clamp(0, 1)

            indexes = (torch.nn.functional.normalize(keys, dim=-1) * binary_mask) @ torch.randn(1, h, 1, d, n_hashes, device=keys.device)
            indexes = torch.cat([indexes, -indexes], dim=-1).argmax(dim=-1, keepdim=True)

        n, h, b, t, d = keys.size()
        
        x_ = torch.zeros(n, h, b, output_size, d, device=keys.device)
        mask_ = torch.zeros(n, h, b, output_size, 1, device=keys.device)
        keys = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=keys)
        values = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=values)
        mask = torch.scatter_add(mask_, dim=-2, index=indexes, src=mask)

        return keys[..., :output_size, :], values[..., :output_size, :], mask[..., :output_size, :]

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        layer_head_mask=None,
        output_attentions=False
        ):

        query_layer, key_layer, value_layer = self.project_QKV(hidden_states)
        outputs = self.not_causal_forward(
            query_layer,
            key_layer,
            value_layer, 
            attention_mask=attention_mask[:, :, :1, :], 
            head_mask=layer_head_mask, 
            output_attentions=output_attentions
            )
        
        return self.out_proj(outputs), None, None

    def not_causal_forward(
        self,
        query_layer,
        key_layer,
        value_layer,
        attention_mask=None,
        head_mask=None,
        output_attentions=False,
        ):

        n, h, t, d = query_layer.size()

        # Cat global mask
        attention_mask = torch.nn.functional.pad(attention_mask, (self.num_global_tokens, 0), value=0)
        
        # Use normal attention if local attention covers every tokens
        if t <= 2 * self.block_size + self.num_global_tokens:
            context_layer = self.full_attention(
                query_layer=query_layer, 
                key_layer=key_layer, 
                value_layer=value_layer, 
                attention_mask=attention_mask
                )

            if head_mask is not None:
                context_layer = context_layer * head_mask[:, :, :1, :1]
            return self.reshape_output(context_layer)

        # Split input into global tokens and other tokens
        split = (self.num_global_tokens, t - self.num_global_tokens)
        global_query, query_layer = query_layer.split(split, dim=-2)
        
        # Get global_attention
        bos = self.full_attention(
            query_layer=global_query, 
            key_layer=key_layer, 
            value_layer=value_layer, 
            attention_mask=attention_mask
            )
        
        # Split K Q M on global and non global
        global_key, key_layer = key_layer.split(split, dim=-2)
        global_value, value_layer = value_layer.split(split, dim=-2)
        global_mask, attention_mask = attention_mask.split(split, dim=-1)
        
        n, h, t, d = key_layer.size()

        # Get sparse idx
        sparse_key, sparse_value, sparse_mask = (None, None, None)

        if self.sparse_block_size and self.sparsity_factor > 0:
            sparse_key, sparse_value, sparse_mask = self.get_sparse_elements(key_layer, value_layer, attention_mask)
        
        # Expand masks on heads
        attention_mask = attention_mask.expand(-1, h, -1, -1)
        global_mask = global_mask.expand(-1, h, -1, -1)

        # Compute dot product attention
        context_layer = self.attention(
            query_layer, 
            key_layer, 
            value_layer, 
            attention_mask,
            sparse_key=sparse_key, 
            sparse_value=sparse_value, 
            sparse_mask=sparse_mask,
            global_key=global_key,
            global_value=global_value,
            global_mask=global_mask
            )

        # Merge global and local-sparse tokens
        context_layer = torch.cat([bos, context_layer], dim=-2)
        if head_mask is not None:
            context_layer = context_layer * head_mask[:, :, :1, :1]
        context_layer = self.reshape_output(context_layer)
        
        return context_layer

    def chunk(self, x, chunk_size):

        n, h, t, d = x.size()
        return x.reshape(n, h, -1, chunk_size, d)


class LSGBartDecoderAttention(nn.Module):

    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        is_decoder=False,
        bias=True,
        ):

        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim ** -0.5
        self.is_decoder = is_decoder

        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)

    def _shape(self, tensor, seq_len, bsz):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states,
        key_value_states=None,
        past_key_value=None,
        attention_mask=None,
        layer_head_mask=None,
        output_attentions=False,
        ):

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
                )
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to be reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
            )

        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned aross GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


class LSGBartLearnedPositionalEmbedding(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    """

    def __init__(self, num_embeddings, embedding_dim):
        # 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 don't have this hack
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim)

    def forward(self, input_ids_shape, past_key_values_length=0):

        """`input_ids_shape` is expected to be [bsz x seqlen]."""
        bsz, seq_len = input_ids_shape[:2]
        positions = torch.arange(
            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
        )
        return super().forward(positions + self.offset)


class LSGBartEncoderLayer(nn.Module):

    def __init__(self, config):

        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = LSGBartEncoderAttention(
            config=config,
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.self_attn_layer_norm = nn.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 = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        output_attentions=False,
        ):
        """
        Args:
            hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
            attention_mask (:obj:`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 (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
        """
        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 = 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


class LSGBartDecoderLayer(nn.Module):

    def __init__(self, config):

        super().__init__()
        self.embed_dim = config.d_model

        self.self_attn = LSGBartDecoderAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = LSGBartDecoderAttention(
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.encoder_attn_layer_norm = nn.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 = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        layer_head_mask=None,
        cross_attn_layer_head_mask=None,
        past_key_value=None,
        output_attentions=False,
        use_cache=True,
        ):
        """
        Args:
            hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            cross_attn_layer_head_mask (:obj:`torch.FloatTensor`): mask for cross-attention heads in a given layer of
                size `(decoder_attention_heads,)`.
            past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Self Attention
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        # add present self-attn cache to positions 1,2 of present_key_value tuple

        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=self_attn_past_key_value,
            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)

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=cross_attn_past_key_value,
                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.encoder_attn_layer_norm(hidden_states)

            # add cross-attn to positions 3,4 of present_key_value tuple
            present_key_value = present_key_value + cross_attn_present_key_value

        # Fully Connected
        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)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class LSGBartClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    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, hidden_states):

        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense(hidden_states)
        hidden_states = torch.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states


class LSGBartPretrainedModel(PreTrainedModel):

    config_class = LSGBartConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]

    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, 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_()

    def _set_gradient_checkpointing(self, module, value=False):

        if isinstance(module, (LSGBartDecoder, LSGBartEncoder)):
            module.gradient_checkpointing = value

    @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


class PretrainedLSGBartModel(LSGBartPretrainedModel):

    def __init_subclass__(self):
        warnings.warn(
            "The class `PretrainedBartModel` has been depreciated, please use `LSGBartPretrainedModel` instead.",
            FutureWarning,
        )


class LSGBartEncoder(LSGBartPretrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    :class:`BartEncoderLayer`.
    Args:
        config: BartConfig
        embed_tokens (nn.Embedding): output embedding
    """

    def __init__(self, config, embed_tokens=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_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 = LSGBartLearnedPositionalEmbedding(
            config.max_position_embeddings,
            embed_dim,
        )
        self.layers = nn.ModuleList([LSGBartEncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(embed_dim)

        # 
        assert hasattr(config, "num_global_tokens")
        self.num_global_tokens = config.num_global_tokens
        self.pad_idx = config.pad_token_id

        assert hasattr(config, "block_size") and hasattr(config, "adaptive")
        self.block_size = config.block_size
        self.adaptive = config.adaptive
        self.pool_with_global = config.pool_with_global
        self.pass_global_tokens_to_decoder = config.pass_global_tokens_to_decoder

        self.global_embeddings = nn.Embedding(512, embedding_dim=config.d_model)

        self.gradient_checkpointing = False

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

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def forward(self,
        input_ids=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None
        ):

        
        inputs_ = input_ids if input_ids is not None else inputs_embeds
        n, t = inputs_.size()[:2]

        if attention_mask is None:
            attention_mask = torch.ones(n, t, device=inputs_.device)
            
        b = self.block_size * 2
        pad = t % self.block_size
        
        # Check if t is multiple of block_size and pad
        if t > b and pad > 0:
            pad_length = self.block_size - pad
            if input_ids is not None:
                input_ids = torch.nn.functional.pad(input_ids, (0, pad_length), value=self.pad_idx)
            else:
                inputs_embeds = torch.nn.functional.pad(inputs_embeds.transpose(-1, -2), (0, pad_length), value=0.).transpose(-1, -2)
            attention_mask = torch.nn.functional.pad(attention_mask, (0, pad_length), value=0)

        # else adaptive sequence length
        elif self.adaptive:
            # Get last non zero mask index
            s = int(attention_mask.cumsum(dim=-1).argmax(dim=-1).max()) + 1
            if s < t and self.block_size is not None:
                s = max(2, s // self.block_size + 1) * self.block_size if s > b else s
                if input_ids is not None:
                    input_ids = input_ids[:, :s]
                else:
                    inputs_embeds = inputs_embeds[:, :s]
                attention_mask = attention_mask[:, :s]
        
        n, t_ = attention_mask.size()
        
        encoder_outputs = self.forward_with_adaptive(
            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,
            )
        
        context = encoder_outputs[0]
        diff = t - t_

        if self.pass_global_tokens_to_decoder:
            offset = self.num_global_tokens
        else:
            if self.pool_with_global:
                context[:, self.num_global_tokens] = context[:, 0]
            context = context[..., self.num_global_tokens:, :]
            offset = 0

        # Adapt sequence to initial shape
        if diff > 0:
            context = torch.nn.functional.pad(context.transpose(-1, -2), pad=(0, diff), value=0).transpose(-1, -2)
        elif diff < 0:
            context = context[:, :t + offset]
        
        if return_dict:
            encoder_outputs.last_hidden_state = context
        else:
            encoder_outputs = (context, ) + encoder_outputs[1:]
        
        return encoder_outputs

    def forward_with_adaptive(
        self,
        input_ids=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        ):
        
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # 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_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-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_shape)
        hidden_states = inputs_embeds + embed_pos

        # Add global tokens
        n, t, d = hidden_states.size()
        global_idx = torch.arange(self.num_global_tokens, device=hidden_states.device).reshape(1, -1)
        hidden_states = torch.cat([self.global_embeddings(global_idx).expand(n, -1, -1), hidden_states], dim=-2)

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

        # expand attention_mask
        if attention_mask is not None:
            # [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 {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,
                        (head_mask[idx] if head_mask is not None else None),
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        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 LSGBartDecoder(LSGBartPretrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`LSGBartDecoderLayer`
    Args:
        config: BartConfig
        embed_tokens (nn.Embedding): output embedding
    """

    def __init__(self, config, embed_tokens=None):

        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.adaptive = config.adaptive

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

        self.embed_positions = LSGBartLearnedPositionalEmbedding(
            config.max_position_embeddings,
            config.d_model,
        )
        self.layers = nn.ModuleList([LSGBartDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(config.d_model)

        self.gradient_checkpointing = False

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

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
            ).to(self.device)

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def resize_inputs(self, inputs_embeds, attention_mask):
        pad = 0
        
        max_len = int(attention_mask.sum(dim=-1).max())
        pad = attention_mask.size()[-1] - max_len
        inputs_embeds = inputs_embeds[:, :max_len]
        attention_mask = attention_mask[..., :max_len]
        return pad, inputs_embeds, attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        ):

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        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

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

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

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

        # Resize to reduce computation
        pad = 0
        if self.adaptive:
            if attention_mask is not None:
                pad, inputs_embeds, attention_mask = self.resize_inputs(inputs_embeds, attention_mask)
                input_shape = inputs_embeds.size()[:-1]
            if encoder_attention_mask is not None:
                _, encoder_hidden_states, encoder_attention_mask = self.resize_inputs(encoder_hidden_states, encoder_attention_mask)

        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, input_shape, inputs_embeds, past_key_values_length
        )

        # expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

        # embed positions
        positions = self.embed_positions(input_shape, past_key_values_length)

        hidden_states = inputs_embeds + positions
        hidden_states = self.layernorm_embedding(hidden_states)

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None

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

        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 += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                if use_cache:
                    logger.warning(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, use_cache)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                    None,
                )
            else:

                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    cross_attn_layer_head_mask=(
                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
                    ),
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

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

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        # Resize to original shape
        hidden_states = torch.nn.functional.pad(hidden_states.transpose(-1, -2), pad=(0, pad), value=0).transpose(-1, -2)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


class LSGBartModel(LSGBartPretrainedModel):

    def __init__(self, config):

        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.pass_global_tokens_to_decoder = config.pass_global_tokens_to_decoder
        self.num_global_tokens = config.num_global_tokens
        self.encoder = LSGBartEncoder(config, self.shared)
        self.decoder = LSGBartDecoder(config, self.shared)

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

    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_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        encoder_outputs=None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        ):

        # different to other models, Bart 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, self.config.decoder_start_token_id
            )

        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

        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,
            )

        # Pad mask for global tokens
        if self.pass_global_tokens_to_decoder:
            attention_mask = torch.nn.functional.pad(attention_mask, pad=(self.num_global_tokens, 0), value=1)
            
        # 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 LSGBartForConditionalGeneration(BartForConditionalGeneration, LSGBartPretrainedModel):
    
    base_model_prefix = "model"
    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head\.weight"]

    def __init__(self, config):

        LSGBartPretrainedModel.__init__(self, config)
        self.model = LSGBartModel(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)
    
        # Initialize weights and apply final processing
        self.post_init()


class LSGBartForSequenceClassification(BartForSequenceClassification, LSGBartPretrainedModel):

    def __init__(self, config: LSGBartConfig, **kwargs):

        LSGBartPretrainedModel.__init__(self, config, **kwargs)
        self.model = LSGBartModel(config)
        self.classification_head = LSGBartClassificationHead(
            config.d_model,
            config.d_model,
            config.num_labels,
            config.classifier_dropout,
        )
        self.model._init_weights(self.classification_head.dense)
        self.model._init_weights(self.classification_head.out_proj)


class LSGBartForQuestionAnswering(BartForQuestionAnswering, LSGBartPretrainedModel):

    def __init__(self, config: LSGBartConfig):

        LSGBartPretrainedModel.__init__(self, config)

        config.num_labels = 2
        self.num_labels = config.num_labels

        self.model = LSGBartModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        self.model._init_weights(self.qa_outputs)


class LSGBartDecoderWrapper(LSGBartPretrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
    used in combination with the :class:`~transformers.EncoderDecoderModel` framework.
    """

    def __init__(self, config: LSGBartConfig):
        super().__init__(config)
        self.decoder = LSGBartDecoder(config)

    def forward(self, *args, **kwargs):
        return self.decoder(*args, **kwargs)


class LSGBartForCausalLM(BartForCausalLM, LSGBartPretrainedModel):

    def __init__(self, config: LSGBartConfig):

        config = copy.deepcopy(config)
        config.is_decoder = True
        config.is_encoder_decoder = False
        LSGBartPretrainedModel.__init__(self, config)
        self.model = LSGBartDecoderWrapper(config)

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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


def str_to_class(classname):
    return getattr(sys.modules[__name__], classname)

# Register model in Auto API
try:
    LSGBartConfig.register_for_auto_class()
    for key, value in AUTO_MAP.items():
        str_to_class(value.split(".")[-1]).register_for_auto_class(key)
except:
    warn("AutoRegister isn't available, you'll have to manually copy modeling.py after .save_pretrained(...).")
    warn("Update to transformers >= 4.17.0 to fix.")