readme

README.md

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+---
+language:
+- en
+tags:
+- summarization
+datasets:
+- scientific_papers
+metrics:
+- rouge
+model-index:
+- name: ccdv/lsg-bart-base-16384-pubmed
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
+**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
+
+# ccdv/lsg-bart-base-16384-pubmed
+
+This model is a fine-tuned version of [ccdv/lsg-bart-base-4096-pubmed](https://huggingface.co/ccdv/lsg-bart-base-4096-pubmed) on the scientific_papers pubmed dataset. \
+It achieves the following results on the test set:
+
+| Length | Global tokens | Sparse Type | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
+|:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
+| 16384  | 64            | -           | 256        | 0        | 768        | 48.29 | 22.53 | 29.35 | 44.55 |
+
+
+## Model description
+The model relies on Local-Sparse-Global attention to handle long sequences:
+![attn](attn.png)
+
+The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \
+The model is warm started from [ccdv/lsg-bart-base-4096-pubmed](https://huggingface.co/ccdv/lsg-bart-base-4096-pubmed), converted to handle long sequences (encoder only) and fine tuned. \
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 8e-05
+- train_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1.0
+
+### Generate hyperparameters
+
+The following hyperparameters were used during generation:
+- dataset_name: scientific_papers
+- dataset_config_name: pubmed
+- eval_batch_size: 2
+- early_stopping: True
+- ignore_pad_token_for_loss: True
+- length_penalty: 2.0
+- max_length: 512
+- min_length: 128
+- num_beams: 5
+- num_samples: None
+- no_repeat_ngram_size: None
+- seed: 123
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.10.1+cu102
+- Datasets 2.1.0
+- Tokenizers 0.11.6

config.json

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+{
+  "_name_or_path": "ccdv/lsg-bart-base-16384-pubmed",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "adaptive": true,
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "LSGBartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "modeling_lsg_bart.LSGBartConfig",
+    "AutoModel": "modeling_lsg_bart.LSGBartModel",
+    "AutoModelForCausalLM": "modeling_lsg_bart.LSGBartForCausalLM",
+    "AutoModelForQuestionAnswering": "modeling_lsg_bart.LSGBartForQuestionAnswering",
+    "AutoModelForSeq2SeqLM": "modeling_lsg_bart.LSGBartForConditionalGeneration",
+    "AutoModelForSequenceClassification": "modeling_lsg_bart.LSGBartForSequenceClassification"
+  },
+  "base_model_prefix": "lsg",
+  "block_size": 256,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "length_penalty": 2.0,
+  "lsh_num_pre_rounds": 1,
+  "max_length": 512,
+  "max_position_embeddings": 16384,
+  "min_length": 128,
+  "model_type": "bart",
+  "no_repeat_ngram_size": null,
+  "normalize_before": false,
+  "normalize_embedding": true,
+  "num_beams": 5,
+  "num_global_tokens": 64,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "pass_global_tokens_to_decoder": true,
+  "pool_with_global": true,
+  "scale_embedding": false,
+  "sparse_block_size": 0,
+  "sparsity_factor": 2,
+  "sparsity_type": "norm",
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "use_cache": true,
+  "vocab_size": 50265
+}

eval_results.json

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+{
+    "eval_gen_len": 337.3282,
+    "eval_loss": 1.5187041759490967,
+    "eval_rouge1": 48.2871,
+    "eval_rouge2": 22.5259,
+    "eval_rougeL": 29.3512,
+    "eval_rougeLsum": 44.5493,
+    "eval_runtime": 32015.2043,
+    "eval_samples": 6658,
+    "eval_samples_per_second": 0.208,
+    "eval_steps_per_second": 0.104
+}

modeling_lsg_bart.py

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+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)
+
+        assert sparsity_type in ["norm", "lsh", "pooling", "stride"], "Sparsity mode must be 'norm', 'lsh' or 'pooling'"
+        
+        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
+        
+        
+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:
+            assert self.block_size % self.sparsity_factor == 0, "block_size must be divisible by sparsity_factor"
+            assert self.block_size//self.sparsity_factor >= 1, "Config is wrong, make sure block_size >= sparsity_factor"
+            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[self.sparsity_type]
+
+        if config.sparsity_type == "stride":
+            if config.sparsity_factor > config.encoder_attention_heads:
+                logger.warning(
+                "Warning: sparsity_factor > encoder_attention_heads is not recommended for stride sparsity"
+            )
+            
+        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
+
+        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
+
+        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
+
+        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)
+
+        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
+
+        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.lsg_round(keys, values, mask, t*extra_factor)
+
+        keys, values, mask = self.lsg_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 lsg_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
+
+        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 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
+
+        # Cut
+        if attention_mask is not None:
+            max_len = int(attention_mask.sum(dim=-1).max())
+            inputs_embeds = inputs_embeds[:, :max_len]
+            attention_mask = attention_mask[..., :max_len]
+            input_shape = inputs_embeds.size()[:-1]
+
+        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],)
+
+        # 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(LSGBartPretrainedModel):
+    
+    base_model_prefix = "model"
+    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head\.weight"]
+
+    def __init__(self, config):
+
+        super().__init__(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()
+
+    def get_encoder(self):
+        return self.model.get_encoder()
+
+    def get_decoder(self):
+        return self.model.get_decoder()
+
+    def resize_token_embeddings(self, new_num_tokens):
+        new_embeddings = super().resize_token_embeddings(new_num_tokens)
+        self._resize_final_logits_bias(new_num_tokens)
+        return new_embeddings
+
+    def _resize_final_logits_bias(self, new_num_tokens):
+        old_num_tokens = self.final_logits_bias.shape[-1]
+        if new_num_tokens <= old_num_tokens:
+            new_bias = self.final_logits_bias[:, :new_num_tokens]
+        else:
+            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
+        self.register_buffer("final_logits_bias", new_bias)
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    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,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        ):
+
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
+            config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.
+        Returns:
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if labels is not None:
+            if decoder_input_ids is None and decoder_inputs_embeds is None:
+                decoder_input_ids = shift_tokens_right(
+                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
+                )
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            encoder_outputs=encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
+            head_mask=head_mask,
+            decoder_head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+
+        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+        
+        if not return_dict:
+            output = (lm_logits,) + outputs[1:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=masked_lm_loss,
+            logits=lm_logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        decoder_input_ids,
+        past=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        **kwargs
+        ):
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            decoder_input_ids = decoder_input_ids[:, -1:]
+
+        return {
+            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
+            "encoder_outputs": encoder_outputs,
+            "past_key_values": past,
+            "decoder_input_ids": decoder_input_ids,
+            "attention_mask": attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
+            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
+        }
+
+    def prepare_decoder_input_ids_from_labels(self, labels):
+        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            # cached cross_attention states don't have to be reordered -> they are always the same
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
+            )
+        return reordered_past
+
+
+class LSGBartForSequenceClassification(LSGBartPretrainedModel):
+
+    def __init__(self, config, **kwargs):
+
+        super().__init__(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)
+
+    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,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        ):
+
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        if input_ids is None and inputs_embeds is not None:
+            raise NotImplementedError(
+                f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+            )
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            head_mask=head_mask,
+            decoder_head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            encoder_outputs=encoder_outputs,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = outputs[0]  # last hidden state
+
+        eos_mask = input_ids.eq(self.config.eos_token_id)
+
+        t, t_ = eos_mask.size()[-1], hidden_states.size()[-2]
+        if t > t_:
+            eos_mask = eos_mask[:, :t_]
+
+        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+            raise ValueError("All examples must have the same number of <eos> tokens.")
+        sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
+            :, -1, :
+        ]
+        logits = self.classification_head(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.config.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+
+class LSGBartForQuestionAnswering(LSGBartPretrainedModel):
+
+    def __init__(self, config):
+
+        super().__init__(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)
+
+    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,
+        start_positions=None,
+        end_positions=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        ):
+
+        r"""
+        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if start_positions is not None and end_positions is not None:
+            use_cache = False
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            head_mask=head_mask,
+            decoder_head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            encoder_outputs=encoder_outputs,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (
+                start_logits,
+                end_logits,
+            ) + outputs[1:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return Seq2SeqQuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+
+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):
+        super().__init__(config)
+        self.decoder = LSGBartDecoder(config)
+
+    def forward(self, *args, **kwargs):
+        return self.decoder(*args, **kwargs)
+
+
+class LSGBartForCausalLM(LSGBartPretrainedModel):
+
+    def __init__(self, config):
+
+        super().__init__(config)
+        config = copy.deepcopy(config)
+        config.is_decoder = True
+        config.is_encoder_decoder = False
+        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 get_input_embeddings(self):
+        return self.model.decoder.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.decoder.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model.decoder = decoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    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,
+        labels=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
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            head_mask=head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        logits = self.lm_head(outputs[0])
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_ids.shape)
+
+        if past:
+            input_ids = input_ids[:, -1:]
+        # first step, decoder_cached_states are empty
+        return {
+            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
+            "attention_mask": attention_mask,
+            "past_key_values": past,
+            "use_cache": use_cache,
+        }
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+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.")

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50d0ecc7c34b142bf2c8ff8485397795187896aaaf99ce996b716877f1886a68
+size 653914167

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

tokenizer_config.json

@@ -0,0 +1 @@
+{"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 16384, "special_tokens_map_file": null, "name_or_path": "tmp/pubmed/lsg_local_large_lr_16384_full_trained", "tokenizer_class": "BartTokenizer"}