update

finetune/boolq/all_results.json

@@ -0,0 +1,16 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.6071922779083252,
+    "eval_f1": 0.7029288702928871,
+    "eval_loss": 0.6942005157470703,
+    "eval_mcc": 0.14370055324223993,
+    "eval_runtime": 1.3999,
+    "eval_samples": 723,
+    "eval_samples_per_second": 516.453,
+    "eval_steps_per_second": 65.003,
+    "train_loss": 0.5978388892279731,
+    "train_runtime": 99.3528,
+    "train_samples": 2072,
+    "train_samples_per_second": 208.55,
+    "train_steps_per_second": 1.812
+}

finetune/boolq/config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "architectures": [
+    "StructformerModelForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "structformer_as_hf.StructformerConfig",
+    "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel",
+    "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "conv_size": 9,
+  "dropatt": 0.1,
+  "dropout": 0.1,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "structformer",
+  "n_context_layers": 0,
+  "n_parser_layers": 4,
+  "nhead": 12,
+  "nlayers": 12,
+  "ntokens": 32000,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad": 0,
+  "pad_token_id": 1,
+  "pos_emb": true,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "relations": [
+    "head",
+    "child"
+  ],
+  "relative_bias": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000,
+  "weight_act": "softmax"
+}

finetune/boolq/eval_results.json

@@ -0,0 +1,11 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.6071922779083252,
+    "eval_f1": 0.7029288702928871,
+    "eval_loss": 0.6942005157470703,
+    "eval_mcc": 0.14370055324223993,
+    "eval_runtime": 1.3999,
+    "eval_samples": 723,
+    "eval_samples_per_second": 516.453,
+    "eval_steps_per_second": 65.003
+}

finetune/boolq/predict_results.txt

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finetune/boolq/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ca8ff33e534e52b8aec72bb5a89e3f6fbfcae473ea395e225305e360c7e1762
+size 534669003

finetune/boolq/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

finetune/boolq/structformer_as_hf.py

@@ -0,0 +1,1123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from transformers import PreTrainedModel
+from transformers import PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput
+from typing import List
+from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput
+)
+
+##########################################
+# HuggingFace Config
+##########################################
+class StructformerConfig(PretrainedConfig):
+    model_type = "structformer"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        n_context_layers=2,
+        nlayers=6,
+        ntokens=32000,
+        nhead=8,
+        dropout=0.1,
+        dropatt=0.1,
+        relative_bias=False,
+        pos_emb=False,
+        pad=0,
+        n_parser_layers=4,
+        conv_size=9,
+        relations=('head', 'child'),
+        weight_act='softmax',
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.n_context_layers = n_context_layers
+        self.nlayers = nlayers
+        self.ntokens = ntokens
+        self.nhead = nhead
+        self.dropout = dropout
+        self.dropatt = dropatt
+        self.relative_bias = relative_bias
+        self.pos_emb = pos_emb
+        self.pad = pad
+        self.n_parser_layers = n_parser_layers
+        self.conv_size = conv_size
+        self.relations = relations
+        self.weight_act = weight_act
+        super().__init__(**kwargs)
+
+##########################################
+# Custom Layers
+##########################################
+def _get_activation_fn(activation):
+  """Get specified activation function."""
+  if activation == "relu":
+    return nn.ReLU()
+  elif activation == "gelu":
+    return nn.GELU()
+  elif activation == "leakyrelu":
+    return nn.LeakyReLU()
+
+  raise RuntimeError(
+      "activation should be relu/gelu, not {}".format(activation))
+
+class Conv1d(nn.Module):
+  """1D convolution layer."""
+
+  def __init__(self, hidden_size, kernel_size, dilation=1):
+    """Initialization.
+    Args:
+      hidden_size: dimension of input embeddings
+      kernel_size: convolution kernel size
+      dilation: the spacing between the kernel points
+    """
+    super(Conv1d, self).__init__()
+
+    if kernel_size % 2 == 0:
+      padding = (kernel_size // 2) * dilation
+      self.shift = True
+    else:
+      padding = ((kernel_size - 1) // 2) * dilation
+      self.shift = False
+    self.conv = nn.Conv1d(
+        hidden_size,
+        hidden_size,
+        kernel_size,
+        padding=padding,
+        dilation=dilation)
+
+  def forward(self, x):
+    """Compute convolution.
+    Args:
+      x: input embeddings
+    Returns:
+      conv_output: convolution results
+    """
+
+    if self.shift:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:]
+    else:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)
+
+class MultiheadAttention(nn.Module):
+  """Multi-head self-attention layer."""
+
+  def __init__(self,
+               embed_dim,
+               num_heads,
+               dropout=0.,
+               bias=True,
+               v_proj=True,
+               out_proj=True,
+               relative_bias=True):
+    """Initialization.
+    Args:
+      embed_dim: dimension of input embeddings
+      num_heads: number of self-attention heads
+      dropout: dropout rate
+      bias: bool, indicate whether include bias for linear transformations
+      v_proj: bool, indicate whether project inputs to new values
+      out_proj: bool, indicate whether project outputs to new values
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(MultiheadAttention, self).__init__()
+    self.embed_dim = embed_dim
+
+    self.num_heads = num_heads
+    self.drop = nn.Dropout(dropout)
+    self.head_dim = embed_dim // num_heads
+    assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be "
+                                                         "divisible by "
+                                                         "num_heads")
+
+    self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    if v_proj:
+      self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.v_proj = nn.Identity()
+
+    if out_proj:
+      self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.out_proj = nn.Identity()
+
+    if relative_bias:
+      self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512)))
+    else:
+      self.relative_bias = None
+
+    self._reset_parameters()
+
+  def _reset_parameters(self):
+    """Initialize attention parameters."""
+
+    init.xavier_uniform_(self.q_proj.weight)
+    init.constant_(self.q_proj.bias, 0.)
+
+    init.xavier_uniform_(self.k_proj.weight)
+    init.constant_(self.k_proj.bias, 0.)
+
+    if isinstance(self.v_proj, nn.Linear):
+      init.xavier_uniform_(self.v_proj.weight)
+      init.constant_(self.v_proj.bias, 0.)
+
+    if isinstance(self.out_proj, nn.Linear):
+      init.xavier_uniform_(self.out_proj.weight)
+      init.constant_(self.out_proj.bias, 0.)
+
+  def forward(self, query, key_padding_mask=None, attn_mask=None):
+    """Compute multi-head self-attention.
+    Args:
+      query: input embeddings
+      key_padding_mask: 3D mask that prevents attention to certain positions
+      attn_mask: 3D mask that rescale the attention weight at each position
+    Returns:
+      attn_output: self-attention output
+    """
+
+    length, bsz, embed_dim = query.size()
+    assert embed_dim == self.embed_dim
+
+    head_dim = embed_dim // self.num_heads
+    assert head_dim * self.num_heads == embed_dim, ("embed_dim must be "
+                                                    "divisible by num_heads")
+    scaling = float(head_dim)**-0.5
+
+    q = self.q_proj(query)
+    k = self.k_proj(query)
+    v = self.v_proj(query)
+
+    q = q * scaling
+
+    if attn_mask is not None:
+      assert list(attn_mask.size()) == [bsz * self.num_heads,
+                                        query.size(0), query.size(0)]
+
+    q = q.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    k = k.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    v = v.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+    assert list(
+        attn_output_weights.size()) == [bsz * self.num_heads, length, length]
+
+    if self.relative_bias is not None:
+      pos = torch.arange(length, device=query.device)
+      relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256
+      relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1,
+                                                     -1)
+
+      relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0)
+      relative_bias = relative_bias[:, None, :].expand(-1, length, -1)
+      relative_bias = torch.gather(relative_bias, 2, relative_pos)
+      attn_output_weights = attn_output_weights + relative_bias
+
+    if key_padding_mask is not None:
+      attn_output_weights = attn_output_weights + key_padding_mask
+
+    if attn_mask is None:
+      attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
+    else:
+      attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask
+
+    attn_output_weights = self.drop(attn_output_weights)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+
+    assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim]
+    attn_output = attn_output.transpose(0, 1).contiguous().view(
+        length, bsz, embed_dim)
+    attn_output = self.out_proj(attn_output)
+
+    return attn_output
+
+class TransformerLayer(nn.Module):
+  """TransformerEncoderLayer is made up of self-attn and feedforward network."""
+
+  def __init__(self,
+               d_model,
+               nhead,
+               dim_feedforward=2048,
+               dropout=0.1,
+               dropatt=0.1,
+               activation="leakyrelu",
+               relative_bias=True):
+    """Initialization.
+    Args:
+      d_model: dimension of inputs
+      nhead: number of self-attention heads
+      dim_feedforward: dimension of hidden layer in feedforward layer
+      dropout: dropout rate
+      dropatt: drop attention rate
+      activation: activation function
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(TransformerLayer, self).__init__()
+    
+    self.self_attn = MultiheadAttention(
+        d_model, nhead, dropout=dropatt, relative_bias=relative_bias)
+    
+    # Implementation of Feedforward model
+    self.feedforward = nn.Sequential(
+        nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward),
+        _get_activation_fn(activation), nn.Dropout(dropout),
+        nn.Linear(dim_feedforward, d_model))
+
+    self.norm = nn.LayerNorm(d_model)
+    self.dropout1 = nn.Dropout(dropout)
+    self.dropout2 = nn.Dropout(dropout)
+
+    self.nhead = nhead
+
+  def forward(self, src, attn_mask=None, key_padding_mask=None):
+    """Pass the input through the encoder layer.
+    Args:
+      src: the sequence to the encoder layer (required).
+      attn_mask: the mask for the src sequence (optional).
+      key_padding_mask: the mask for the src keys per batch (optional).
+    Returns:
+      src3: the output of transformer layer, share the same shape as src.
+    """
+    src2 = self.self_attn(
+        self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask)
+    src2 = src + self.dropout1(src2)
+    src3 = self.feedforward(src2)
+    src3 = src2 + self.dropout2(src3)
+
+    return src3
+
+
+
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+##########################################
+# Custom Models
+##########################################
+def cumprod(x, reverse=False, exclusive=False):
+  """cumulative product."""
+  if reverse:
+    x = x.flip([-1])
+
+  if exclusive:
+    x = F.pad(x[:, :, :-1], (1, 0), value=1)
+
+  cx = x.cumprod(-1)
+
+  if reverse:
+    cx = cx.flip([-1])
+  return cx
+
+def cumsum(x, reverse=False, exclusive=False):
+  """cumulative sum."""
+  bsz, _, length = x.size()
+  device = x.device
+  if reverse:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).tril(-1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).tril(0)
+    cx = torch.bmm(x, w)
+  else:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).triu(1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).triu(0)
+    cx = torch.bmm(x, w)
+  return cx
+
+def cummin(x, reverse=False, exclusive=False, max_value=1e9):
+  """cumulative min."""
+  if reverse:
+    if exclusive:
+      x = F.pad(x[:, :, 1:], (0, 1), value=max_value)
+    x = x.flip([-1]).cummin(-1)[0].flip([-1])
+  else:
+    if exclusive:
+      x = F.pad(x[:, :, :-1], (1, 0), value=max_value)
+    x = x.cummin(-1)[0]
+  return x
+
+class Transformer(nn.Module):
+  """Transformer model."""
+
+  def __init__(self,
+               hidden_size,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=True,
+               pos_emb=False,
+               pad=0):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+    """
+
+    super(Transformer, self).__init__()
+
+    self.drop = nn.Dropout(dropout)
+
+    self.emb = nn.Embedding(ntokens, hidden_size)
+    if pos_emb:
+      self.pos_emb = nn.Embedding(500, hidden_size)
+
+    self.layers = nn.ModuleList([
+        TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                dropatt=dropatt, relative_bias=relative_bias)
+        for _ in range(nlayers)])
+
+    self.norm = nn.LayerNorm(hidden_size)
+
+    self.output_layer = nn.Linear(hidden_size, ntokens)
+    self.output_layer.weight = self.emb.weight
+
+    self.init_weights()
+
+    self.nlayers = nlayers
+    self.nhead = nhead
+    self.ntokens = ntokens
+    self.hidden_size = hidden_size
+    self.pad = pad
+
+  def init_weights(self):
+    """Initialize token embedding and output bias."""
+    initrange = 0.1
+    self.emb.weight.data.uniform_(-initrange, initrange)
+    if hasattr(self, 'pos_emb'):
+      self.pos_emb.weight.data.uniform_(-initrange, initrange)
+    self.output_layer.bias.data.fill_(0)
+
+  def visibility(self, x, device):
+    """Mask pad tokens."""
+    visibility = (x != self.pad).float()
+    visibility = visibility[:, None, :].expand(-1, x.size(1), -1)
+    visibility = torch.repeat_interleave(visibility, self.nhead, dim=0)
+    return visibility.log()
+
+  def encode(self, x, pos):
+    """Standard transformer encode process."""
+    h = self.emb(x)
+    if hasattr(self, 'pos_emb'):
+      h = h + self.pos_emb(pos)
+    h_list = []
+    visibility = self.visibility(x, x.device)
+
+    for i in range(self.nlayers):
+      h_list.append(h)
+      h = self.layers[i](
+          h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+    output = h
+    h_array = torch.stack(h_list, dim=2)
+
+    return output, h_array
+
+  def forward(self, x, pos):
+    """Pass the input through the encoder layer.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      output: probability distributions for missing tokens.
+      state_dict: parsing results and raw output
+    """
+
+    batch_size, length = x.size()
+
+    raw_output, _ = self.encode(x, pos)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    return output.view(batch_size * length, -1), {'raw_output': raw_output,}
+
+class StructFormer(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax'):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+      n_parser_layers: number of parsing layers
+      conv_size: convolution kernel size for parser
+      relations: relations that are used to compute self attention
+      weight_act: relations distribution activation function
+    """
+
+    super(StructFormer, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    
+    loss = None
+    if labels is not None:
+      loss_fct = nn.CrossEntropyLoss()
+      loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1))
+    
+    return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=output, # shape: (batch_size * length, ntokens)
+            hidden_states=None,
+            attentions=None,
+    )   
+    
+
+
+
+class StructFormerClassification(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax',
+               config=None,
+               ):
+    
+
+    super(StructFormerClassification, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+    
+    self.num_labels = config.num_labels
+    self.config = config
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+    
+    self.classifier = RobertaClassificationHead(config)
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    #output = self.output_layer(raw_output)
+    logits = self.classifier(raw_output)
+    
+    loss = None
+    if labels is not None:
+        if self.config.problem_type is None:
+            if self.num_labels == 1:
+                self.config.problem_type = "regression"
+            elif self.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.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.num_labels), labels.view(-1))
+        elif self.config.problem_type == "multi_label_classification":
+            loss_fct = BCEWithLogitsLoss()
+            loss = loss_fct(logits, labels)
+
+
+    return SequenceClassifierOutput(
+        loss=loss,
+        logits=logits,
+        hidden_states=None,
+        attentions=None,
+    )
+
+
+
+##########################################
+# HuggingFace Model
+##########################################
+class StructformerModel(PreTrainedModel):
+    config_class = StructformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act
+        )
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)
+
+
+
+class StructformerModelForSequenceClassification(PreTrainedModel):
+    config_class = StructformerConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormerClassification(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act,
+               config=config)
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+                module.weight.data.fill_(1.0)
+
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)

finetune/boolq/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

finetune/boolq/train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 10.0,
+    "train_loss": 0.5978388892279731,
+    "train_runtime": 99.3528,
+    "train_samples": 2072,
+    "train_samples_per_second": 208.55,
+    "train_steps_per_second": 1.812
+}

finetune/boolq/trainer_state.json

@@ -0,0 +1,25 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 10.0,
+  "global_step": 180,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 10.0,
+      "step": 180,
+      "total_flos": 1729574880337920.0,
+      "train_loss": 0.5978388892279731,
+      "train_runtime": 99.3528,
+      "train_samples_per_second": 208.55,
+      "train_steps_per_second": 1.812
+    }
+  ],
+  "max_steps": 180,
+  "num_train_epochs": 10,
+  "total_flos": 1729574880337920.0,
+  "trial_name": null,
+  "trial_params": null
+}

finetune/boolq/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:884024d2e1f216c9a9e919729a7ade7c914cf9a7f3aaef5d07b647af45d074a5
+size 3503

finetune/cola/all_results.json

@@ -0,0 +1,16 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.689892053604126,
+    "eval_f1": 0.7931937172774869,
+    "eval_loss": 0.762876570224762,
+    "eval_mcc": 0.19495355846277174,
+    "eval_runtime": 1.9662,
+    "eval_samples": 1019,
+    "eval_samples_per_second": 518.247,
+    "eval_steps_per_second": 65.099,
+    "train_loss": 0.3857684093972911,
+    "train_runtime": 391.5771,
+    "train_samples": 8164,
+    "train_samples_per_second": 208.49,
+    "train_steps_per_second": 1.762
+}

finetune/cola/checkpoint-400/config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "architectures": [
+    "StructformerModelForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "structformer_as_hf.StructformerConfig",
+    "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel",
+    "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "conv_size": 9,
+  "dropatt": 0.1,
+  "dropout": 0.1,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "structformer",
+  "n_context_layers": 0,
+  "n_parser_layers": 4,
+  "nhead": 12,
+  "nlayers": 12,
+  "ntokens": 32000,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad": 0,
+  "pad_token_id": 1,
+  "pos_emb": true,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "relations": [
+    "head",
+    "child"
+  ],
+  "relative_bias": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000,
+  "weight_act": "softmax"
+}

finetune/cola/checkpoint-400/optimizer.pt

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+oid sha256:09a754a103adc04bc61c97c0cd7151bd44370f268ddea835479252b0ea4cae13
+size 1069068057

finetune/cola/checkpoint-400/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bc16b9a152d3c83b6da1a5a69be258541741700af78a1cb388eaf13d5424ed4
+size 534669003

finetune/cola/checkpoint-400/rng_state.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5769fda57687e02db326bee3281d32f541cdc788f737f37c47c3a91239b699cc
+size 14503

finetune/cola/checkpoint-400/scheduler.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b09db5c9b9264697e44562c70af05a12a67180f480992d419fa287214c7be7a
+size 623

finetune/cola/checkpoint-400/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

finetune/cola/checkpoint-400/structformer_as_hf.py

@@ -0,0 +1,1123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from transformers import PreTrainedModel
+from transformers import PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput
+from typing import List
+from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput
+)
+
+##########################################
+# HuggingFace Config
+##########################################
+class StructformerConfig(PretrainedConfig):
+    model_type = "structformer"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        n_context_layers=2,
+        nlayers=6,
+        ntokens=32000,
+        nhead=8,
+        dropout=0.1,
+        dropatt=0.1,
+        relative_bias=False,
+        pos_emb=False,
+        pad=0,
+        n_parser_layers=4,
+        conv_size=9,
+        relations=('head', 'child'),
+        weight_act='softmax',
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.n_context_layers = n_context_layers
+        self.nlayers = nlayers
+        self.ntokens = ntokens
+        self.nhead = nhead
+        self.dropout = dropout
+        self.dropatt = dropatt
+        self.relative_bias = relative_bias
+        self.pos_emb = pos_emb
+        self.pad = pad
+        self.n_parser_layers = n_parser_layers
+        self.conv_size = conv_size
+        self.relations = relations
+        self.weight_act = weight_act
+        super().__init__(**kwargs)
+
+##########################################
+# Custom Layers
+##########################################
+def _get_activation_fn(activation):
+  """Get specified activation function."""
+  if activation == "relu":
+    return nn.ReLU()
+  elif activation == "gelu":
+    return nn.GELU()
+  elif activation == "leakyrelu":
+    return nn.LeakyReLU()
+
+  raise RuntimeError(
+      "activation should be relu/gelu, not {}".format(activation))
+
+class Conv1d(nn.Module):
+  """1D convolution layer."""
+
+  def __init__(self, hidden_size, kernel_size, dilation=1):
+    """Initialization.
+    Args:
+      hidden_size: dimension of input embeddings
+      kernel_size: convolution kernel size
+      dilation: the spacing between the kernel points
+    """
+    super(Conv1d, self).__init__()
+
+    if kernel_size % 2 == 0:
+      padding = (kernel_size // 2) * dilation
+      self.shift = True
+    else:
+      padding = ((kernel_size - 1) // 2) * dilation
+      self.shift = False
+    self.conv = nn.Conv1d(
+        hidden_size,
+        hidden_size,
+        kernel_size,
+        padding=padding,
+        dilation=dilation)
+
+  def forward(self, x):
+    """Compute convolution.
+    Args:
+      x: input embeddings
+    Returns:
+      conv_output: convolution results
+    """
+
+    if self.shift:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:]
+    else:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)
+
+class MultiheadAttention(nn.Module):
+  """Multi-head self-attention layer."""
+
+  def __init__(self,
+               embed_dim,
+               num_heads,
+               dropout=0.,
+               bias=True,
+               v_proj=True,
+               out_proj=True,
+               relative_bias=True):
+    """Initialization.
+    Args:
+      embed_dim: dimension of input embeddings
+      num_heads: number of self-attention heads
+      dropout: dropout rate
+      bias: bool, indicate whether include bias for linear transformations
+      v_proj: bool, indicate whether project inputs to new values
+      out_proj: bool, indicate whether project outputs to new values
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(MultiheadAttention, self).__init__()
+    self.embed_dim = embed_dim
+
+    self.num_heads = num_heads
+    self.drop = nn.Dropout(dropout)
+    self.head_dim = embed_dim // num_heads
+    assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be "
+                                                         "divisible by "
+                                                         "num_heads")
+
+    self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    if v_proj:
+      self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.v_proj = nn.Identity()
+
+    if out_proj:
+      self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.out_proj = nn.Identity()
+
+    if relative_bias:
+      self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512)))
+    else:
+      self.relative_bias = None
+
+    self._reset_parameters()
+
+  def _reset_parameters(self):
+    """Initialize attention parameters."""
+
+    init.xavier_uniform_(self.q_proj.weight)
+    init.constant_(self.q_proj.bias, 0.)
+
+    init.xavier_uniform_(self.k_proj.weight)
+    init.constant_(self.k_proj.bias, 0.)
+
+    if isinstance(self.v_proj, nn.Linear):
+      init.xavier_uniform_(self.v_proj.weight)
+      init.constant_(self.v_proj.bias, 0.)
+
+    if isinstance(self.out_proj, nn.Linear):
+      init.xavier_uniform_(self.out_proj.weight)
+      init.constant_(self.out_proj.bias, 0.)
+
+  def forward(self, query, key_padding_mask=None, attn_mask=None):
+    """Compute multi-head self-attention.
+    Args:
+      query: input embeddings
+      key_padding_mask: 3D mask that prevents attention to certain positions
+      attn_mask: 3D mask that rescale the attention weight at each position
+    Returns:
+      attn_output: self-attention output
+    """
+
+    length, bsz, embed_dim = query.size()
+    assert embed_dim == self.embed_dim
+
+    head_dim = embed_dim // self.num_heads
+    assert head_dim * self.num_heads == embed_dim, ("embed_dim must be "
+                                                    "divisible by num_heads")
+    scaling = float(head_dim)**-0.5
+
+    q = self.q_proj(query)
+    k = self.k_proj(query)
+    v = self.v_proj(query)
+
+    q = q * scaling
+
+    if attn_mask is not None:
+      assert list(attn_mask.size()) == [bsz * self.num_heads,
+                                        query.size(0), query.size(0)]
+
+    q = q.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    k = k.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    v = v.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+    assert list(
+        attn_output_weights.size()) == [bsz * self.num_heads, length, length]
+
+    if self.relative_bias is not None:
+      pos = torch.arange(length, device=query.device)
+      relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256
+      relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1,
+                                                     -1)
+
+      relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0)
+      relative_bias = relative_bias[:, None, :].expand(-1, length, -1)
+      relative_bias = torch.gather(relative_bias, 2, relative_pos)
+      attn_output_weights = attn_output_weights + relative_bias
+
+    if key_padding_mask is not None:
+      attn_output_weights = attn_output_weights + key_padding_mask
+
+    if attn_mask is None:
+      attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
+    else:
+      attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask
+
+    attn_output_weights = self.drop(attn_output_weights)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+
+    assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim]
+    attn_output = attn_output.transpose(0, 1).contiguous().view(
+        length, bsz, embed_dim)
+    attn_output = self.out_proj(attn_output)
+
+    return attn_output
+
+class TransformerLayer(nn.Module):
+  """TransformerEncoderLayer is made up of self-attn and feedforward network."""
+
+  def __init__(self,
+               d_model,
+               nhead,
+               dim_feedforward=2048,
+               dropout=0.1,
+               dropatt=0.1,
+               activation="leakyrelu",
+               relative_bias=True):
+    """Initialization.
+    Args:
+      d_model: dimension of inputs
+      nhead: number of self-attention heads
+      dim_feedforward: dimension of hidden layer in feedforward layer
+      dropout: dropout rate
+      dropatt: drop attention rate
+      activation: activation function
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(TransformerLayer, self).__init__()
+    
+    self.self_attn = MultiheadAttention(
+        d_model, nhead, dropout=dropatt, relative_bias=relative_bias)
+    
+    # Implementation of Feedforward model
+    self.feedforward = nn.Sequential(
+        nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward),
+        _get_activation_fn(activation), nn.Dropout(dropout),
+        nn.Linear(dim_feedforward, d_model))
+
+    self.norm = nn.LayerNorm(d_model)
+    self.dropout1 = nn.Dropout(dropout)
+    self.dropout2 = nn.Dropout(dropout)
+
+    self.nhead = nhead
+
+  def forward(self, src, attn_mask=None, key_padding_mask=None):
+    """Pass the input through the encoder layer.
+    Args:
+      src: the sequence to the encoder layer (required).
+      attn_mask: the mask for the src sequence (optional).
+      key_padding_mask: the mask for the src keys per batch (optional).
+    Returns:
+      src3: the output of transformer layer, share the same shape as src.
+    """
+    src2 = self.self_attn(
+        self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask)
+    src2 = src + self.dropout1(src2)
+    src3 = self.feedforward(src2)
+    src3 = src2 + self.dropout2(src3)
+
+    return src3
+
+
+
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+##########################################
+# Custom Models
+##########################################
+def cumprod(x, reverse=False, exclusive=False):
+  """cumulative product."""
+  if reverse:
+    x = x.flip([-1])
+
+  if exclusive:
+    x = F.pad(x[:, :, :-1], (1, 0), value=1)
+
+  cx = x.cumprod(-1)
+
+  if reverse:
+    cx = cx.flip([-1])
+  return cx
+
+def cumsum(x, reverse=False, exclusive=False):
+  """cumulative sum."""
+  bsz, _, length = x.size()
+  device = x.device
+  if reverse:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).tril(-1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).tril(0)
+    cx = torch.bmm(x, w)
+  else:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).triu(1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).triu(0)
+    cx = torch.bmm(x, w)
+  return cx
+
+def cummin(x, reverse=False, exclusive=False, max_value=1e9):
+  """cumulative min."""
+  if reverse:
+    if exclusive:
+      x = F.pad(x[:, :, 1:], (0, 1), value=max_value)
+    x = x.flip([-1]).cummin(-1)[0].flip([-1])
+  else:
+    if exclusive:
+      x = F.pad(x[:, :, :-1], (1, 0), value=max_value)
+    x = x.cummin(-1)[0]
+  return x
+
+class Transformer(nn.Module):
+  """Transformer model."""
+
+  def __init__(self,
+               hidden_size,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=True,
+               pos_emb=False,
+               pad=0):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+    """
+
+    super(Transformer, self).__init__()
+
+    self.drop = nn.Dropout(dropout)
+
+    self.emb = nn.Embedding(ntokens, hidden_size)
+    if pos_emb:
+      self.pos_emb = nn.Embedding(500, hidden_size)
+
+    self.layers = nn.ModuleList([
+        TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                dropatt=dropatt, relative_bias=relative_bias)
+        for _ in range(nlayers)])
+
+    self.norm = nn.LayerNorm(hidden_size)
+
+    self.output_layer = nn.Linear(hidden_size, ntokens)
+    self.output_layer.weight = self.emb.weight
+
+    self.init_weights()
+
+    self.nlayers = nlayers
+    self.nhead = nhead
+    self.ntokens = ntokens
+    self.hidden_size = hidden_size
+    self.pad = pad
+
+  def init_weights(self):
+    """Initialize token embedding and output bias."""
+    initrange = 0.1
+    self.emb.weight.data.uniform_(-initrange, initrange)
+    if hasattr(self, 'pos_emb'):
+      self.pos_emb.weight.data.uniform_(-initrange, initrange)
+    self.output_layer.bias.data.fill_(0)
+
+  def visibility(self, x, device):
+    """Mask pad tokens."""
+    visibility = (x != self.pad).float()
+    visibility = visibility[:, None, :].expand(-1, x.size(1), -1)
+    visibility = torch.repeat_interleave(visibility, self.nhead, dim=0)
+    return visibility.log()
+
+  def encode(self, x, pos):
+    """Standard transformer encode process."""
+    h = self.emb(x)
+    if hasattr(self, 'pos_emb'):
+      h = h + self.pos_emb(pos)
+    h_list = []
+    visibility = self.visibility(x, x.device)
+
+    for i in range(self.nlayers):
+      h_list.append(h)
+      h = self.layers[i](
+          h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+    output = h
+    h_array = torch.stack(h_list, dim=2)
+
+    return output, h_array
+
+  def forward(self, x, pos):
+    """Pass the input through the encoder layer.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      output: probability distributions for missing tokens.
+      state_dict: parsing results and raw output
+    """
+
+    batch_size, length = x.size()
+
+    raw_output, _ = self.encode(x, pos)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    return output.view(batch_size * length, -1), {'raw_output': raw_output,}
+
+class StructFormer(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax'):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+      n_parser_layers: number of parsing layers
+      conv_size: convolution kernel size for parser
+      relations: relations that are used to compute self attention
+      weight_act: relations distribution activation function
+    """
+
+    super(StructFormer, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    
+    loss = None
+    if labels is not None:
+      loss_fct = nn.CrossEntropyLoss()
+      loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1))
+    
+    return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=output, # shape: (batch_size * length, ntokens)
+            hidden_states=None,
+            attentions=None,
+    )   
+    
+
+
+
+class StructFormerClassification(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax',
+               config=None,
+               ):
+    
+
+    super(StructFormerClassification, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+    
+    self.num_labels = config.num_labels
+    self.config = config
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+    
+    self.classifier = RobertaClassificationHead(config)
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    #output = self.output_layer(raw_output)
+    logits = self.classifier(raw_output)
+    
+    loss = None
+    if labels is not None:
+        if self.config.problem_type is None:
+            if self.num_labels == 1:
+                self.config.problem_type = "regression"
+            elif self.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.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.num_labels), labels.view(-1))
+        elif self.config.problem_type == "multi_label_classification":
+            loss_fct = BCEWithLogitsLoss()
+            loss = loss_fct(logits, labels)
+
+
+    return SequenceClassifierOutput(
+        loss=loss,
+        logits=logits,
+        hidden_states=None,
+        attentions=None,
+    )
+
+
+
+##########################################
+# HuggingFace Model
+##########################################
+class StructformerModel(PreTrainedModel):
+    config_class = StructformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act
+        )
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)
+
+
+
+class StructformerModelForSequenceClassification(PreTrainedModel):
+    config_class = StructformerConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormerClassification(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act,
+               config=config)
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+                module.weight.data.fill_(1.0)
+
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)

finetune/cola/checkpoint-400/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

finetune/cola/checkpoint-400/trainer_state.json

@@ -0,0 +1,27 @@
+{
+  "best_metric": 0.7931937172774869,
+  "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/cola/checkpoint-400",
+  "epoch": 5.797101449275362,
+  "global_step": 400,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 5.8,
+      "eval_accuracy": 0.689892053604126,
+      "eval_f1": 0.7931937172774869,
+      "eval_loss": 0.762876570224762,
+      "eval_mcc": 0.19495355846277174,
+      "eval_runtime": 1.9776,
+      "eval_samples_per_second": 515.265,
+      "eval_steps_per_second": 64.724,
+      "step": 400
+    }
+  ],
+  "max_steps": 690,
+  "num_train_epochs": 10,
+  "total_flos": 3958322433669120.0,
+  "trial_name": null,
+  "trial_params": null
+}

finetune/cola/checkpoint-400/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f4559102ecfa4341cceea0c32d0db1fe5185ac1bd8ad00af1c57c8dfaa12f80
+size 3503

finetune/cola/config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "architectures": [
+    "StructformerModelForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "structformer_as_hf.StructformerConfig",
+    "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel",
+    "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "conv_size": 9,
+  "dropatt": 0.1,
+  "dropout": 0.1,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "structformer",
+  "n_context_layers": 0,
+  "n_parser_layers": 4,
+  "nhead": 12,
+  "nlayers": 12,
+  "ntokens": 32000,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad": 0,
+  "pad_token_id": 1,
+  "pos_emb": true,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "relations": [
+    "head",
+    "child"
+  ],
+  "relative_bias": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000,
+  "weight_act": "softmax"
+}

finetune/cola/eval_results.json

@@ -0,0 +1,11 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.689892053604126,
+    "eval_f1": 0.7931937172774869,
+    "eval_loss": 0.762876570224762,
+    "eval_mcc": 0.19495355846277174,
+    "eval_runtime": 1.9662,
+    "eval_samples": 1019,
+    "eval_samples_per_second": 518.247,
+    "eval_steps_per_second": 65.099
+}

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finetune/cola/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bc16b9a152d3c83b6da1a5a69be258541741700af78a1cb388eaf13d5424ed4
+size 534669003

finetune/cola/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

finetune/cola/structformer_as_hf.py

@@ -0,0 +1,1123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from transformers import PreTrainedModel
+from transformers import PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput
+from typing import List
+from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput
+)
+
+##########################################
+# HuggingFace Config
+##########################################
+class StructformerConfig(PretrainedConfig):
+    model_type = "structformer"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        n_context_layers=2,
+        nlayers=6,
+        ntokens=32000,
+        nhead=8,
+        dropout=0.1,
+        dropatt=0.1,
+        relative_bias=False,
+        pos_emb=False,
+        pad=0,
+        n_parser_layers=4,
+        conv_size=9,
+        relations=('head', 'child'),
+        weight_act='softmax',
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.n_context_layers = n_context_layers
+        self.nlayers = nlayers
+        self.ntokens = ntokens
+        self.nhead = nhead
+        self.dropout = dropout
+        self.dropatt = dropatt
+        self.relative_bias = relative_bias
+        self.pos_emb = pos_emb
+        self.pad = pad
+        self.n_parser_layers = n_parser_layers
+        self.conv_size = conv_size
+        self.relations = relations
+        self.weight_act = weight_act
+        super().__init__(**kwargs)
+
+##########################################
+# Custom Layers
+##########################################
+def _get_activation_fn(activation):
+  """Get specified activation function."""
+  if activation == "relu":
+    return nn.ReLU()
+  elif activation == "gelu":
+    return nn.GELU()
+  elif activation == "leakyrelu":
+    return nn.LeakyReLU()
+
+  raise RuntimeError(
+      "activation should be relu/gelu, not {}".format(activation))
+
+class Conv1d(nn.Module):
+  """1D convolution layer."""
+
+  def __init__(self, hidden_size, kernel_size, dilation=1):
+    """Initialization.
+    Args:
+      hidden_size: dimension of input embeddings
+      kernel_size: convolution kernel size
+      dilation: the spacing between the kernel points
+    """
+    super(Conv1d, self).__init__()
+
+    if kernel_size % 2 == 0:
+      padding = (kernel_size // 2) * dilation
+      self.shift = True
+    else:
+      padding = ((kernel_size - 1) // 2) * dilation
+      self.shift = False
+    self.conv = nn.Conv1d(
+        hidden_size,
+        hidden_size,
+        kernel_size,
+        padding=padding,
+        dilation=dilation)
+
+  def forward(self, x):
+    """Compute convolution.
+    Args:
+      x: input embeddings
+    Returns:
+      conv_output: convolution results
+    """
+
+    if self.shift:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:]
+    else:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)
+
+class MultiheadAttention(nn.Module):
+  """Multi-head self-attention layer."""
+
+  def __init__(self,
+               embed_dim,
+               num_heads,
+               dropout=0.,
+               bias=True,
+               v_proj=True,
+               out_proj=True,
+               relative_bias=True):
+    """Initialization.
+    Args:
+      embed_dim: dimension of input embeddings
+      num_heads: number of self-attention heads
+      dropout: dropout rate
+      bias: bool, indicate whether include bias for linear transformations
+      v_proj: bool, indicate whether project inputs to new values
+      out_proj: bool, indicate whether project outputs to new values
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(MultiheadAttention, self).__init__()
+    self.embed_dim = embed_dim
+
+    self.num_heads = num_heads
+    self.drop = nn.Dropout(dropout)
+    self.head_dim = embed_dim // num_heads
+    assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be "
+                                                         "divisible by "
+                                                         "num_heads")
+
+    self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    if v_proj:
+      self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.v_proj = nn.Identity()
+
+    if out_proj:
+      self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.out_proj = nn.Identity()
+
+    if relative_bias:
+      self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512)))
+    else:
+      self.relative_bias = None
+
+    self._reset_parameters()
+
+  def _reset_parameters(self):
+    """Initialize attention parameters."""
+
+    init.xavier_uniform_(self.q_proj.weight)
+    init.constant_(self.q_proj.bias, 0.)
+
+    init.xavier_uniform_(self.k_proj.weight)
+    init.constant_(self.k_proj.bias, 0.)
+
+    if isinstance(self.v_proj, nn.Linear):
+      init.xavier_uniform_(self.v_proj.weight)
+      init.constant_(self.v_proj.bias, 0.)
+
+    if isinstance(self.out_proj, nn.Linear):
+      init.xavier_uniform_(self.out_proj.weight)
+      init.constant_(self.out_proj.bias, 0.)
+
+  def forward(self, query, key_padding_mask=None, attn_mask=None):
+    """Compute multi-head self-attention.
+    Args:
+      query: input embeddings
+      key_padding_mask: 3D mask that prevents attention to certain positions
+      attn_mask: 3D mask that rescale the attention weight at each position
+    Returns:
+      attn_output: self-attention output
+    """
+
+    length, bsz, embed_dim = query.size()
+    assert embed_dim == self.embed_dim
+
+    head_dim = embed_dim // self.num_heads
+    assert head_dim * self.num_heads == embed_dim, ("embed_dim must be "
+                                                    "divisible by num_heads")
+    scaling = float(head_dim)**-0.5
+
+    q = self.q_proj(query)
+    k = self.k_proj(query)
+    v = self.v_proj(query)
+
+    q = q * scaling
+
+    if attn_mask is not None:
+      assert list(attn_mask.size()) == [bsz * self.num_heads,
+                                        query.size(0), query.size(0)]
+
+    q = q.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    k = k.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    v = v.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+    assert list(
+        attn_output_weights.size()) == [bsz * self.num_heads, length, length]
+
+    if self.relative_bias is not None:
+      pos = torch.arange(length, device=query.device)
+      relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256
+      relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1,
+                                                     -1)
+
+      relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0)
+      relative_bias = relative_bias[:, None, :].expand(-1, length, -1)
+      relative_bias = torch.gather(relative_bias, 2, relative_pos)
+      attn_output_weights = attn_output_weights + relative_bias
+
+    if key_padding_mask is not None:
+      attn_output_weights = attn_output_weights + key_padding_mask
+
+    if attn_mask is None:
+      attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
+    else:
+      attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask
+
+    attn_output_weights = self.drop(attn_output_weights)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+
+    assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim]
+    attn_output = attn_output.transpose(0, 1).contiguous().view(
+        length, bsz, embed_dim)
+    attn_output = self.out_proj(attn_output)
+
+    return attn_output
+
+class TransformerLayer(nn.Module):
+  """TransformerEncoderLayer is made up of self-attn and feedforward network."""
+
+  def __init__(self,
+               d_model,
+               nhead,
+               dim_feedforward=2048,
+               dropout=0.1,
+               dropatt=0.1,
+               activation="leakyrelu",
+               relative_bias=True):
+    """Initialization.
+    Args:
+      d_model: dimension of inputs
+      nhead: number of self-attention heads
+      dim_feedforward: dimension of hidden layer in feedforward layer
+      dropout: dropout rate
+      dropatt: drop attention rate
+      activation: activation function
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(TransformerLayer, self).__init__()
+    
+    self.self_attn = MultiheadAttention(
+        d_model, nhead, dropout=dropatt, relative_bias=relative_bias)
+    
+    # Implementation of Feedforward model
+    self.feedforward = nn.Sequential(
+        nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward),
+        _get_activation_fn(activation), nn.Dropout(dropout),
+        nn.Linear(dim_feedforward, d_model))
+
+    self.norm = nn.LayerNorm(d_model)
+    self.dropout1 = nn.Dropout(dropout)
+    self.dropout2 = nn.Dropout(dropout)
+
+    self.nhead = nhead
+
+  def forward(self, src, attn_mask=None, key_padding_mask=None):
+    """Pass the input through the encoder layer.
+    Args:
+      src: the sequence to the encoder layer (required).
+      attn_mask: the mask for the src sequence (optional).
+      key_padding_mask: the mask for the src keys per batch (optional).
+    Returns:
+      src3: the output of transformer layer, share the same shape as src.
+    """
+    src2 = self.self_attn(
+        self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask)
+    src2 = src + self.dropout1(src2)
+    src3 = self.feedforward(src2)
+    src3 = src2 + self.dropout2(src3)
+
+    return src3
+
+
+
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+##########################################
+# Custom Models
+##########################################
+def cumprod(x, reverse=False, exclusive=False):
+  """cumulative product."""
+  if reverse:
+    x = x.flip([-1])
+
+  if exclusive:
+    x = F.pad(x[:, :, :-1], (1, 0), value=1)
+
+  cx = x.cumprod(-1)
+
+  if reverse:
+    cx = cx.flip([-1])
+  return cx
+
+def cumsum(x, reverse=False, exclusive=False):
+  """cumulative sum."""
+  bsz, _, length = x.size()
+  device = x.device
+  if reverse:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).tril(-1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).tril(0)
+    cx = torch.bmm(x, w)
+  else:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).triu(1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).triu(0)
+    cx = torch.bmm(x, w)
+  return cx
+
+def cummin(x, reverse=False, exclusive=False, max_value=1e9):
+  """cumulative min."""
+  if reverse:
+    if exclusive:
+      x = F.pad(x[:, :, 1:], (0, 1), value=max_value)
+    x = x.flip([-1]).cummin(-1)[0].flip([-1])
+  else:
+    if exclusive:
+      x = F.pad(x[:, :, :-1], (1, 0), value=max_value)
+    x = x.cummin(-1)[0]
+  return x
+
+class Transformer(nn.Module):
+  """Transformer model."""
+
+  def __init__(self,
+               hidden_size,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=True,
+               pos_emb=False,
+               pad=0):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+    """
+
+    super(Transformer, self).__init__()
+
+    self.drop = nn.Dropout(dropout)
+
+    self.emb = nn.Embedding(ntokens, hidden_size)
+    if pos_emb:
+      self.pos_emb = nn.Embedding(500, hidden_size)
+
+    self.layers = nn.ModuleList([
+        TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                dropatt=dropatt, relative_bias=relative_bias)
+        for _ in range(nlayers)])
+
+    self.norm = nn.LayerNorm(hidden_size)
+
+    self.output_layer = nn.Linear(hidden_size, ntokens)
+    self.output_layer.weight = self.emb.weight
+
+    self.init_weights()
+
+    self.nlayers = nlayers
+    self.nhead = nhead
+    self.ntokens = ntokens
+    self.hidden_size = hidden_size
+    self.pad = pad
+
+  def init_weights(self):
+    """Initialize token embedding and output bias."""
+    initrange = 0.1
+    self.emb.weight.data.uniform_(-initrange, initrange)
+    if hasattr(self, 'pos_emb'):
+      self.pos_emb.weight.data.uniform_(-initrange, initrange)
+    self.output_layer.bias.data.fill_(0)
+
+  def visibility(self, x, device):
+    """Mask pad tokens."""
+    visibility = (x != self.pad).float()
+    visibility = visibility[:, None, :].expand(-1, x.size(1), -1)
+    visibility = torch.repeat_interleave(visibility, self.nhead, dim=0)
+    return visibility.log()
+
+  def encode(self, x, pos):
+    """Standard transformer encode process."""
+    h = self.emb(x)
+    if hasattr(self, 'pos_emb'):
+      h = h + self.pos_emb(pos)
+    h_list = []
+    visibility = self.visibility(x, x.device)
+
+    for i in range(self.nlayers):
+      h_list.append(h)
+      h = self.layers[i](
+          h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+    output = h
+    h_array = torch.stack(h_list, dim=2)
+
+    return output, h_array
+
+  def forward(self, x, pos):
+    """Pass the input through the encoder layer.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      output: probability distributions for missing tokens.
+      state_dict: parsing results and raw output
+    """
+
+    batch_size, length = x.size()
+
+    raw_output, _ = self.encode(x, pos)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    return output.view(batch_size * length, -1), {'raw_output': raw_output,}
+
+class StructFormer(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax'):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+      n_parser_layers: number of parsing layers
+      conv_size: convolution kernel size for parser
+      relations: relations that are used to compute self attention
+      weight_act: relations distribution activation function
+    """
+
+    super(StructFormer, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    
+    loss = None
+    if labels is not None:
+      loss_fct = nn.CrossEntropyLoss()
+      loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1))
+    
+    return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=output, # shape: (batch_size * length, ntokens)
+            hidden_states=None,
+            attentions=None,
+    )   
+    
+
+
+
+class StructFormerClassification(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax',
+               config=None,
+               ):
+    
+
+    super(StructFormerClassification, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+    
+    self.num_labels = config.num_labels
+    self.config = config
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+    
+    self.classifier = RobertaClassificationHead(config)
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    #output = self.output_layer(raw_output)
+    logits = self.classifier(raw_output)
+    
+    loss = None
+    if labels is not None:
+        if self.config.problem_type is None:
+            if self.num_labels == 1:
+                self.config.problem_type = "regression"
+            elif self.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.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.num_labels), labels.view(-1))
+        elif self.config.problem_type == "multi_label_classification":
+            loss_fct = BCEWithLogitsLoss()
+            loss = loss_fct(logits, labels)
+
+
+    return SequenceClassifierOutput(
+        loss=loss,
+        logits=logits,
+        hidden_states=None,
+        attentions=None,
+    )
+
+
+
+##########################################
+# HuggingFace Model
+##########################################
+class StructformerModel(PreTrainedModel):
+    config_class = StructformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act
+        )
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)
+
+
+
+class StructformerModelForSequenceClassification(PreTrainedModel):
+    config_class = StructformerConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormerClassification(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act,
+               config=config)
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+                module.weight.data.fill_(1.0)
+
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)

finetune/cola/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

finetune/cola/train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 10.0,
+    "train_loss": 0.3857684093972911,
+    "train_runtime": 391.5771,
+    "train_samples": 8164,
+    "train_samples_per_second": 208.49,
+    "train_steps_per_second": 1.762
+}

finetune/cola/trainer_state.json

@@ -0,0 +1,42 @@
+{
+  "best_metric": 0.7931937172774869,
+  "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/cola/checkpoint-400",
+  "epoch": 10.0,
+  "global_step": 690,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 5.8,
+      "eval_accuracy": 0.689892053604126,
+      "eval_f1": 0.7931937172774869,
+      "eval_loss": 0.762876570224762,
+      "eval_mcc": 0.19495355846277174,
+      "eval_runtime": 1.9776,
+      "eval_samples_per_second": 515.265,
+      "eval_steps_per_second": 64.724,
+      "step": 400
+    },
+    {
+      "epoch": 7.25,
+      "learning_rate": 1.3768115942028985e-05,
+      "loss": 0.4623,
+      "step": 500
+    },
+    {
+      "epoch": 10.0,
+      "step": 690,
+      "total_flos": 6814792144343040.0,
+      "train_loss": 0.3857684093972911,
+      "train_runtime": 391.5771,
+      "train_samples_per_second": 208.49,
+      "train_steps_per_second": 1.762
+    }
+  ],
+  "max_steps": 690,
+  "num_train_epochs": 10,
+  "total_flos": 6814792144343040.0,
+  "trial_name": null,
+  "trial_params": null
+}

finetune/cola/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f4559102ecfa4341cceea0c32d0db1fe5185ac1bd8ad00af1c57c8dfaa12f80
+size 3503

finetune/control_raising_control/all_results.json

@@ -0,0 +1,16 @@
+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.7487743496894836,
+    "eval_f1": 0.7944072948328267,
+    "eval_loss": 2.3855533599853516,
+    "eval_mcc": 0.5565748633817942,
+    "eval_runtime": 13.0758,
+    "eval_samples": 6731,
+    "eval_samples_per_second": 514.768,
+    "eval_steps_per_second": 64.394,
+    "train_loss": 0.08940400845815681,
+    "train_runtime": 342.2215,
+    "train_samples": 6570,
+    "train_samples_per_second": 191.981,
+    "train_steps_per_second": 1.607
+}

finetune/control_raising_control/checkpoint-400/config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "architectures": [
+    "StructformerModelForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "structformer_as_hf.StructformerConfig",
+    "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel",
+    "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "conv_size": 9,
+  "dropatt": 0.1,
+  "dropout": 0.1,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "structformer",
+  "n_context_layers": 0,
+  "n_parser_layers": 4,
+  "nhead": 12,
+  "nlayers": 12,
+  "ntokens": 32000,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad": 0,
+  "pad_token_id": 1,
+  "pos_emb": true,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "relations": [
+    "head",
+    "child"
+  ],
+  "relative_bias": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000,
+  "weight_act": "softmax"
+}

finetune/control_raising_control/checkpoint-400/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7de6edd5fe571d34ec0a653350c58cad024bd88053adb63a25dbf64389a4afff
+size 1069068057

finetune/control_raising_control/checkpoint-400/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ebf4182ca1295e18b36927bca97bdfc348d7fa6004f8e520b06007af1861628
+size 534669003

finetune/control_raising_control/checkpoint-400/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02576a68480d1bd556bf2545dbb91a6fcd2d751faf798e19f80054fd04bf34a7
+size 14503

finetune/control_raising_control/checkpoint-400/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c2403f14641b3caeb1b4d17bf70ec776358494ec9059cbe53a4c9c5a18c4c15
+size 623

finetune/control_raising_control/checkpoint-400/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

finetune/control_raising_control/checkpoint-400/structformer_as_hf.py

@@ -0,0 +1,1123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from transformers import PreTrainedModel
+from transformers import PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput
+from typing import List
+from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput
+)
+
+##########################################
+# HuggingFace Config
+##########################################
+class StructformerConfig(PretrainedConfig):
+    model_type = "structformer"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        n_context_layers=2,
+        nlayers=6,
+        ntokens=32000,
+        nhead=8,
+        dropout=0.1,
+        dropatt=0.1,
+        relative_bias=False,
+        pos_emb=False,
+        pad=0,
+        n_parser_layers=4,
+        conv_size=9,
+        relations=('head', 'child'),
+        weight_act='softmax',
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.n_context_layers = n_context_layers
+        self.nlayers = nlayers
+        self.ntokens = ntokens
+        self.nhead = nhead
+        self.dropout = dropout
+        self.dropatt = dropatt
+        self.relative_bias = relative_bias
+        self.pos_emb = pos_emb
+        self.pad = pad
+        self.n_parser_layers = n_parser_layers
+        self.conv_size = conv_size
+        self.relations = relations
+        self.weight_act = weight_act
+        super().__init__(**kwargs)
+
+##########################################
+# Custom Layers
+##########################################
+def _get_activation_fn(activation):
+  """Get specified activation function."""
+  if activation == "relu":
+    return nn.ReLU()
+  elif activation == "gelu":
+    return nn.GELU()
+  elif activation == "leakyrelu":
+    return nn.LeakyReLU()
+
+  raise RuntimeError(
+      "activation should be relu/gelu, not {}".format(activation))
+
+class Conv1d(nn.Module):
+  """1D convolution layer."""
+
+  def __init__(self, hidden_size, kernel_size, dilation=1):
+    """Initialization.
+    Args:
+      hidden_size: dimension of input embeddings
+      kernel_size: convolution kernel size
+      dilation: the spacing between the kernel points
+    """
+    super(Conv1d, self).__init__()
+
+    if kernel_size % 2 == 0:
+      padding = (kernel_size // 2) * dilation
+      self.shift = True
+    else:
+      padding = ((kernel_size - 1) // 2) * dilation
+      self.shift = False
+    self.conv = nn.Conv1d(
+        hidden_size,
+        hidden_size,
+        kernel_size,
+        padding=padding,
+        dilation=dilation)
+
+  def forward(self, x):
+    """Compute convolution.
+    Args:
+      x: input embeddings
+    Returns:
+      conv_output: convolution results
+    """
+
+    if self.shift:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:]
+    else:
+      return self.conv(x.transpose(1, 2)).transpose(1, 2)
+
+class MultiheadAttention(nn.Module):
+  """Multi-head self-attention layer."""
+
+  def __init__(self,
+               embed_dim,
+               num_heads,
+               dropout=0.,
+               bias=True,
+               v_proj=True,
+               out_proj=True,
+               relative_bias=True):
+    """Initialization.
+    Args:
+      embed_dim: dimension of input embeddings
+      num_heads: number of self-attention heads
+      dropout: dropout rate
+      bias: bool, indicate whether include bias for linear transformations
+      v_proj: bool, indicate whether project inputs to new values
+      out_proj: bool, indicate whether project outputs to new values
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(MultiheadAttention, self).__init__()
+    self.embed_dim = embed_dim
+
+    self.num_heads = num_heads
+    self.drop = nn.Dropout(dropout)
+    self.head_dim = embed_dim // num_heads
+    assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be "
+                                                         "divisible by "
+                                                         "num_heads")
+
+    self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    if v_proj:
+      self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.v_proj = nn.Identity()
+
+    if out_proj:
+      self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+    else:
+      self.out_proj = nn.Identity()
+
+    if relative_bias:
+      self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512)))
+    else:
+      self.relative_bias = None
+
+    self._reset_parameters()
+
+  def _reset_parameters(self):
+    """Initialize attention parameters."""
+
+    init.xavier_uniform_(self.q_proj.weight)
+    init.constant_(self.q_proj.bias, 0.)
+
+    init.xavier_uniform_(self.k_proj.weight)
+    init.constant_(self.k_proj.bias, 0.)
+
+    if isinstance(self.v_proj, nn.Linear):
+      init.xavier_uniform_(self.v_proj.weight)
+      init.constant_(self.v_proj.bias, 0.)
+
+    if isinstance(self.out_proj, nn.Linear):
+      init.xavier_uniform_(self.out_proj.weight)
+      init.constant_(self.out_proj.bias, 0.)
+
+  def forward(self, query, key_padding_mask=None, attn_mask=None):
+    """Compute multi-head self-attention.
+    Args:
+      query: input embeddings
+      key_padding_mask: 3D mask that prevents attention to certain positions
+      attn_mask: 3D mask that rescale the attention weight at each position
+    Returns:
+      attn_output: self-attention output
+    """
+
+    length, bsz, embed_dim = query.size()
+    assert embed_dim == self.embed_dim
+
+    head_dim = embed_dim // self.num_heads
+    assert head_dim * self.num_heads == embed_dim, ("embed_dim must be "
+                                                    "divisible by num_heads")
+    scaling = float(head_dim)**-0.5
+
+    q = self.q_proj(query)
+    k = self.k_proj(query)
+    v = self.v_proj(query)
+
+    q = q * scaling
+
+    if attn_mask is not None:
+      assert list(attn_mask.size()) == [bsz * self.num_heads,
+                                        query.size(0), query.size(0)]
+
+    q = q.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    k = k.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+    v = v.contiguous().view(length, bsz * self.num_heads,
+                            head_dim).transpose(0, 1)
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+    assert list(
+        attn_output_weights.size()) == [bsz * self.num_heads, length, length]
+
+    if self.relative_bias is not None:
+      pos = torch.arange(length, device=query.device)
+      relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256
+      relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1,
+                                                     -1)
+
+      relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0)
+      relative_bias = relative_bias[:, None, :].expand(-1, length, -1)
+      relative_bias = torch.gather(relative_bias, 2, relative_pos)
+      attn_output_weights = attn_output_weights + relative_bias
+
+    if key_padding_mask is not None:
+      attn_output_weights = attn_output_weights + key_padding_mask
+
+    if attn_mask is None:
+      attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
+    else:
+      attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask
+
+    attn_output_weights = self.drop(attn_output_weights)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+
+    assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim]
+    attn_output = attn_output.transpose(0, 1).contiguous().view(
+        length, bsz, embed_dim)
+    attn_output = self.out_proj(attn_output)
+
+    return attn_output
+
+class TransformerLayer(nn.Module):
+  """TransformerEncoderLayer is made up of self-attn and feedforward network."""
+
+  def __init__(self,
+               d_model,
+               nhead,
+               dim_feedforward=2048,
+               dropout=0.1,
+               dropatt=0.1,
+               activation="leakyrelu",
+               relative_bias=True):
+    """Initialization.
+    Args:
+      d_model: dimension of inputs
+      nhead: number of self-attention heads
+      dim_feedforward: dimension of hidden layer in feedforward layer
+      dropout: dropout rate
+      dropatt: drop attention rate
+      activation: activation function
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+    """
+
+    super(TransformerLayer, self).__init__()
+    
+    self.self_attn = MultiheadAttention(
+        d_model, nhead, dropout=dropatt, relative_bias=relative_bias)
+    
+    # Implementation of Feedforward model
+    self.feedforward = nn.Sequential(
+        nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward),
+        _get_activation_fn(activation), nn.Dropout(dropout),
+        nn.Linear(dim_feedforward, d_model))
+
+    self.norm = nn.LayerNorm(d_model)
+    self.dropout1 = nn.Dropout(dropout)
+    self.dropout2 = nn.Dropout(dropout)
+
+    self.nhead = nhead
+
+  def forward(self, src, attn_mask=None, key_padding_mask=None):
+    """Pass the input through the encoder layer.
+    Args:
+      src: the sequence to the encoder layer (required).
+      attn_mask: the mask for the src sequence (optional).
+      key_padding_mask: the mask for the src keys per batch (optional).
+    Returns:
+      src3: the output of transformer layer, share the same shape as src.
+    """
+    src2 = self.self_attn(
+        self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask)
+    src2 = src + self.dropout1(src2)
+    src3 = self.feedforward(src2)
+    src3 = src2 + self.dropout2(src3)
+
+    return src3
+
+
+
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+##########################################
+# Custom Models
+##########################################
+def cumprod(x, reverse=False, exclusive=False):
+  """cumulative product."""
+  if reverse:
+    x = x.flip([-1])
+
+  if exclusive:
+    x = F.pad(x[:, :, :-1], (1, 0), value=1)
+
+  cx = x.cumprod(-1)
+
+  if reverse:
+    cx = cx.flip([-1])
+  return cx
+
+def cumsum(x, reverse=False, exclusive=False):
+  """cumulative sum."""
+  bsz, _, length = x.size()
+  device = x.device
+  if reverse:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).tril(-1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).tril(0)
+    cx = torch.bmm(x, w)
+  else:
+    if exclusive:
+      w = torch.ones([bsz, length, length], device=device).triu(1)
+    else:
+      w = torch.ones([bsz, length, length], device=device).triu(0)
+    cx = torch.bmm(x, w)
+  return cx
+
+def cummin(x, reverse=False, exclusive=False, max_value=1e9):
+  """cumulative min."""
+  if reverse:
+    if exclusive:
+      x = F.pad(x[:, :, 1:], (0, 1), value=max_value)
+    x = x.flip([-1]).cummin(-1)[0].flip([-1])
+  else:
+    if exclusive:
+      x = F.pad(x[:, :, :-1], (1, 0), value=max_value)
+    x = x.cummin(-1)[0]
+  return x
+
+class Transformer(nn.Module):
+  """Transformer model."""
+
+  def __init__(self,
+               hidden_size,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=True,
+               pos_emb=False,
+               pad=0):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+    """
+
+    super(Transformer, self).__init__()
+
+    self.drop = nn.Dropout(dropout)
+
+    self.emb = nn.Embedding(ntokens, hidden_size)
+    if pos_emb:
+      self.pos_emb = nn.Embedding(500, hidden_size)
+
+    self.layers = nn.ModuleList([
+        TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                dropatt=dropatt, relative_bias=relative_bias)
+        for _ in range(nlayers)])
+
+    self.norm = nn.LayerNorm(hidden_size)
+
+    self.output_layer = nn.Linear(hidden_size, ntokens)
+    self.output_layer.weight = self.emb.weight
+
+    self.init_weights()
+
+    self.nlayers = nlayers
+    self.nhead = nhead
+    self.ntokens = ntokens
+    self.hidden_size = hidden_size
+    self.pad = pad
+
+  def init_weights(self):
+    """Initialize token embedding and output bias."""
+    initrange = 0.1
+    self.emb.weight.data.uniform_(-initrange, initrange)
+    if hasattr(self, 'pos_emb'):
+      self.pos_emb.weight.data.uniform_(-initrange, initrange)
+    self.output_layer.bias.data.fill_(0)
+
+  def visibility(self, x, device):
+    """Mask pad tokens."""
+    visibility = (x != self.pad).float()
+    visibility = visibility[:, None, :].expand(-1, x.size(1), -1)
+    visibility = torch.repeat_interleave(visibility, self.nhead, dim=0)
+    return visibility.log()
+
+  def encode(self, x, pos):
+    """Standard transformer encode process."""
+    h = self.emb(x)
+    if hasattr(self, 'pos_emb'):
+      h = h + self.pos_emb(pos)
+    h_list = []
+    visibility = self.visibility(x, x.device)
+
+    for i in range(self.nlayers):
+      h_list.append(h)
+      h = self.layers[i](
+          h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+    output = h
+    h_array = torch.stack(h_list, dim=2)
+
+    return output, h_array
+
+  def forward(self, x, pos):
+    """Pass the input through the encoder layer.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      output: probability distributions for missing tokens.
+      state_dict: parsing results and raw output
+    """
+
+    batch_size, length = x.size()
+
+    raw_output, _ = self.encode(x, pos)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    return output.view(batch_size * length, -1), {'raw_output': raw_output,}
+
+class StructFormer(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax'):
+    """Initialization.
+    Args:
+      hidden_size: dimension of inputs and hidden states
+      nlayers: number of layers
+      ntokens: number of output categories
+      nhead: number of self-attention heads
+      dropout: dropout rate
+      dropatt: drop attention rate
+      relative_bias: bool, indicate whether use a relative position based
+        attention bias
+      pos_emb: bool, indicate whether use a learnable positional embedding
+      pad: pad token index
+      n_parser_layers: number of parsing layers
+      conv_size: convolution kernel size for parser
+      relations: relations that are used to compute self attention
+      weight_act: relations distribution activation function
+    """
+
+    super(StructFormer, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    output = self.output_layer(raw_output)
+    
+    loss = None
+    if labels is not None:
+      loss_fct = nn.CrossEntropyLoss()
+      loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1))
+    
+    return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=output, # shape: (batch_size * length, ntokens)
+            hidden_states=None,
+            attentions=None,
+    )   
+    
+
+
+
+class StructFormerClassification(Transformer):
+  """StructFormer model."""
+
+  def __init__(self,
+               hidden_size,
+               n_context_layers,
+               nlayers,
+               ntokens,
+               nhead=8,
+               dropout=0.1,
+               dropatt=0.1,
+               relative_bias=False,
+               pos_emb=False,
+               pad=0,
+               n_parser_layers=4,
+               conv_size=9,
+               relations=('head', 'child'),
+               weight_act='softmax',
+               config=None,
+               ):
+    
+
+    super(StructFormerClassification, self).__init__(
+        hidden_size,
+        nlayers,
+        ntokens,
+        nhead=nhead,
+        dropout=dropout,
+        dropatt=dropatt,
+        relative_bias=relative_bias,
+        pos_emb=pos_emb,
+        pad=pad)
+    
+    self.num_labels = config.num_labels
+    self.config = config
+
+    if n_context_layers > 0:
+      self.context_layers = nn.ModuleList([
+          TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout,
+                                  dropatt=dropatt, relative_bias=relative_bias)
+          for _ in range(n_context_layers)])
+  
+    self.parser_layers = nn.ModuleList([
+        nn.Sequential(Conv1d(hidden_size, conv_size),
+                      nn.LayerNorm(hidden_size, elementwise_affine=False),
+                      nn.Tanh()) for i in range(n_parser_layers)])
+
+    self.distance_ff = nn.Sequential(
+        Conv1d(hidden_size, 2),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    self.height_ff = nn.Sequential(
+        nn.Linear(hidden_size, hidden_size),
+        nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(),
+        nn.Linear(hidden_size, 1))
+
+    n_rel = len(relations)
+    self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel)))
+    self._rel_weight.data.normal_(0, 0.1)
+
+    self._scaler = nn.Parameter(torch.zeros(2))
+
+    self.n_parse_layers = n_parser_layers
+    self.n_context_layers = n_context_layers
+    self.weight_act = weight_act
+    self.relations = relations
+    
+    self.classifier = RobertaClassificationHead(config)
+
+  @property
+  def scaler(self):
+    return self._scaler.exp()
+
+  @property
+  def rel_weight(self):
+    if self.weight_act == 'sigmoid':
+      return torch.sigmoid(self._rel_weight)
+    elif self.weight_act == 'softmax':
+      return torch.softmax(self._rel_weight, dim=-1)
+
+  def parse(self, x, pos, embeds=None):
+    """Parse input sentence.
+    Args:
+      x: input tokens (required).
+      pos: position for each token (optional).
+    Returns:
+      distance: syntactic distance
+      height: syntactic height
+    """
+
+    mask = (x != self.pad)
+    mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+    
+    if embeds is not None:
+      h = embeds
+    else:
+      h = self.emb(x)
+    
+    for i in range(self.n_parse_layers):
+      h = h.masked_fill(~mask[:, :, None], 0)
+      h = self.parser_layers[i](h)
+
+    height = self.height_ff(h).squeeze(-1)
+    height.masked_fill_(~mask, -1e9)
+
+    distance = self.distance_ff(h).squeeze(-1)
+    distance.masked_fill_(~mask_shifted, 1e9)
+
+    # Calbrating the distance and height to the same level
+    length = distance.size(1)
+    height_max = height[:, None, :].expand(-1, length, -1)
+    height_max = torch.cummax(
+        height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9,
+        dim=-1)[0].triu(0)
+
+    margin_left = torch.relu(
+        F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max)
+    margin_right = torch.relu(distance[:, None, :] - height_max)
+    margin = torch.where(margin_left > margin_right, margin_right,
+                         margin_left).triu(0)
+
+    margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+    margin.masked_fill_(~margin_mask, 0)
+    margin = margin.max()
+
+    distance = distance - margin
+
+    return distance, height
+
+  def compute_block(self, distance, height):
+    """Compute constituents from distance and height."""
+
+    beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+    gamma = torch.sigmoid(-beta_logits)
+    ones = torch.ones_like(gamma)
+
+    block_mask_left = cummin(
+        gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1)
+    block_mask_left = block_mask_left - F.pad(
+        block_mask_left[:, :, :-1], (1, 0), value=0)
+    block_mask_left.tril_(0)
+
+    block_mask_right = cummin(
+        gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1)
+    block_mask_right = block_mask_right - F.pad(
+        block_mask_right[:, :, 1:], (0, 1), value=0)
+    block_mask_right.triu_(0)
+
+    block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+    block = cumsum(block_mask_left).tril(0) + cumsum(
+        block_mask_right, reverse=True).triu(1)
+
+    return block_p, block
+
+  def compute_head(self, height):
+    """Estimate head for each constituent."""
+
+    _, length = height.size()
+    head_logits = height * self.scaler[1]
+    index = torch.arange(length, device=height.device)
+
+    mask = (index[:, None, None] <= index[None, None, :]) * (
+        index[None, None, :] <= index[None, :, None])
+    head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+    head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+    head_p = torch.softmax(head_logits, dim=-1)
+
+    return head_p
+
+  def generate_mask(self, x, distance, height):
+    """Compute head and cibling distribution for each token."""
+
+    bsz, length = x.size()
+
+    eye = torch.eye(length, device=x.device, dtype=torch.bool)
+    eye = eye[None, :, :].expand((bsz, -1, -1))
+
+    block_p, block = self.compute_block(distance, height)
+    head_p = self.compute_head(height)
+    head = torch.einsum('blij,bijh->blh', block_p, head_p)
+    head = head.masked_fill(eye, 0)
+    child = head.transpose(1, 2)
+    cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+    rel_list = []
+    if 'head' in self.relations:
+      rel_list.append(head)
+    if 'child' in self.relations:
+      rel_list.append(child)
+    if 'cibling' in self.relations:
+      rel_list.append(cibling)
+
+    rel = torch.stack(rel_list, dim=1)
+
+    rel_weight = self.rel_weight
+
+    dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel)
+    att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length)
+
+    return att_mask, cibling, head, block
+
+  def encode(self, x, pos, att_mask=None, context_layers=False):
+    """Structformer encoding process."""
+
+    if context_layers:
+      """Standard transformer encode process."""
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        h = h + self.pos_emb(pos)
+      h_list = []
+      visibility = self.visibility(x, x.device)
+      for i in range(self.n_context_layers):
+        h_list.append(h)
+        h = self.context_layers[i](
+            h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1)
+
+      output = h
+      h_array = torch.stack(h_list, dim=2)
+      return output
+    
+    else:
+      visibility = self.visibility(x, x.device)
+      h = self.emb(x)
+      if hasattr(self, 'pos_emb'):
+        assert pos.max() < 500
+        h = h + self.pos_emb(pos)
+      for i in range(self.nlayers):
+        h = self.layers[i](
+            h.transpose(0, 1), attn_mask=att_mask[i],
+            key_padding_mask=visibility).transpose(0, 1)
+      return h
+
+  def forward(self, input_ids, labels=None, position_ids=None, **kwargs):
+        
+    x = input_ids
+    batch_size, length = x.size()
+
+    if position_ids is None:
+      pos = torch.arange(length, device=x.device).expand(batch_size, length)
+
+    context_layers_output = None
+    if self.n_context_layers > 0:
+      context_layers_output = self.encode(x, pos, context_layers=True)    
+    
+    distance, height = self.parse(x, pos, embeds=context_layers_output)
+    att_mask, cibling, head, block = self.generate_mask(x, distance, height)
+    
+    raw_output = self.encode(x, pos, att_mask)
+    raw_output = self.norm(raw_output)
+    raw_output = self.drop(raw_output)
+
+    #output = self.output_layer(raw_output)
+    logits = self.classifier(raw_output)
+    
+    loss = None
+    if labels is not None:
+        if self.config.problem_type is None:
+            if self.num_labels == 1:
+                self.config.problem_type = "regression"
+            elif self.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.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.num_labels), labels.view(-1))
+        elif self.config.problem_type == "multi_label_classification":
+            loss_fct = BCEWithLogitsLoss()
+            loss = loss_fct(logits, labels)
+
+
+    return SequenceClassifierOutput(
+        loss=loss,
+        logits=logits,
+        hidden_states=None,
+        attentions=None,
+    )
+
+
+
+##########################################
+# HuggingFace Model
+##########################################
+class StructformerModel(PreTrainedModel):
+    config_class = StructformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act
+        )
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)
+
+
+
+class StructformerModelForSequenceClassification(PreTrainedModel):
+    config_class = StructformerConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormerClassification(
+               hidden_size=config.hidden_size,
+               n_context_layers=config.n_context_layers,
+               nlayers=config.nlayers,
+               ntokens=config.ntokens,
+               nhead=config.nhead,
+               dropout=config.dropout,
+               dropatt=config.dropatt,
+               relative_bias=config.relative_bias,
+               pos_emb=config.pos_emb,
+               pad=config.pad,
+               n_parser_layers=config.n_parser_layers,
+               conv_size=config.conv_size,
+               relations=config.relations,
+               weight_act=config.weight_act,
+               config=config)
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+                module.weight.data.fill_(1.0)
+
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        return self.model(input_ids, labels=labels, **kwargs)

finetune/control_raising_control/checkpoint-400/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "omarmomen/structformer_s1_final_with_pos",
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

finetune/control_raising_control/checkpoint-400/trainer_state.json

@@ -0,0 +1,27 @@
+{
+  "best_metric": 0.8853014990164824,
+  "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/control_raising_control/checkpoint-400",
+  "epoch": 7.2727272727272725,
+  "global_step": 400,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 7.27,
+      "eval_accuracy": 0.8736362457275391,
+      "eval_f1": 0.8853014990164824,
+      "eval_loss": 1.199914813041687,
+      "eval_mcc": 0.7674272477853503,
+      "eval_runtime": 25.9995,
+      "eval_samples_per_second": 514.702,
+      "eval_steps_per_second": 64.347,
+      "step": 400
+    }
+  ],
+  "max_steps": 550,
+  "num_train_epochs": 10,
+  "total_flos": 3989207699389440.0,
+  "trial_name": null,
+  "trial_params": null
+}

finetune/control_raising_control/checkpoint-400/training_args.bin

@@ -0,0 +1,3 @@
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+oid sha256:f1dcbfabab2b2121e41e9e1698127c1906a1da248b0530c01c7064746c08a2fc
+size 3567