add model

config.json

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+{
+  "architectures": [
+    "TransformerModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "structformer.TransformerConfig",
+    "AutoModelForMaskedLM": "structformer.TransformerModel"
+  },
+  "dropatt": 0.1,
+  "dropout": 0.1,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "model_type": "transformer",
+  "nhead": 8,
+  "nlayers": 8,
+  "ntokens": 16000,
+  "pad": 1,
+  "pos_emb": true,
+  "relative_bias": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f295cf6b736aeee12757ea7ecc1c32ead791a33adf69b46f60969f6cd0b87a2
+size 134778679

structformer.py

@@ -0,0 +1,1014 @@
+# coding=utf-8
+# Copyright 2023 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""StructFormer and transformer model."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput
+
+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
+
+
+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,
+              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)
+
+    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.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):
+    """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)
+
+    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):
+    """Structformer encoding process."""
+
+    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, 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()
+
+    distance, height = self.parse(x, pos)
+    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)
+
+    return output.view(batch_size * length, -1), \
+        {'raw_output': raw_output, 'distance': distance, 'height': height,
+        'cibling': cibling, 'head': head, 'block': block}
+
+
+##########################################
+# Clasication Head For BabyLM Evaluation Tasks
+##########################################
+class ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+    def __init__(self, config):
+        super(ClassificationHead, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        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
+
+##########################################
+# HuggingFace Config
+##########################################
+class StructFormerConfig(PretrainedConfig):
+    model_type = "structformer"
+
+    def __init__(
+        self,
+        hidden_size=512,
+        nlayers=8,
+        ntokens=10_000,
+        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',
+        num_labels=1,
+        hidden_dropout_prob=0.1,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        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
+        self.num_labels = num_labels
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.initializer_range=initializer_range
+        super().__init__(**kwargs)
+
+class TransformerConfig(PretrainedConfig):
+    model_type = "transformer"
+
+    def __init__(
+        self,
+        hidden_size=512,
+        nlayers=8,
+        ntokens=10_000,
+        nhead=8,
+        dropout=0.1,
+        dropatt=0.1,
+        relative_bias=False,
+        pos_emb=False,
+        pad=0,
+        num_labels=1,
+        hidden_dropout_prob=0.1,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        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.num_labels = num_labels
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.initializer_range=initializer_range
+        super().__init__(**kwargs)
+
+
+
+##########################################
+# HuggingFace Models
+##########################################
+class StructFormerModel(PreTrainedModel):
+    config_class = StructFormerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+              hidden_size=config.hidden_size,
+              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
+        )
+        self.config = config
+
+    def parse(self, input_ids, **kwargs):
+      x = input_ids
+      batch_size, length = x.size()
+      pos = kwargs['position_ids'] if 'position_ids' in kwargs.keys() else torch.arange(length, device=x.device).expand(batch_size, length)
+      
+      sf_output = self.model(x, pos)
+      
+      return sf_output[1]
+    
+    def forward(self, input_ids, labels=None, **kwargs):
+        x = input_ids
+        batch_size, length = x.size()
+        pos = kwargs['position_ids'] if 'position_ids' in kwargs.keys() else torch.arange(length, device=x.device).expand(batch_size, length)
+        
+        sf_output = self.model(x, pos)
+        
+        loss = None
+        if labels is not None:
+          loss_fct = nn.CrossEntropyLoss()
+          loss = loss_fct(sf_output[0], labels.reshape(-1))
+        
+        return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=sf_output[0].view(batch_size, length, -1), # shape: (batch_size, length, ntokens)
+            hidden_states=None,
+            attentions=None
+        )
+
+class StructFormerModelForSequenceClassification(PreTrainedModel):
+    config_class = StructFormerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+              hidden_size=config.hidden_size,
+              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
+        )
+        self.config = config
+        self.num_labels = config.num_labels
+        self.model.classifier = ClassificationHead(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):
+        x = input_ids
+        batch_size, length = x.size()
+        pos = kwargs['position_ids'] if 'position_ids' in kwargs.keys() else torch.arange(length, device=x.device).expand(batch_size, length)
+        
+        sf_output = self.model(x, pos)
+        
+        logits = self.model.classifier(sf_output[1]['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 = nn.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 = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=None,
+            attentions=None,
+        )
+
+class TransformerModel(PreTrainedModel):
+    config_class = TransformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Transformer(
+              hidden_size=config.hidden_size,
+              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
+        )
+        self.config = config
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        x = input_ids
+        batch_size, length = x.size()
+        pos = kwargs['position_ids'] if 'position_ids' in kwargs.keys() else torch.arange(length, device=x.device).expand(batch_size, length)
+        
+        sf_output = self.model(x, pos)
+        
+        loss = None
+        if labels is not None:
+          loss_fct = nn.CrossEntropyLoss()
+          loss = loss_fct(sf_output[0], labels.reshape(-1))
+        
+        return MaskedLMOutput(
+            loss=loss, # shape: 1
+            logits=sf_output[0].view(batch_size, length, -1), # shape: (batch_size, length, ntokens)
+            hidden_states=None,
+            attentions=None
+        )
+
+class TransformerModelForSequenceClassification(PreTrainedModel):
+    config_class = TransformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = StructFormer(
+              hidden_size=config.hidden_size,
+              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
+        )
+        self.config = config
+        self.num_labels = config.num_labels
+        self.model.classifier = ClassificationHead(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):
+        x = input_ids
+        batch_size, length = x.size()
+        pos = kwargs['position_ids'] if 'position_ids' in kwargs.keys() else torch.arange(length, device=x.device).expand(batch_size, length)
+        
+        sf_output = self.model(x, pos)
+        
+        logits = self.model.classifier(sf_output[1]['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 = nn.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 = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=None,
+            attentions=None,
+        )