Update structformer.py

structformer.py

@@ -18,12 +18,262 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
-import layers
-
+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."""