Add model skeleton

5 files changed, 92 insertions, 0 deletions

diff --git a/model/__init__.py b/model/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/model/__init__.py
diff --git a/model/attention.py b/model/attention.py
new file mode 100644
index 0000000..d82ca17
--- /dev/null
+++ b/model/attention.py
@@ -0,0 +1,15 @@
+from torch import nn
+
+class Attention(nn.Module):
+  def __init__(self, hidden_dim, num_heads):
+    super(Attention, self).__init__()
+    # TODO: implement Attention
+    pass
+
+  def forward(self, x):
+    # TODO: implement Attention; return both result of attention mechanism and
+    # attention weights (for visualization).
+    # x shape: (seqlen, batch, hiddendim)
+    result, att_weights = x, None # placeholder
+    pass
+    return result, att_weights
diff --git a/model/encoder.py b/model/encoder.py
new file mode 100644
index 0000000..63a5149
--- /dev/null
+++ b/model/encoder.py
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+import torch
+from torch import nn
+
+from util.util import get_positional_encoding
+from model.encoder_layer import EncoderLayer
+
+class EncoderModel(nn.Module):
+  def __init__(self, input_dim, hidden_dim, d_ff, output_dim, n_layers,
+               num_heads, use_attention=True, use_feedforward=True,
+               use_positional=True, device='cpu'):
+    super(EncoderModel, self).__init__()
+    self._device = device
+    self._use_positional = use_positional
+    self.embedding_layer = nn.Embedding(input_dim, hidden_dim)
+    self.layers = nn.ModuleList([
+          EncoderLayer(hidden_dim, d_ff, num_heads, use_attention,
+                       use_feedforward) for i in range(n_layers)])
+    self.output_layer = nn.Linear(hidden_dim, output_dim)
+
+  def forward(self, x, return_att_weights=False):
+    # x shape: (seqlen, batch)
+    hidden = self.embedding_layer(x)
+    # hidden shape: (seqlen, batch, hiddendim)
+
+    if self._use_positional:
+      positional_encoding = get_positional_encoding(
+          n_positions=hidden.shape[0],
+          n_dimensions=hidden.shape[-1],
+          device=self._device
+        )
+      # reshaping to (seqlen, 1, hiddendim)
+      positional_encoding = torch.reshape(
+          positional_encoding,
+          (hidden.shape[0], 1, hidden.shape[-1])
+      )
+      hidden = hidden + positional_encoding
+
+    list_att_weights = []
+    for layer in self.layers:
+      hidden, att_weights = layer(hidden)
+      list_att_weights.append(att_weights)
+
+    result = self.output_layer(hidden)
+
+    if return_att_weights:
+      return result, list_att_weights
+    else:
+      return result
diff --git a/model/encoder_layer.py b/model/encoder_layer.py
new file mode 100644
index 0000000..56a3a0c
--- /dev/null
+++ b/model/encoder_layer.py
@@ -0,0 +1,15 @@
+from torch import nn
+
+class EncoderLayer(nn.Module):
+  def __init__(self, hidden_dim, d_ff, num_heads, use_attention=True,
+               use_feedforward=True):
+    super(EncoderLayer, self).__init__()
+    # TODO: implement a single encoder layer, using Attention and FeedForward.
+    pass
+
+  def forward(self, x):
+    # x shape: (seqlen, batch, hiddendim)
+    # TODO: implement a single encoder layer, using Attention and FeedForward.
+    result, att_weights = x, None # placeholder
+    pass
+    return result, att_weights
diff --git a/model/forward.py b/model/forward.py
new file mode 100644
index 0000000..63ec6ea
--- /dev/null
+++ b/model/forward.py
@@ -0,0 +1,14 @@
+from torch import nn
+
+class FeedForward(nn.Module):
+  def __init__(self, hidden_dim, d_ff):
+    super(FeedForward, self).__init__()
+    # TODO: implement FeedForward layer
+    pass
+
+  def forward(self, x):
+    # TODO: implement
+    # x shape: (seqlen, batch, hiddendim)
+    result = x # placeholder
+    pass
+    return result