[Cleanup] Add new section to CHANGELOG

CHANGELOG.md

@@ -1,6 +1,10 @@
 # CHANGELOG
 
-## 0.1
+## 0.2.0
+
+- Add `Attention` base class, `MultiHeadAttention`, and `ScaledDotProductAttention` classes.
+
+## 0.1.0
 
 - Move all content of `__init__.py` files to sub-modules.
 - Add `Trainer` class to replace `operator.fit` method.
@@ -24,7 +28,6 @@
 - Add `benchmarks` infrastructure.
 - An `Operator` now takes a `device` argument.
 - Add `QuantileScaler` class.
-- Add `Attention` base class, `MultiHeadAttention`, and `ScaledDotProductAttention` classes.
 
 ## 0.0.0 (2024-02-22)
 

src/continuiti/networks/attention.py

@@ -12,10 +12,10 @@
 class Attention(nn.Module):
     """Base class for various attention implementations.
 
-    Attention assigns different parts of an input varying importance without set kernels. The importance of different
-    components is designated using "soft" weights. These weights are assigned according to specific algorithms (e.g.
+    Attention assigns different parts of an input varying importance without set
+    kernels. The importance of different components is designated using "soft"
+    weights. These weights are assigned according to specific algorithms (e.g.
     scaled-dot-product attention).
-
     """
 
     def __init__(self):
@@ -26,7 +26,7 @@ def forward(
         self,
         query: torch.Tensor,
         key: torch.Tensor,
-        value: torch,
+        value: torch.Tensor,
         attn_mask: torch.Tensor = None,
     ) -> torch.Tensor:
         """Calculates the attention scores.

src/continuiti/networks/multi_head_attention.py

@@ -14,9 +14,11 @@
 class MultiHeadAttention(Attention):
     r"""Multi-Head Attention module.
 
-    Module as described in the paper [Attention is All you Need](https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)
-    with optional bias for the projections. This implementation allows to use attention implementations other than the
-    standard scaled dot product attention implemented by the MultiheadAttention PyTorch module.
+    Module as described in the paper [Attention is All you
+    Need](https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)
+    with optional bias for the projections. This implementation allows to use
+    attention implementations other than the standard scaled dot product
+    attention implemented by the MultiheadAttention PyTorch module.
 
     $$MultiHead(Q,K,V)=Concat(head_1,\dots,head_n)W^O + b^O$$
 
@@ -67,7 +69,7 @@ def forward(
         self,
         query: torch.Tensor,
         key: torch.Tensor,
-        value: torch,
+        value: torch.Tensor,
         attn_mask: torch.Tensor = None,
     ) -> torch.Tensor:
         r"""Compute the attention scores.

src/continuiti/networks/scaled_dot_product_attention.py

@@ -12,10 +12,12 @@
 class ScaledDotProductAttention(Attention):
     """Scaled dot product attention module.
 
-    This module is a wrapper for the torch implementation of the scaled dot product attention mechanism as described in
-    the paper "Attention Is All You Need" by Vaswani et al. (2017). This attention mechanism computes the attention
-    weights based on the dot product of the query and key matrices, scaled by the square root of the dimension of the
-    key vectors. The weights are then applied to the value vectors to obtain the final output.
+    This module is a wrapper for the torch implementation of the scaled dot
+    product attention mechanism as described in the paper "Attention Is All You
+    Need" by Vaswani et al. (2017). This attention mechanism computes the
+    attention weights based on the dot product of the query and key matrices,
+    scaled by the square root of the dimension of the key vectors. The weights
+    are then applied to the value vectors to obtain the final output.
     """
 
     def __init__(self, dropout_p: float = 0.0):
@@ -26,13 +28,10 @@ def forward(
         self,
         query: torch.Tensor,
         key: torch.Tensor,
-        value: torch,
+        value: torch.Tensor,
         attn_mask: torch.Tensor = None,
     ) -> torch.Tensor:
-        if self.training:
-            dropout_p = self.dropout_p
-        else:
-            dropout_p = 0.0
+        dropout_p = self.dropout_p if self.training else 0.0
         return scaled_dot_product_attention(
             query=query,
             key=key,