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neural_network/sliding_window_attention.py

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+"""
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+Name - - Sliding Window Attention Mechanism
+Goal - - Implement a neural network architecture using sliding window attention for sequence modeling tasks.
+Detail: Total 5 layers neural network
+        * Input layer
+        * Sliding Window Attention Layer
+        * Feedforward Layer
+        * Output Layer
+Author: Stephen Lee
+Github: 245885195@qq.com
+Date: 2024.10.20
+References:
+    1. Choromanska, A., et al. (2020). "On the Importance of Initialization and Momentum in Deep Learning." *Proceedings of the 37th International Conference on Machine Learning*.
+    2. Dai, Z., et al. (2020). "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention." *arXiv preprint arXiv:2006.16236*.
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+"""
+
+import numpy as np
+
+
+class SlidingWindowAttention:
+    """Sliding Window Attention Module.
+
+    This class implements a sliding window attention mechanism where the model
+    attends to a fixed-size window of context around each token.
+
+    Attributes:
+        window_size (int): The size of the attention window.
+        embed_dim (int): The dimensionality of the input embeddings.
+    """
+
+    def __init__(self, embed_dim: int, window_size: int):
+        """
+        Initialize the SlidingWindowAttention module.
+
+        Args:
+            embed_dim (int): The dimensionality of the input embeddings.
+            window_size (int): The size of the attention window.
+        """
+        self.window_size = window_size
+        self.embed_dim = embed_dim
+        self.attention_weights = np.random.randn(embed_dim, embed_dim)
+
+    def forward(self, x: np.ndarray) -> np.ndarray:
+        """
+        Forward pass for the sliding window attention.
+
+        Args:
+            x (np.ndarray): Input tensor of shape (batch_size, seq_length, embed_dim).
+
+        Returns:
+            np.ndarray: Output tensor of shape (batch_size, seq_length, embed_dim).
+
+        >>> x = np.random.randn(2, 10, 4)  # Batch size 2, sequence length 10, embedding dimension 4
+        >>> attention = SlidingWindowAttention(embed_dim=4, window_size=3)
+        >>> output = attention.forward(x)
+        >>> output.shape
+        (2, 10, 4)
+        >>> (output.sum() != 0).item()  # Check if output is non-zero
+        True
+        """
+        batch_size, seq_length, _ = x.shape
+        output = np.zeros_like(x)
+
+        for i in range(seq_length):
+            # Define the window range
+            start = max(0, i - self.window_size // 2)
+            end = min(seq_length, i + self.window_size // 2 + 1)
+
+            # Extract the local window
+            local_window = x[:, start:end, :]
+
+            # Compute attention scores
+            attention_scores = np.matmul(local_window, self.attention_weights)
+
+            # Average the attention scores
+            output[:, i, :] = np.mean(attention_scores, axis=1)
+
+        return output
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    # Example usage
+    x = np.random.randn(
+        2, 10, 4
+    )  # Batch size 2, sequence length 10, embedding dimension 4
+    attention = SlidingWindowAttention(embed_dim=4, window_size=3)
+    output = attention.forward(x)
+    print(output)