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neural_network/sliding_window_attention.py
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@ -1,8 +1,8 @@
"""
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - sliding_window_attention.py
Goal - - Implement a neural network architecture using sliding window attention for sequence
modeling tasks.
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
@ -12,10 +12,12 @@ 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*.
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*.
3. [Attention Mechanisms in Neural Networks](https://en.wikipedia.org/wiki/Attention_(machine_learning))
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""
@ -26,8 +28,8 @@ 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.
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.
@ -52,13 +54,13 @@ class SlidingWindowAttention:
Forward pass for the sliding window attention.
Args:
input_tensor (np.ndarray): Input tensor of shape (batch_size, seq_length,
embed_dim).
input_tensor (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
>>> x = np.random.randn(2, 10, 4) # Batch size 2, sequence
>>> attention = SlidingWindowAttention(embed_dim=4, window_size=3)
>>> output = attention.forward(x)
>>> output.shape
@ -93,9 +95,7 @@ if __name__ == "__main__":
# usage
rng = np.random.default_rng()
x = rng.standard_normal(
(2, 10, 4)
) # Batch size 2, sequence length 10, embedding dimension 4
x = rng.standard_normal((2, 10, 4)) # Batch size 2,
attention = SlidingWindowAttention(embed_dim=4, window_size=3)
output = attention.forward(x)
print(output)