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Attention Pooling to a Single Token

EasyVision-Language Models 📚 VLM 1: Vision-Language Models

Problem Statement

The simplest bridge is attention pooling: collapse many patch features into one vector using a set of attention weights. Compute the weighted average of the patch features.

Background

Given patch features X (N x D) and attention weights w (length N, already summing to 1), the pooled token is

$p = \sum_{i} w_i\, X_i$

a convex combination of the rows. This is exactly what a one-query attention layer produces after its softmax.

Your Task

Implement:

def attention_pool(X, w):

Return the pooled D-vector as a list rounded to 4 decimals.

Input Format

X: N x D nested list.
w: list of N weights summing to 1.

Output Format

A list of D floats rounded to 4 decimals.

Sample

print(attention_pool([[1.0, 2.0], [3.0, 4.0]], [0.5, 0.5]))

Output:

[2.0, 3.0]

Example:

Input:

print(attention_pool([[1.0, 2.0], [3.0, 4.0]], [0.5, 0.5]))

Output:

[2.0, 3.0]

Reasoning:

Identify the patch features $X$ as a $2 \times 2$ matrix with rows $[1.0, 2.0]$ and $[3.0, 4.0]$ , and the attention weights $w$ as $[0.5, 0.5]$ .
Compute the pooled value for the first dimension by taking the weighted sum of the first column: $0.5 \times 1.0 + 0.5 \times 3.0 = 0.5 + 1.5 = 2.0$ .
Compute the pooled value for the second dimension by taking the weighted sum of the second column: $0.5 \times 2.0 + 0.5 \times 4.0 = 1.0 + 2.0 = 3.0$ .
Combine these results into the vector $[2.0, 3.0]$ and round each element to 4 decimal places, which leaves the values unchanged.
The final output is [2.0, 3.0]

Constraints:

1 <= N <= 5000, 1 <= D <= 4096.
w is a valid distribution over the N rows.
Round every entry to 4 decimals; avoid -0.0.

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1. Background Knowledge

Attention pooling is the simplest form of a bridge module used in Vision-Language Models (VLMs) to reduce a sequence of patch features into a single representation. In a standard Vision Transformer, an image is split into $N$ patches, each encoded into a $D$ -dimensional vector. To feed this into a language model that expects a single token, we compute a weighted average of all patch features. This operation is mathematically equivalent to a one-query attention mechanism: the query attends to all keys, and the softmax-normalized attention scores become the weights $w_i$ .

The core operation is a convex combination. Given patch features $X \in \mathbb{R}^{N \times D}$ and weights $w \in \mathbb{R}^N$ where $\sum_{i=0}^{N-1} w_i = 1$ , the pooled vector is:

p = \sum_{i=0}^{N-1} w_i X_i

Each element $p_j$ of the output is the weighted sum of the $j$ -th column of $X$ . This is a linear operation that preserves the dimensionality $D$ while collapsing the sequence length $N$ to 1.

In the context of Q-Former and other bridge modules, attention pooling serves as the baseline. More complex bridges use cross-attention with learnable queries, but the fundamental idea remains: project multiple visual tokens into a compact representation that the language model can consume.

2. Algorithm Approach

This is a straightforward weighted sum computation. The approach is:

Initialize an accumulator vector of length $D$ with zeros.
For each patch $i$ (from $0$ to $N-1$ ), multiply the entire row $X[i]$ by the scalar weight $w[i]$ .
Add the scaled row to the accumulator.
After processing all patches, round each element to 4 decimal places.

This can be implemented with nested loops or vectorized operations. Since the problem specifies Python lists, a nested loop approach is natural and efficient enough for typical input sizes.

3. Step-by-Step Strategy

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solution.py

Python 3.13

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