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This question is commonly asked for Machine Learning Engineer candidates at Google during technical interviews.

Implement a Transformer Block with SwiGLU

Quick Overview

This question evaluates implementation and reasoning skills for Transformer internals, testing competency in multi-head self-attention, SwiGLU gated feed-forward layers, residual connections, and layer normalization within the Machine Learning / deep learning domain.

Company: Google

Role: Machine Learning Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Implement a Transformer-style neural network block in Python using either NumPy or PyTorch. Your implementation should include: 1. **Multi-head self-attention** - Input tensor shape: `(batch_size, sequence_length, hidden_dim)`. - Split the hidden dimension into `num_heads` attention heads. - Compute scaled dot-product attention. - Support an optional attention mask. - Concatenate heads and apply an output projection. 2. **SwiGLU feed-forward network** - Implement a gated feed-forward layer using the SwiGLU formulation: `SwiGLU(x) = Swish(xW_gate) * (xW_value)`, followed by an output projection. - The gate and value projections should be computed in parallel. - Use `Swish(z) = z * sigmoid(z)`. 3. **Transformer block structure** - Include residual connections. - Include layer normalization. - Return an output tensor with the same shape as the input. Be prepared to explain tensor shapes, masking behavior, and the time and memory complexity of multi-head attention.

Quick Answer: This question evaluates implementation and reasoning skills for Transformer internals, testing competency in multi-head self-attention, SwiGLU gated feed-forward layers, residual connections, and layer normalization within the Machine Learning / deep learning domain.

Implement a Transformer-style block in Python using NumPy. Do not use high-level attention or feed-forward APIs. The block must follow this pre-normalization structure: 1. y = x + MultiHeadSelfAttention(LN1(x)) 2. out = y + SwiGLUFeedForward(LN2(y)) Details: - Input tensor x has shape (batch_size, sequence_length, hidden_dim). - Multi-head self-attention must: - project x into Q, K, V using weight matrices, - split hidden_dim evenly across num_heads, - compute scaled dot-product attention, - apply an optional mask before softmax, - concatenate all heads, - apply the output projection. - The SwiGLU feed-forward network must compute: - gate = x @ W_gate - value = x @ W_value - swish(gate) = gate * sigmoid(gate) - hidden = swish(gate) * value - output = hidden @ W_ffn_out - Layer normalization is applied over the last dimension only. - For deterministic grading, use layer normalization without epsilon: LN(t) = ((t - mean(t)) / sqrt(var(t))) * gamma + beta All test inputs are chosen so var(t) > 0 for every token. - There are no bias terms and no dropout. - The returned tensor must have the same shape as the input and each value must be rounded to 6 decimal places.

Constraints

1 <= batch_size <= 10
1 <= sequence_length <= 30
1 <= hidden_dim <= 64
hidden_dim % num_heads == 0
w_q, w_k, w_v, w_o are all (hidden_dim x hidden_dim)
w_gate and w_value have the same shape (hidden_dim x ffn_dim)
w_ffn_out has shape (ffn_dim x hidden_dim)
Every token vector has non-zero variance across the hidden dimension
If a mask is provided, every query position has at least one unmasked key

Examples

Input: ([[[1, 0], [0, 1]]], 1, [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[0, 0], [0, 0]], [[0, 0], [0, 0]], [[0, 0], [0, 0]], [1, 1], [0, 0], [1, 1], [0, 0], None)

Expected Output: [[[1.888386, -0.888386], [-0.888386, 1.888386]]]

Explanation: Layer normalization turns the two tokens into [1, -1] and [-1, 1]. With identity Q/K/V/O and one head, each token attends mostly to itself. The feed-forward part is zero, so only the attention residual changes the input.

Input: ([[[1, 0], [0, 1]]], 1, [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]], [[0, 0], [0, 0]], [[0, 0], [0, 0]], [[0, 0], [0, 0]], [1, 1], [0, 0], [1, 1], [0, 0], [[1, 0], [1, 1]])

Expected Output: [[[2.0, -1.0], [-0.888386, 1.888386]]]

Explanation: The mask blocks token 0 from attending to token 1, so token 0 attends only to itself and adds [1, -1] to the residual. Token 1 can still attend to both positions.

Hints

After computing Q, K, and V with shape (B, T, D), reshape each to (B, T, H, D/H) and transpose to (B, H, T, D/H) so attention is computed independently per head.
Apply the mask to the attention score matrix before softmax by replacing blocked positions with a very large negative number. For SwiGLU, compute the gate and value projections from the same normalized input, then use swish(gate) * value.

Quick Overview