\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
This is self attention with only "1 head" (like having only 1 conv filter)
\mathbf v^{\textcolor{skyblue}{(h)}} (\mathbf x_i) = W^T_{v, \textcolor{skyblue}{h}} \mathbf x_i, \quad \mathbf q^{\textcolor{skyblue}{(h)}} (\mathbf x_i) = W^T_{q, \textcolor{skyblue}{h}} \mathbf x_i, \quad \mathbf k^{\textcolor{skyblue}{(h)}} (\mathbf x_i) = W^T_{k, \textcolor{skyblue}{h}} \mathbf x_i
\alpha_{i, j}^{\textcolor{skyblue}{(h)}} = \text{softmax}_j \left( \frac{\langle \mathbf q^{\textcolor{skyblue}{(h)}}(\mathbf x_i),\: \mathbf k^{\textcolor{skyblue}{(h)}}(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \textcolor{skyblue}{\sum_{h=1}^{H} W^T_{c, h}} \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j) ^{\textcolor{skyblue}{(h)}}
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
Multi-headed attention with H heads (similar to conv. filter dimension)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_{v, \textcolor{skyblue}{h}}, W_{q, \textcolor{skyblue}{h}}, W_{k, \textcolor{skyblue}{h}} \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query for each head.
Attention weights calculate how similar the query of each position is to the keys of all other positions for each head.
For each position and each head, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
\mathbf v (\mathbf x_i) = W^T_v \mathbf x_i, \quad \mathbf q (\mathbf x_i) = W^T_q \mathbf x_i, \quad \mathbf k (\mathbf x_i) = W^T_k \mathbf x_i
\alpha_{i, j} = \text{softmax}_j \left( \frac{\langle \mathbf q(\mathbf x_i),\: \mathbf k(\mathbf x_j) \rangle}{\sqrt{d'}} \right)
\mathbf u_i = \sum_{j=1}^{n} \alpha_{i, j} \mathbf v(\mathbf x_j)
\mathbf u_i' = \text{LayerNorm}(x_i + \mathbf u_i; \gamma_1, \beta_1)
\mathbf z_i = W_2^T \text{ReLU} (W_1^T \mathbf u_i')
\mathbf z_i' = \text{LayerNorm}(\mathbf u_i' + \mathbf z_i; \gamma_2, \beta_2)
This block constitutes self-attention. In ordinary attention, keys/queries take different inputs.
W_v, W_q, W_k \in \mathbb R^{d \times d'}
\text{LayerNorm}(\mathbf z; \mathbf \gamma, \mathbf \beta) = \mathbf \gamma \frac{\mathbf z - \mathbf \mu_z}{\sigma_z} + \mathbf \beta
Each position gets a value, key, and query vector
Attention weights calculate how similar the query of each position is to the keys of all other positions
For each position, value vectors are linearly combined weighted by attention.
Add output to residual input + normalize
Feed through linear / nonlinearity
Add another residual connection + normalize
Note: often add positional encoding into x
Transformer block maps
\mathbf x \in \mathbb R^{n \times d} \to \mathbb R^{n \times d}
Self-attention cheat-sheet
By Chandan Singh
Self-attention cheat-sheet
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