To calculate the number of parameters in a BERT model with the provided configuration, we need to consider the following components:

1. Embedding Layer:

   • BERT uses two embeddings:
     • Token embeddings: Each token in the vocabulary (size 40k) is mapped to a 768-dimensional vector.
     • Positional embeddings: BERT takes a maximum of 512 tokens as input, so we need 512 positional embeddings, each of size 768.
     • Segment embeddings: These are typically used to differentiate between two sentences, each of size 768. Since there are two segments, we need 2 segment embeddings.

   The number of parameters in the embedding layer: $\text{Token embeddings} = 40000 \times 768 = 30,720,000$ $\text{Position embeddings} = 512 \times 768 = 393,216$ $\text{Segment embeddings} = 2 \times 768 = 1,536$ Total embedding parameters: $30,720,000 + 393,216 + 1,536 = 31,114,752$

2. Self-Attention Layers: BERT uses multi-head self-attention, where each head projects the hidden state into key, query, and value vectors. Since there are 8 heads, the size of the key, query, and value vectors for each head is $\frac{768}{8} = 96$.

   • Q, K, V projections: For each attention head, the projection from the hidden state (768 dimensions) to the key, query, and value vectors requires: $\text{Q/K/V projections} = 768 \times 768 = 589,824 \text{ (for each projection)}$ There are 3 such projections (for Q, K, and V) per attention head, and 8 attention heads in total, so: $3 \times 768 \times 768 = 1,769,472$ Total for all heads: $1,769,472 \times 8 = 14,155,776$

   • Output projection: After concatenating the results of the attention heads, the combined vector (768 dimensions) is projected back to a 768-dimensional space: $\text{Output projection} = 768 \times 768 = 589,824$

   Total attention parameters per layer: $14,155,776 + 589,824 = 14,745,600$

3. Feed-forward layers: Each transformer layer has a feed-forward network consisting of two linear transformations with a ReLU activation in between. The size of the hidden layer is 3072: $\text{First layer} = 768 \times 3072 = 2,359,296$ $\text{Second layer} = 3072 \times 768 = 2,359,296$ Total feed-forward parameters per layer: $2,359,296 + 2,359,296 = 4,718,592$

4. Total parameters per transformer layer: Each transformer layer consists of the self-attention mechanism and the feed-forward layers: $\text{Parameters per layer} = 14,745,600 + 4,718,592 = 19,464,192$

5. Total parameters across 8 layers: Since the model has 8 transformer layers: $\text{Total for 8 layers} = 19,464,192 \times 8 = 155,713,536$

6. Total Parameters of the Model: Now, summing the parameters from the embedding layer and the 8 transformer layers: $\text{Total parameters} = 31,114,752 + 155,713,536 = 186,828,288$

Thus, the total number of parameters in the BERT model is 186,828,288.

Would you like more details or have any questions?

Related Questions:

1. How does multi-head attention work in a transformer model?
2. What is the role of positional embeddings in BERT?
3. Can you explain the difference between token and segment embeddings in BERT?
4. How does the size of the hidden state affect the number of parameters in BERT?
5. Why is the feed-forward hidden layer dimension 3072 in BERT?

Tip:

When calculating model parameters, always carefully distinguish between layer dimensions (e.g., hidden state size, embedding size) and the number of heads or layers, as these significantly impact the parameter count.