Answers.Body: <p>In many NLP tasks information is intrinsically structured hierarchically, for instance, early layers in a transformer model may specialize in short-range dependencies such as syntactic relationships between adjacent tokens, while deeper layers specialize in long-range dependencies like semantic coherence across sentences or paragraphs. Multiple transformer blocks allow the model to perform a hierarchical transformation of the input at different levels of contextualized abstraction. In contrast, a single transformer block, even with many attention heads, would not have the same depth to specialize attention in a single representation space and iteratively capture the nuanced and hierarchical dependencies with only the same raw input embeddings at once.</p>
  <p>In addition, in a single transformer block you only get one application of the feed-forward network (FFN), which reduces the model's capacity to model complex non-linear relationships, even if there are more attention heads. Multiple blocks with interleaving FFNs allow the model to compute various non-linear combinations of the relationships captured by attention.</p>
  <p>Lastly transformers rely heavily on residual connections between blocks which allow the model to avoid vanishing gradients and enable better learning by passing information from earlier layers directly to later layers. With only one block, you'd lose the advantage of having these residual connections between different hierarchical representations. This could impair learning and result in suboptimal training, especially when dealing with complex patterns.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-10-13T07:34:26.863Z
Answers.LastActivityDate: 2024-10-13T07:34:26.863Z
Answers.UserDisplayName: cinch
Body: <p>In transformers model, we have multiple transformer blocks each with multiple attention heads.</p>
  <p>So assume a model has <code>N</code> transformer blocks with each having <code>M</code> attention heads, why don't we just use a single transformer block with <code>NxM</code> attention heads ?</p>
  <p>That will also have approximately same number of parameters ?</p>
  
Tags: <transformer><deep-neural-networks><attention>
Title: Why not increase the number of attention heads rather than stacking transformer layers?
UserDisplayName: Bhavesh Achhada
ThreadText: # Why not increase the number of attention heads rather than stacking transformer layers?
  
  (Asked by: username_0 on 2024-10-13)
  
  In transformers model, we have multiple transformer blocks each with multiple attention heads.
  
  So assume a model has `N` transformer blocks with each having `M` attention heads, why don't we just use a single transformer block with `NxM` attention heads ?
  
  That will also have approximately same number of parameters ?
  
  **Tags:** transformer, deep-neural-networks, attention
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-10-13. Score: 4 | Accepted answer
  
  In many NLP tasks information is intrinsically structured hierarchically, for instance, early layers in a transformer model may specialize in short-range dependencies such as syntactic relationships between adjacent tokens, while deeper layers specialize in long-range dependencies like semantic coherence across sentences or paragraphs. Multiple transformer blocks allow the model to perform a hierarchical transformation of the input at different levels of contextualized abstraction. In contrast, a single transformer block, even with many attention heads, would not have the same depth to specialize attention in a single representation space and iteratively capture the nuanced and hierarchical dependencies with only the same raw input embeddings at once.
  
  In addition, in a single transformer block you only get one application of the feed-forward network (FFN), which reduces the model's capacity to model complex non-linear relationships, even if there are more attention heads. Multiple blocks with interleaving FFNs allow the model to compute various non-linear combinations of the relationships captured by attention.
  
  Lastly transformers rely heavily on residual connections between blocks which allow the model to avoid vanishing gradients and enable better learning by passing information from earlier layers directly to later layers. With only one block, you'd lose the advantage of having these residual connections between different hierarchical representations. This could impair learning and result in suboptimal training, especially when dealing with complex patterns.
  
  
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