Dense_Attention_Module.md

graph LR
    Dense_Attention_Module["Dense Attention Module"]
    attention_attention_impl["attention.attention_impl"]
    attention_split_heads["attention.split_heads"]
    attention_merge_heads["attention.merge_heads"]
    attention_get_attn_mask["attention.get_attn_mask"]
    Dense_Attention_Module -- "orchestrates" --> attention_attention_impl
    attention_attention_impl -- "calls" --> attention_split_heads
    attention_attention_impl -- "calls" --> attention_get_attn_mask
    attention_attention_impl -- "calls" --> attention_merge_heads
    click Dense_Attention_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sparse_attention/Dense_Attention_Module.md" "Details"

Loading

Details

This section details the architecture of the Dense Attention Module subsystem, a core component within the sparse_attention project responsible for implementing standard, full multi-head attention.

Dense Attention Module [Expand]

This is the overarching conceptual component that orchestrates the entire standard multi-head attention process. It encompasses the input preparation, core matrix multiplications, and output consolidation, leveraging specialized sub-components for specific tasks.

Related Classes/Methods:

• attention.attention_impl:69-87
• attention.split_heads:42-43
• attention.merge_heads:46-47
• attention.get_attn_mask:8-29

attention.attention_impl

Implements the core computational flow of the dense multi-head attention. It manages the sequence of operations: calling split_heads, generating masks via get_attn_mask, performing the query-key dot product, applying softmax, and finally the attention-value dot product, before calling merge_heads.

Related Classes/Methods:

• attention.attention_impl:69-87

attention.split_heads

Prepares input tensors (queries, keys, values) for multi-head processing by reshaping them to explicitly separate the attention heads. This is a crucial data transformation step before the core attention calculations.

Related Classes/Methods:

• attention.split_heads:42-43

attention.merge_heads

Recombines the outputs from individual attention heads back into a single tensor, reversing the split_heads operation. This produces the final, consolidated output of the attention layer.

Related Classes/Methods:

• attention.merge_heads:46-47

attention.get_attn_mask

Generates attention masks (e.g., causal masks) to control information flow during attention calculation, preventing attention to future positions or padded tokens. This ensures adherence to sequence modeling constraints.

Related Classes/Methods:

• attention.get_attn_mask:8-29

FAQ