graph LR
Input_Data["Input Data"]
Dense_Attention_Module["Dense Attention Module"]
Sparse_Attention_Module["Sparse Attention Module"]
Function_Configuration_Optimization["Function Configuration & Optimization"]
Output_Data["Output Data"]
Input_Data -- "Feeds Data" --> Dense_Attention_Module
Input_Data -- "Feeds Data" --> Sparse_Attention_Module
Function_Configuration_Optimization -- "Configures/Initializes" --> Dense_Attention_Module
Function_Configuration_Optimization -- "Configures/Initializes" --> Sparse_Attention_Module
Dense_Attention_Module -- "Produces Result" --> Output_Data
Sparse_Attention_Module -- "Produces Result" --> Output_Data
click Dense_Attention_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sparse_attention/Dense_Attention_Module.md" "Details"
click Sparse_Attention_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sparse_attention/Sparse_Attention_Module.md" "Details"
click Function_Configuration_Optimization href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sparse_attention/Function_Configuration_Optimization.md" "Details"
The sparse_attention project implements a flexible attention mechanism, capable of both dense and sparse computations. At its core, the system processes Input Data through either a Dense Attention Module or a Sparse Attention Module, with the choice and configuration dynamically managed by the Function Configuration & Optimization component. This configuration component leverages meta-programming to build and optimize the attention functions based on specific requirements, ensuring efficient computation. Both attention modules then produce Output Data, representing the transformed tensors. The architecture emphasizes modularity, allowing for easy integration of different attention implementations and dynamic optimization based on the computational context.
Represents the raw input tensors or sequences fed into the attention mechanism. These are typically the query, key, and value tensors.
Related Classes/Methods:
Dense Attention Module [Expand]
Implements the standard, full multi-head attention logic, including splitting/merging heads, masking, scaling, and core matrix multiplications.
Related Classes/Methods:
attention.attention_impl:69-87attention.split_heads:42-43attention.merge_heads:46-47attention.get_attn_mask:8-29
Sparse Attention Module [Expand]
Provides an optimized, sparse attention implementation leveraging specialized hardware (e.g., NVIDIA CUDA via OpenAI blocksparse library) for computational efficiency.
Related Classes/Methods:
attention.blocksparse_attention_impl:90-111attention.strided_transpose:32-39attention.get_blocksparse_obj:114-182attention.get_callback:185-212
Function Configuration & Optimization [Expand]
A meta-programming component responsible for dynamically building, configuring, and potentially optimizing specialized attention functions or computational graphs based on input parameters.
Related Classes/Methods:
Represents the processed tensors or sequences resulting from the attention computation.
Related Classes/Methods: