Practical overview
When it fits
Consider it when building a PyTorch model and you want to experiment with sparse rather than fully dense attention. Review the repository implementation for complete setup and integration details.
Who it is for
- PyTorch developers integrating attention components into machine-learning models.
- Transformer researchers and developers exploring sparse attention for longer input sequences.
Documented uses
- Add a sparse-attention component to a PyTorch model.
- Prototype attention using query, key and value tensors with documented attention modes.
- Explore block-sparse attention using a configurable block size.
Requirements & limits
Repository metadata identifies the project as public and MIT-licensed.
- The SparseAttention class accepts query, key and value tensors and uses a mask to define sparsity.
- Block size controls the granularity of sparsity; the README example uses 32.
- The documented attention modes are “all,” “local” and “strided.”
- The README example includes an incomplete function call, and the available documentation does not specify performance results or deployment targets.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Use SparseAttention in a PyTorch example
Apply the documented SparseAttention module to query, key, and value tensors.
Before you start
- PyTorch, imported as
torch. - Query, key, and value tensors with batch, sequence, and embedding dimensions.
Steps
Create the module with
sparse_attn = SparseAttention(block_size=32).Create query, key, and value tensors; the example uses shape
(4, 1024, 256)for each.Run the forward pass with
output = sparse_attn(q, k, v).Use
outputas the attention module's result.
Expected outcome
The example produces an output by passing the three tensors through the SparseAttention module.
Guide limitations
- The README does not show the import path for
SparseAttention. - The example does not document tensor constraints, output shape, or test results.
Call the documented attention implementation functions
Use the documented dense or block-sparse attention function with query, key, and value tensors.
Before you start
- PyTorch, imported as
torch. - Query, key, and value tensors; the example uses shape
(4, 1024, 256).
Steps
Create query, key, and value tensors as shown in the README example.
Call
attention_impl(q, k, v, heads=4, attn_mode="all")for the documented dense attention example.Alternatively, call
blocksparse_attention_impl(q, k, v, heads=4, attn_mode="all", blocksize=32)for the documented block-sparse example.Use the returned value as the respective function's attention output.
Expected outcome
The example assigns results from the dense and block-sparse attention functions to output variables.
Guide limitations
- The README example contains an incomplete
attention_implline before its complete call. - The import paths, supported tensor constraints, output shape, and function behavior beyond the example are not specified.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 12 October 2026
- Original website title
- kyegomez/SparseAttention · Official homepage title · retained in its source language