[FEA] Structured sparsity for convolution
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- Dominant language
- C++
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Description
GEMM currently supports 50% structured sparsity on ampere as in example 15_ampere_sparse_tensorop_gemm. Is there a way to use this GEMM to power a conv2d? I have not been able to find any implementations of sparse tensorop conv2d except for those built in to TensorRT. CuDNN does not support sparse weights, having this feature in cutlass would be very useful.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by studying the 15_ampere_sparse_tensorop_gemm example to understand the existing 50% structured-sparsity support. Compare that capability with the requested conv2d use case and the TensorRT implementations mentioned in the issue. Done means determining and implementing a CUTLASS structured-sparse convolution path, or documenting why the GEMM approach cannot support it.
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Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100