Comments (4)
Triton did not support some of the operations. In particular their associative scan function is quite limited right now (and sometimes buggy / difficult to use); on the other hand CUDA has quite sophisticated support for scans (https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda).
I think it should be possible in principle to implement something similar in Pallas, but I don't know how efficient it would be.
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Triton does have associative scan, but (1) it only supports scan with 1 float instead of a pair of floats, which is what we need (2) it doesn't have reverse scan yet (needed for the backward pass).
Does Pallas have those?
When Triton has more robust support for scan, it would be simpler to write it in Triton.
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Ideally it would be nice to be truly hardware agnostic, in the spirit of the Mamba mission statement. Tying the architecture to CUDA may not be the best way.
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Thanks for the response @tridao & @albertfgu, indeed Triton doesn't have the support, and Pallas wouldn't be much use here as it lowers to Triton. We'll work on a JAX interface to the CUDA kernels.
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Related Issues (20)
- How to calculate Flops for mamba_chunk_scan_combined and mamba_split_conv1d_scan_combined function HOT 1
- Replace mamba1 with mamba2 and training becomes very slow! HOT 2
- Implement a "crossmamba" block for different length sequences HOT 3
- How to use sequence parallelism in Mamba2?
- Changing expansion_factor resulted in 'c10::DistBackendError HOT 1
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- mamba_split_conv1d_scan_combined error HOT 1
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- How to modify mambaV2 to set causal=False? HOT 3
- Reducing _chunk_scan_bwd_kernel computation HOT 4
- Exploding gradients if ngroups is higher than 1. HOT 3
- clarification on how to interpret kernel size for conv1d HOT 1
- Some questions about the shape of A,B,C,D HOT 5
- How can I avoid using causal-conv1d? HOT 11
- Is it okay to put S6(MAMBA) and gated MLP blocks like a transformer? (Also please open the discussion tab)
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