Comments (2)
In Mamba B and C are functions of x (the first part of the xz variable). To create a B and C, I'm guessing they use x_proj to get something like this B, C, delta = x_proj(x)
, which means that B, C, and delta are input dependent. If you want them to be static, i.e. not time-variant, then you can set them to parameters like in the S4 papers. However, the paper abalates that selectivity is important for language so it would be best to leave them as functions of x
if the downstream task in language.
That being said, their cuda kernel does technically allow B and C to be static and not inputs of x
, but again this has been shown to have worse downstream performance.
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By default, B and C are the results of x_proj(x) (i.e. B and C are input-dependent), which is done inside mamba_inner_fn
. If you pass sth not None to mamba_inner_fn, those would be used instead. It might work, but that code path is used extensively and as a result not as well-tested.
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Related Issues (20)
- RuntimeError: CUDA error: unknown error
- 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
- Mamba for encoder-decoder HOT 2
- mamba_split_conv1d_scan_combined error HOT 1
- torch.cuda.amp.custom_fwd(args...) Deprecated
- Is it possible to get ssm_states at specific seq len instead of just the last state? HOT 1
- 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)
- Vanishing gradient problem with more layer HOT 1
- Results vary greatly across experiments
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