Comments (6)
In the fused fast implementations, the inner modules might not be getting called directly. Instead, we use their weights (e.g. backbone.layers.0.mixer.conv1d.weight
) and pass it into a different function (e.g. from the causal-conv1d
package).
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I tried setting the fast path variable of the layers to false but I still couldn't get it to work. Is it even possible in the pretained versions?
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Is it even possible in the pretained versions?
It should be. Pretrained versions just give a set of weights which are the same no matter the computation path.
I tried setting the fast path variable of the layers to false but I still couldn't get it to work.
You should double check that it's running the path you intend and that the modules are being called directly. For example, you want to hit the following line to call the conv module, instead of using the fast causal_conv1d_fn
. Perhaps it's not hitting this path if you've already pip installed the module because it will automatically use the fast conv1d function.
https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py#L169
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Thank you I hadn't seen that logic there. Because it does not seem feasible to make sure that causal-conv1d is not installed, I will probably have to do some hacky work around to go around that
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It should be fairly easy to add another flag to the init function and change the line
if causal_conv1d_fn is None:
to something like
if causal_conv1d_fn is None or not self.use_fast_conv:
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Yes, that's what I was thinking
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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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- 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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