Comments (3)
efrantar
commented on August 18, 2024
Hi, as also discussed in #15 (see there for some more details), the layer-wise 2:4 inference speedups we report were directly produced with NVIDIA's CUTLASS profiler using their prebuilt kernels, no custom code from our side was involved.
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pkulium
commented on August 18, 2024
thanks for this info!
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Hongbosherlock
commented on August 18, 2024
Hi, as also discussed in #15 (see there for some more details), the layer-wise 2:4 inference speedups we report were directly produced with NVIDIA's CUTLASS profiler using their prebuilt kernels, no custom code from our side was involved.
Have you tried cuSPARSE? Is it easier to use and more effective than cutlass?
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Related Issues (20)
- How should I verify the speedup effect of the algorithm? HOT 4
- Purpose of this update
- finetuning sparsified LLaMa HOT 5
- Dependencies are wrong HOT 3
- Would sparsegpt be available for Llama2? HOT 3
- When would the code for GPT-J-6B be released?
- Adaptation for Pruning Conv2d or Conv3d Layers? HOT 1
- Can SparseGPT be used on BERT ?
- Using llama.py silently fails and occasionally causes system instability
- transformers version is not correct
- Mistral Support HOT 2
- how to use for Baichuan?
- 2:4 sparsity with to_sparse_semi_structured method from pytorch results in memory issue
- Why Hessian can get by activation ($H = XX^T$) ？ HOT 4
- Why transpose the input when in case of nn.Linear or nn.Conv1d?
- AttributeError: 'NoneType' object has no attribute 'shape' HOT 7
- AWQ alongside sparsegpt
- why i can't reproduce the result of paper? HOT 7
- [feature request] all models.
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