We lifted 8 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ameenali/hiddenmambaattn | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| broadcat | Ran | ameenali/hiddenmambaattn/vim/rope.py pointer only (licence: NONE) · get_code("f50a8d20efb35c68") |
| compute_rollout_attention | Ran | ameenali/hiddenmambaattn/vim/xai_utils.py pointer only (licence: NONE) · get_code("5a98eb7226d25564") |
| generate_raw_attn | Ran | ameenali/hiddenmambaattn/vim/xai_utils.py pointer only (licence: NONE) · get_code("166972325ad0ce37") |
| new_data_aug_generator | Ran | ameenali/hiddenmambaattn/vim/augment.py pointer only (licence: NONE) · get_code("4a0feeebf8ac43a8") |
| rotate_half | Ran | ameenali/hiddenmambaattn/vim/rope.py pointer only (licence: NONE) · get_code("58823d9435a8751b") |
| build_dataset | Not yet run | ameenali/hiddenmambaattn/vim/datasets.py pointer only (licence: NONE) · get_code("d58967bba23da4e6") |
| build_transform | Not yet run | ameenali/hiddenmambaattn/vim/datasets.py pointer only (licence: NONE) · get_code("ebeb7b6e20a434cf") |
| generate_mamba_attr | Not yet run | ameenali/hiddenmambaattn/vim/xai_utils.py pointer only (licence: NONE) · get_code("c8f47a670b4d9489") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains, including NLP, long-range sequence processing, and computer vision. Selective SSMs are viewed as dual models, in which one trains in parallel on the entire sequence via an IO-aware parallel scan, and deploys in an autoregressive manner. We add a third view and show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers and allows us to peer inside the inner workings of the Mamba model with explainability methods. Our code is publicly available.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.01590")
get_code_for_paper("2403.01590")
have("2403.01590")
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