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| Metadata-Version: 2.1 | |
| Name: causal-conv1d | |
| Version: 1.4.0 | |
| Summary: Causal depthwise conv1d in CUDA, with a PyTorch interface | |
| Home-page: https://github.com/Dao-AILab/causal-conv1d | |
| Author: Tri Dao | |
| Author-email: [email protected] | |
| License: UNKNOWN | |
| Platform: UNKNOWN | |
| Classifier: Programming Language :: Python :: 3 | |
| Classifier: License :: OSI Approved :: BSD License | |
| Classifier: Operating System :: Unix | |
| Requires-Python: >=3.8 | |
| Description-Content-Type: text/markdown | |
| License-File: LICENSE | |
| License-File: AUTHORS | |
| # Causal depthwise conv1d in CUDA with a PyTorch interface | |
| Features: | |
| - Support fp32, fp16, bf16. | |
| - Kernel size 2, 3, 4. | |
| ## How to use | |
| ``` | |
| from causal_conv1d import causal_conv1d_fn | |
| ``` | |
| ``` | |
| def causal_conv1d_fn(x, weight, bias=None, activation=None): | |
| """ | |
| x: (batch, dim, seqlen) | |
| weight: (dim, width) | |
| bias: (dim,) | |
| activation: either None or "silu" or "swish" | |
| out: (batch, dim, seqlen) | |
| """ | |
| ``` | |
| Equivalent to: | |
| ``` | |
| import torch.nn.functional as F | |
| F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)[..., :seqlen] | |
| ``` | |
| ## Additional Prerequisites for AMD cards | |
| ### Patching ROCm | |
| If you are on ROCm 6.0, run the following steps to avoid errors during compilation. This is not required for ROCm 6.1 onwards. | |
| 1. Locate your ROCm installation directory. This is typically found at `/opt/rocm/`, but may vary depending on your installation. | |
| 2. Apply the Patch. Run with `sudo` in case you encounter permission issues. | |
| ```bash | |
| patch /opt/rocm/include/hip/amd_detail/amd_hip_bf16.h < rocm_patch/rocm6_0.patch | |
| ``` | |