.github causal_conv1d csrc rocm_patch tests .gitignore AUTHORS LICENSE README.md setup.py 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...
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...
参考步骤为: git clone https://github.com/Dao-AILab/causal-conv1d.gitcd causal-conv1dgit checkout v1.1.1 # current latest version tagCAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .cd ..git clone https://github.com/state-spaces/mamba.gitcd mambagit checkout v1.1.1 # current latest version...
1、使用网友配置好的Docker环境,参考:解决causal_conv1d和mamba_ssm无法安装 -> 直接使用Mamba基础环境docker镜像DockHub仓库地址:https://hub.docker.com/repository/docker/kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1/general代码:docker pull kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1:1.1.1 2、直接下载工程文件,...
访问causal_conv1d的官方文档或GitHub仓库,了解是否有特定的安装步骤或已知的安装问题。例如,仓库中可能提供了预编译的wheel文件,或者指出了某些常见的安装错误及其解决方案。 在虚拟环境中安装: 为了避免系统环境干扰,建议在虚拟环境中安装causal_conv1d。你可以使用venv(Python内置模块)或conda(如果已安装)来创建虚拟环...
conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2) self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None self.relu = nn.ReLU() self.init_weights() def init_weights(self): self.conv1.weight...
LoRA冻结了预训练模型的参数,并在每一层decoder中加入dropout+Linear+Conv1d额外的参数 那么,LoRA是否能达到全参数微调的性能呢? 根据实验可知,全参数微调要比LoRA方式好的多,但在低资源的情况下也不失为一种选择 细致到每个任务中的差距如下图: 代码样例: ...
BASE_WHEEL_URL="https://github.com/Dao-AILab/causal-conv1d/releases/download/{tag_name}/{wheel_name}" # FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels # SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to co...
causal_conv1d import causal_conv1d_fn device = "cuda" b, l = 1, 2**2 k = 2 # this works c = 2**16 - 1 x = torch.randn(b, c, l, dtype=torch.float32, device=device) # batch, channel, seq_len weight = torch.randn...
Causal depthwise conv1d in CUDA, with a PyTorch interface - causal-conv1d/LICENSE at v1.3.0.post1 · Dao-AILab/causal-conv1d