注意:你需要根据你的CUDA版本选择合适的PyTorch版本。例如,如果你的CUDA版本是11.1,则应选择支持CUDA 11.1的PyTorch版本。 验证安装是否成功: 你可以通过以下代码来验证reformer_pytorch和PyTorch是否正确安装,并且可以在你的环境中使用:python import torch from reformer_pytorch import ReformerLM # 检查PyTorch是否支持CUD...
下面是 Reformer 模型的类图: Reformer+__init__(self)+forward(self, x)Encoder+__init__(self)+forward(self, x)Decoder+__init__(self)+forward(self, x)LSHAttention+__init__(self)+forward(self, x) PyTorch 实现 以下是一个简单的 Reformer 的 PyTorch 实现示例,展示了特定的基本结构。这个代码并...
The Reformer (just a stack of reversible LSH attention) # should fit in ~ 5gb - 8k embeddings import torch from reformer_pytorch import Reformer model = Reformer( dim = 512, depth = 12, max_seq_len = 8192, heads = 8, lsh_dropout = 0.1, causal = True ).cuda() x = torch.randn(...
Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH attention, reversible network, and chunking. It has been validated with an auto-regressive task (enwik8). 32k tokens 81k tokens with half preci...
win10 CUDA11.1安装torch1.9 / reformer_pytorch 环境 NVIDIA-SMI 457.52 Driver Version: 457.52 CUDA Version: 11.1 安装torch-gpu conda create -n torch1.9 python=3.8 pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable....
Releases Releases 功能基于仓库中的历史标记 建议使用类似 V1.0 的版本标记作为 Releases 点。深圳市奥思网络科技有限公司版权所有 Git 大全 Git 命令学习 CopyCat 代码克隆检测 APP与插件下载 Gitee Reward Gitee 封面人物 GVP 项目 Gitee 博客 Gitee 公益计划 Gitee 持续集成 OpenAPI 帮助文档 在线自助...
The Reformer (just a stack of reversible LSH attention) # should fit in ~ 5gb - 8k embeddings import torch from reformer_pytorch import Reformer model = Reformer( dim = 512, depth = 12, max_seq_len = 8192, heads = 8, lsh_dropout = 0.1, causal = True ).cuda() x = torch.randn(...
Reformer, the efficient Transformer, in Pytorch. Contribute to sxjscience/reformer-pytorch development by creating an account on GitHub.
The Reformer (just a stack of reversible LSH attention) # should fit in ~ 5gb - 8k embeddings import torch from reformer_pytorch import Reformer model = Reformer( dim = 512, depth = 12, heads = 8, lsh_dropout = 0.1, causal = True ).cuda() x = torch.randn(1, 8192, 512).cuda(...
Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH attention, reversible network, and chunking. It has been validated with an auto-regressive task (enwik8). It also includes additional features to...