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 Refo
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...
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...
安装reformer_pytorch 需要先pip install reformer_pytorch 然后执行第一步,安装torch-gpu版本
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(...
Phil Wangremove unused keyword argument on Reformer...a751fe24年前 243 次提交 提交取消 提示:由于 Git 不支持空文件夾,创建文件夹后会生成空的 .keep 文件 .github/workflows Create python-publish.yml 5年前 examples make sure zero optimization is turned on in deepspeed config ...
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). It also includes additional features to...
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...
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(...