【发布时间】:2019-12-23 16:15:55
【问题描述】:
我正在 Google CoLab 上使用“lda2vec-pytorch”项目, 运行 pytorch 1.1.0
https://github.com/TropComplique/lda2vec-pytorch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cuda:0
我在转发方法中遇到异常,在我的 类negative_sampling_loss(nn.Module):
noise = self.multinomial.draw(batch_size*window_size*self.num_sampled)
noise = Variable(noise).view(batch_size, window_size*self.num_sampled)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.embedding = self.embedding.to(device)
#print("negative_sampling_loss::forward() self.embedding", self.embedding.is_cuda) This line get's an error.
# shape: [batch_size, window_size*num_sampled, embedding_dim]
noise = self.embedding(noise) # Exception HERE
这是堆栈跟踪:
Traceback (most recent call last):
File "train.py", line 36, in <module>
main()
File "train.py", line 32, in main
save_every=20, grad_clip=5.0
File "../utils/training.py", line 138, in train
neg_loss, dirichlet_loss = model(doc_indices, pivot_words, target_words)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "../utils/lda2vec_loss.py", line 82, in forward
neg_loss = self.neg(pivot_words, target_words, doc_vectors, w)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "../utils/lda2vec_loss.py", line 167, in forward
noise = self.embedding(noise)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/sparse.py", line 117, in forward
self.norm_type, self.scale_grad_by_freq, self.sparse)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1506, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index'
有什么想法吗?
【问题讨论】: