Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) . so you define your embedding as follows. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. The most frequent cause of this error is. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. one way to debug this is checking the max value for the batch before sending to model. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =.
from pythonrepo.com
i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. one way to debug this is checking the max value for the batch before sending to model. The most frequent cause of this error is. so you define your embedding as follows. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,.
An implementation of model parallel GPT2 and GPT3style models using the meshtensorflow library.
Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. one way to debug this is checking the max value for the batch before sending to model. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. i think you have messed up with input dimension declared torch.nn.embedding and with your input. The most frequent cause of this error is. so you define your embedding as follows.
From www.scaler.com
PyTorch Linear and PyTorch Embedding Layers Scaler Topics Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. so you define your embedding as follows. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
torch.nn.Embedding参数解析CSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. The most frequent cause of this error is. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. one way to debug this is checking the max value. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
index out of range in self torch.embedding(weight, input, padding_idx, scale_grad_by_freq Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) The most frequent cause of this error is. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. so you define your embedding as follows. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. class. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
多卡微调报错呢 · Issue 28 · yuanzhoulvpi2017/zero_nlp · GitHub Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. i think you have messed up with input dimension declared torch.nn.embedding. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.51cto.com
【Pytorch基础教程28】浅谈torch.nn.embedding_51CTO博客_Pytorch 教程 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. i think you have messed up with input dimension declared torch.nn.embedding. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From zhuanlan.zhihu.com
torch函数 知乎 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) one way to debug this is checking the max value for the batch before sending to model. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. i think you have messed up with input dimension declared torch.nn.embedding and with your input. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type =. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From www.youtube.com
torch.nn.Embedding How embedding weights are updated in Backpropagation YouTube Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) The most frequent cause of this error is. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. one way to debug this is checking the max value for the batch before sending to model. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. i think you have messed up with input dimension declared. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From pythonrepo.com
An implementation of model parallel GPT2 and GPT3style models using the meshtensorflow library. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. so you define your embedding as follows. Embedding (input, weight,. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
Convering tf to torch How to set custom embedding_size when using load_tf_weights_in_bert Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. one way to debug this is checking the max value for the. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From www.tutorialexample.com
Understand torch.nn.functional.pad() with Examples PyTorch Tutorial Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) The most frequent cause of this error is. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. one way to debug this is checking the max value for the batch before sending to model.. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
【python函数】torch.nn.Embedding函数用法图解CSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. i think you have messed up with input dimension declared torch.nn.embedding and with your. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
Can you share config.json file for BERT? · Issue 1 · omarsou/layoutlm_CORD · GitHub Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. so you define your embedding as follows. one way to debug this is checking the max value for the batch before sending to model. return torch.embedding(weight, input,. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
pytorch 笔记： torch.nn.Embedding_pytorch embeding的权重CSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. one way to debug this is checking the max value for the batch before. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
多卡微调报错呢 · Issue 28 · yuanzhoulvpi2017/zero_nlp · GitHub Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse). Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
torch.embedding IndexError index out of range in self · Issue 37 · paulbricman/dualobsidian Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) one way to debug this is checking the max value for the batch before sending to model. so you define your embedding as follows. i think you have messed up with input dimension declared torch.nn.embedding and with your input. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From www.developerload.com
[SOLVED] Faster way to do multiple embeddings in PyTorch? DeveloperLoad Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. one way. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
Embedding scale_grad_by_freq should probably shrink by sqrt(count) · Issue 84491 · pytorch Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. The most frequent cause of this error is. one way to debug this is checking the max value for the batch before sending to model. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. the traceback indicates. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
Transformer笔记上CSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) one way to debug this is checking the max value for the batch before sending to model. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. the traceback indicates that. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From raishi12.hatenablog.com
PytorchのEmbeddingメモ raishi12’s diary Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. i think you have messed up with input dimension declared torch.nn.embedding and with your input. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. one way to debug this is checking the max value for the batch before sending to model. the traceback indicates. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From exoxmgifz.blob.core.windows.net
Torch.embedding Source Code at David Allmon blog Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. one way to debug this is checking the max value for the batch before sending to model. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. The most frequent cause of. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From t.zoukankan.com
pytorch中，嵌入层torch.nn.embedding的计算方式 走看看 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From exoxmgifz.blob.core.windows.net
Torch.embedding Source Code at David Allmon blog Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. so you define your embedding as follows. one way. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From discuss.pytorch.org
Camembert fine tuning nlp PyTorch Forums Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) one way to debug this is checking the max value for the batch before sending to model. so you define your embedding as follows. The most frequent cause of this error is. i think you have messed up with input dimension declared torch.nn.embedding and with your input. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. the traceback. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the sameCSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) The most frequent cause of this error is. i think you have messed up with input dimension declared torch.nn.embedding and with your input. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. one way to debug this is checking the max value for the batch before sending to model. Embedding (input,. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
padding_idx and provided weights in nn.Embedding and nn.functional.embedding · Issue 46585 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. one way to debug this is checking the max value for the batch before sending to model. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. The most frequent. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From www.syrr.cn
nn.Embedding使用 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. one way to debug this is checking the max value for the batch before sending to model. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:.. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From www.cnblogs.com
Pytorch中使用Embedding报错'IndexError'的解决方法 絵守辛玥 博客园 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. The most frequent cause of this error is. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. i think you have messed up with input dimension declared torch.nn.embedding and with your input. so you define your embedding as follows. class. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From zhuanlan.zhihu.com
Torch.nn.Embedding的用法 知乎 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. The most frequent cause of. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From zhuanlan.zhihu.com
torch.nn 之 Normalization Layers 知乎 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) i think you have messed up with input dimension declared torch.nn.embedding and with your input. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. The most frequent cause of this error is. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type =. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From exoxmgifz.blob.core.windows.net
Torch.embedding Source Code at David Allmon blog Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) so you define your embedding as follows. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. The most frequent cause of this error is. Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
torch.nn.Embedding参数详解之num_embeddings,embedding_dim_torchembeddingCSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. so you define your embedding as follows. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. i think you have messed up with input dimension declared torch.nn.embedding and with your input. one way to debug this is checking the max value for the batch before sending to model. the traceback. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From github.com
Adding scale_grad_by_freq option to torchrec embedding · Issue 1537 · pytorch/torchrec · GitHub Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. i think you have messed up with input dimension declared torch.nn.embedding and with your input. one way to debug this is checking the max value for the batch before sending to model. Embedding (input, weight, padding_idx = none, max_norm =. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
torch.nn.Embedding()的固定化_embedding 固定初始化CSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. one way to debug this is checking the max value for the batch before sending to model. class torch.nn.embedding(num_embeddings, embedding_dim, padding_idx=none, max_norm=none, norm_type=2.0,. The most frequent cause of this error is. the traceback indicates that (in sparse.py) you’re trying to index_select weight with. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From blog.csdn.net
小白学Pytorch系列Torch.nn API Sparse Layers(12)_pytorch的sparse layerCSDN博客 Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) Embedding (input, weight, padding_idx = none, max_norm = none, norm_type = 2.0, scale_grad_by_freq =. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:. The most frequent cause of this error is. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
From discuss.pytorch.org
[Solved, Self Implementing] How to return sparse tensor from nn.Embedding() PyTorch Forums Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse) one way to debug this is checking the max value for the batch before sending to model. i think you have messed up with input dimension declared torch.nn.embedding and with your input. the traceback indicates that (in sparse.py) you’re trying to index_select weight with something that’s out of range. return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) runtimeerror:.. Torch.embedding(Weight Input Padding_Idx Scale_Grad_By_Freq Sparse).
