Description
when i am converting from english to roman urdu, it is giving error:
`
IndexError Traceback (most recent call last)
Cell In[21], line 14
12 encoder_self_attention_mask, decoder_self_attention_mask, decoder_cross_attention_mask = create_masks(eng_batch, ur_batch)
13 optim.zero_grad()
---> 14 ur_predictions = transformer(eng_batch,
15 ur_batch,
16 encoder_self_attention_mask.to(device),
17 decoder_self_attention_mask.to(device),
18 decoder_cross_attention_mask.to(device),
19 enc_start_token=False,
20 enc_end_token=False,
21 dec_start_token=True,
22 dec_end_token=True)
23 labels = transformer.decoder.sentence_embedding.batch_tokenize(ur_batch, start_token=False, end_token=True)
24 loss = criterian(
25 ur_predictions.view(-1, ur_vocab_size).to(device),
26 labels.view(-1).to(device)
27 ).to(device)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File ~\Desktop\FYP\Web App\Meme_AI\transformer model\transformer_model.py:302, in Transformer.forward(self, x, y, encoder_self_attention_mask, decoder_self_attention_mask, decoder_cross_attention_mask, enc_start_token, enc_end_token, dec_start_token, dec_end_token)
292 def forward(self,
293 x,
294 y,
(...)
300 dec_start_token=False, # We should make this true
301 dec_end_token=False): # x, y are batch of sentences
--> 302 x = self.encoder(x, encoder_self_attention_mask, start_token=enc_start_token, end_token=enc_end_token)
303 out = self.decoder(x, y, decoder_self_attention_mask, decoder_cross_attention_mask, start_token=dec_start_token, end_token=dec_end_token)
304 out = self.linear(out)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File ~\Desktop\FYP\Web App\Meme_AI\transformer model\transformer_model.py:179, in Encoder.forward(self, x, self_attention_mask, start_token, end_token)
178 def forward(self, x, self_attention_mask, start_token, end_token):
--> 179 x = self.sentence_embedding(x, start_token, end_token)
180 x = self.layers(x, self_attention_mask)
181 return x
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File ~\Desktop\FYP\Web App\Meme_AI\transformer model\transformer_model.py:72, in SentenceEmbedding.forward(self, x, start_token, end_token)
70 def forward(self, x, start_token, end_token): # sentence
71 x = self.batch_tokenize(x, start_token, end_token)
---> 72 x = self.embedding(x)
73 pos = self.position_encoder().to(get_device())
74 x = self.dropout(x + pos)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\modules\sparse.py:163, in Embedding.forward(self, input)
162 def forward(self, input: Tensor) -> Tensor:
--> 163 return F.embedding(
164 input, self.weight, self.padding_idx, self.max_norm,
165 self.norm_type, self.scale_grad_by_freq, self.sparse)
File c:\Users\AAHIL ALWANI\Desktop\FYP\Web App\Meme_AI\meme\Lib\site-packages\torch\nn\functional.py:2237, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2231 # Note [embedding_renorm set_grad_enabled]
2232 # XXX: equivalent to
2233 # with torch.no_grad():
2234 # torch.embedding_renorm_
2235 # remove once script supports set_grad_enabled
2236 no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 2237 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self
`