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| #from transformers import T5TokenizerFast, T5ForConditionalGeneration | |
| import Tensor | |
| #from torch.nn import Module | |
| from typing import List, Optional, Tuple | |
| #import torch, os | |
| #import torch.nn.functional as F | |
| class T5(): | |
| ''' | |
| T5 model from: https://huggingface.co/docs/transformers/model_doc/t5 | |
| ''' | |
| def __init__(self, | |
| variant:str="t5-small", | |
| max_source_length:int=256, | |
| max_target_length:int=128, | |
| optimizer_config:dict={}, | |
| ): | |
| # Assertions | |
| assert variant in ["t5-small", "t5-base", "t5-large"] | |
| super().__init__() | |
| self.variant = variant | |
| self.max_source_length = max_source_length | |
| self.max_target_length = max_target_length | |
| # Tokenizer & model | |
| self.tokenizer = T5TokenizerFast.from_pretrained(self.variant, | |
| model_max_length=self.max_source_length) | |
| self.model = T5ForConditionalGeneration.from_pretrained(self.variant) | |
| # Optimizer | |
| self.optimizer = torch.optim.AdamW(self.parameters(), **optimizer_config) | |
| # Scheduler | |
| self.scheduler = None | |
| def tokenize(self, input:List[str]): | |
| out = self.tokenizer(input, max_length=self.max_source_length, | |
| truncation=True, padding=True, | |
| return_tensors="pt") | |
| return out.input_ids.cuda(), out.attention_mask.cuda() | |
| def forward(self, input:List[str], label:Optional[List[str]]=None) -> Tuple[Tensor, Optional[Tensor]]: | |
| ''' | |
| Will receive input and target string and produce the final output as tensor (not decoded) | |
| when target is not None, it will give the loss functions with the output as tuple | |
| ''' | |
| input_ids, input_masks = self.tokenize(input) | |
| if label is not None: | |
| label_ids, label_masks = self.tokenize(label) | |
| output = self.model(input_ids=input_ids, labels=label_ids) | |
| return output.logits, output.loss | |
| return self.model.generate(input_ids=input_ids, | |
| max_new_tokens=self.max_target_length), None | |
| def predict(self, input:List[str]) -> List[str]: | |
| ''' | |
| Will generate the target output as string | |
| ''' | |
| logits, loss = self.forward(input=input) | |
| return self.tokenizer.batch_decode(logits, skip_special_tokens=True) | |
| if __name__ == '__main__': | |
| ''' | |
| Implement a tester class similar to T5-old.py to test if it works | |
| ''' | |
| model = T5('t5-small') | |
| model.to('cuda') | |
| #inputs = [ | |
| #"translate English to German: Thank you so much, Chris.", | |
| #"translate English to German: I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.", | |
| #"translate German to English: Es ist mir wirklich eine Ehre, zweimal auf dieser Bühne stehen zu dürfen. Tausend Dank dafür.", | |
| #] | |
| #targets = [ | |
| #"Vielen Dank, Chris.", | |
| #"Ich bin wirklich begeistert von dieser Konferenz, und ich danke Ihnen allen für die vielen netten Kommentare zu meiner Rede vorgestern Abend.", | |
| #"And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.", | |
| #] | |
| inputs = ["Good Morning, How are you?"] | |
| targets = ["Buongiorno, come stai?"] | |
| logits, loss = model.forward(inputs, targets) | |
| print('Model forward') | |
| print('logits: ', logits) | |
| print('loss: ', loss) | |
| outputs = model.predict(inputs) | |
| #print('OUTPUT') | |
| #print(outputs) | |
| for (inp, out), tar in zip(zip(inputs, outputs), targets): | |
| print(f"Input: \n{inp}\n\nOutput: \n{out}\n\nTarget: \n{tar}\n\n") | |