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| from typing import List, Union, Tuple | |
| import torch | |
| from transformers import AutoConfig, AutoTokenizer, AutoModelWithLMHead, AutoModel | |
| from transformer_formatter import TransformerOutputFormatter | |
| from utils.f import delegates, pick, memoize | |
| def get_details(mname): | |
| return ModelDetails(mname) | |
| def get_model_tok(mname): | |
| conf = AutoConfig.from_pretrained(mname, output_attentions=True, output_past=False) | |
| tok = AutoTokenizer.from_pretrained(mname, config=conf) | |
| model = AutoModelWithLMHead.from_pretrained(mname, config=conf) | |
| return model, tok | |
| class ModelDetails: | |
| """Wraps a transformer model and tokenizer to prepare inputs to the frontend visualization""" | |
| def __init__(self, mname): | |
| self.mname = mname | |
| self.model, self.tok = get_model_tok(self.mname) | |
| self.model.eval() | |
| self.config = self.model.config | |
| def from_sentence(self, sentence: str) -> TransformerOutputFormatter: | |
| """Get attentions and word probabilities from a sentence. Special tokens are automatically added if a sentence is passed. | |
| Args: | |
| sentence: The input sentence to tokenize and analyze. | |
| """ | |
| tokens = self.tok.tokenize(sentence) | |
| return self.from_tokens(tokens, sentence, add_special_tokens=True) | |
| def from_tokens( | |
| self, tokens: List[str], orig_sentence:str, add_special_tokens:bool=False, mask_attentions:bool=False, topk:int=5 | |
| ) -> TransformerOutputFormatter: | |
| """Get formatted attention and predictions from a list of tokens. | |
| Args: | |
| tokens: Tokens to analyze | |
| orig_sentence: The sentence the tokens came from (needed to help organize the output) | |
| add_special_tokens: Whether to add special tokens like CLS / <|endoftext|> to the tokens. | |
| If False, assume the tokens already have the special tokens | |
| mask_attentions: If True, do not pay attention to attention patterns to special tokens through the model. | |
| topk: How many top predictions to report | |
| """ | |
| ids = self.tok.convert_tokens_to_ids(tokens) | |
| # For GPT2, add the beginning of sentence token to the input. Note that this will work on all models but XLM | |
| bost = self.tok.bos_token_id | |
| clst = self.tok.cls_token_id | |
| sept = self.tok.sep_token_id | |
| if (bost is not None) and (bost != clst)and add_special_tokens: | |
| ids.insert(0, bost) | |
| inputs = self.tok.prepare_for_model(ids, add_special_tokens=add_special_tokens, return_tensors="pt") | |
| parsed_input = self.parse_inputs(inputs, mask_attentions=mask_attentions) | |
| output = self.model(parsed_input['input_ids'], attention_mask=parsed_input['attention_mask']) | |
| logits, atts = self.choose_logits_att(output) | |
| words, probs = self.logits2words(logits, topk) | |
| tokens = self.view_ids(inputs["input_ids"]) | |
| formatted_output = TransformerOutputFormatter( | |
| orig_sentence, | |
| tokens, | |
| inputs["special_tokens_mask"], | |
| atts, | |
| words, | |
| probs.tolist(), | |
| self.config | |
| ) | |
| return formatted_output | |
| def choose_logits_att(self, out:Tuple) -> Tuple: | |
| """Select from the model's output the logits and the attentions, switching on model name | |
| Args: | |
| out: Output from the model's forward pass | |
| Returns: | |
| (logits: tensor((bs, N)), attentions: Tuple[tensor(())]) | |
| """ | |
| if 't5' in self.mname: | |
| logits, _, atts = out | |
| else: | |
| logits, atts = out | |
| return logits, atts | |
| def logits2words(self, logits, topk): | |
| """Convert logit probabilities into words from the tokenizer's vocabulary. | |
| """ | |
| probs, idxs = torch.topk(torch.softmax(logits.squeeze(0), 1), topk) | |
| words = [self.tok.convert_ids_to_tokens(i) for i in idxs] | |
| return words, probs | |
| def view_ids(self, ids: Union[List[int], torch.Tensor]) -> List[str]: | |
| """View what the tokenizer thinks certain ids are for a single input""" | |
| if type(ids) == torch.Tensor: | |
| # Remove batch dimension | |
| ids = ids.squeeze(0).tolist() | |
| out = self.tok.convert_ids_to_tokens(ids) | |
| return out | |
| def parse_inputs(self, inputs, mask_attentions=False): | |
| """Parse the output from `tokenizer.prepare_for_model` to the desired attention mask from special tokens | |
| Args: | |
| - inputs: The output of `tokenizer.prepare_for_model`. | |
| A dict with keys: {'special_token_mask', 'token_type_ids', 'input_ids'} | |
| - mask_attentions: Flag indicating whether to mask the attentions or not | |
| Returns: | |
| Dict with keys: {'input_ids', 'token_type_ids', 'attention_mask', 'special_tokens_mask'} | |
| Usage: | |
| ``` | |
| s = "test sentence" | |
| # from raw sentence to tokens | |
| tokens = tokenizer.tokenize(s) | |
| # From tokens to ids | |
| ids = tokenizer.convert_tokens_to_ids(tokens) | |
| # From ids to input | |
| inputs = tokenizer.prepare_for_model(ids, return_tensors='pt') | |
| # Parse the input. Optionally mask the special tokens from the analysis. | |
| parsed_input = parse_inputs(inputs) | |
| # Run the model, pick from this output whatever inputs you want | |
| from utils.f import pick | |
| out = model(**pick(['input_ids'], parse_inputs(inputs))) | |
| ``` | |
| """ | |
| out = inputs.copy() | |
| # DEFINE SPECIAL TOKENS MASK | |
| if "special_tokens_mask" not in inputs.keys(): | |
| special_tokens = set([self.tok.unk_token_id, self.tok.cls_token_id, self.tok.sep_token_id, self.tok.bos_token_id, self.tok.eos_token_id, self.tok.pad_token_id]) | |
| in_ids = inputs['input_ids'][0] | |
| special_tok_mask = [1 if int(i) in special_tokens else 0 for i in in_ids] | |
| inputs['special_tokens_mask'] = special_tok_mask | |
| if mask_attentions: | |
| out["attention_mask"] = torch.tensor( | |
| [int(not i) for i in inputs.get("special_tokens_mask")] | |
| ).unsqueeze(0) | |
| else: | |
| out["attention_mask"] = torch.tensor( | |
| [1 for i in inputs.get("special_tokens_mask")] | |
| ).unsqueeze(0) | |
| return out |