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| import torch | |
| import numpy as np | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| def getScores(ids, scores, pad_token_id): | |
| """get sequence scores from model.generate output""" | |
| scores = torch.stack(scores, dim=1) | |
| log_probs = torch.log_softmax(scores, dim=2) | |
| # remove start token | |
| ids = ids[:,1:] | |
| # gather needed probs | |
| x = ids.unsqueeze(-1).expand(log_probs.shape) | |
| needed_logits = torch.gather(log_probs, 2, x) | |
| final_logits = needed_logits[:, :, 0] | |
| padded_mask = (ids == pad_token_id) | |
| final_logits[padded_mask] = 0 | |
| final_scores = final_logits.sum(dim=-1) | |
| return final_scores.cpu().detach().numpy() | |
| def topkSample(input, model, tokenizer, | |
| num_samples=5, | |
| num_beams=1, | |
| max_output_length=30): | |
| tokenized = tokenizer(input, return_tensors="pt") | |
| out = model.generate(**tokenized, | |
| do_sample=True, | |
| num_return_sequences = num_samples, | |
| num_beams = num_beams, | |
| eos_token_id = tokenizer.eos_token_id, | |
| pad_token_id = tokenizer.pad_token_id, | |
| output_scores = True, | |
| return_dict_in_generate=True, | |
| max_length=max_output_length,) | |
| out_tokens = out.sequences | |
| out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True) | |
| out_scores = getScores(out_tokens, out.scores, tokenizer.pad_token_id) | |
| pair_list = [(x[0], x[1]) for x in zip(out_str, out_scores)] | |
| sorted_pair_list = sorted(pair_list, key=lambda x:x[1], reverse=True) | |
| return sorted_pair_list | |
| def greedyPredict(input, model, tokenizer): | |
| input_ids = tokenizer([input], return_tensors="pt").input_ids | |
| out_tokens = model.generate(input_ids) | |
| out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True) | |
| return out_str[0] | |
| def predict_tail(entity, relation): | |
| global model, tokenizer | |
| input = entity + "| " + relation | |
| out = topkSample(input, model, tokenizer, num_samples=5) | |
| out_dict = {} | |
| for k, v in out: | |
| out_dict[k] = np.exp(v).item() | |
| return out_dict | |
| tokenizer = AutoTokenizer.from_pretrained("apoorvumang/kgt5-wikikg90mv2") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2") | |
| ent_input = gradio.inputs.Textbox(lines=1, default="World War II") | |
| rel_input = gradio.inputs.Textbox(lines=1, default="followed by") | |
| output = gradio.outputs.Label() | |
| iface = gr.Interface(fn=predict_tail, inputs=[ent_input, rel_input], outputs=output) | |
| iface.launch() | |