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31 lines
975 B
Python
31 lines
975 B
Python
import torch
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from modules import sampler_hijack, shared
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from modules.text_generation import generate_reply
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global_scores = None
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def get_next_logits(prompt, state, use_samplers, previous):
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if use_samplers:
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state['max_new_tokens'] = 1
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state['auto_max_new_tokens'] = False
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for _ in generate_reply(prompt, state):
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pass
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scores = sampler_hijack.global_scores[-1]
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else:
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tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
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output = shared.model(input_ids=tokens)
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scores = output['logits'][-1][-1]
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probs = torch.softmax(scores, dim=-1, dtype=torch.float)
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topk_values, topk_indices = torch.topk(probs, k=20, largest=True, sorted=True)
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topk_values = [f"{float(i):.5f}" for i in topk_values]
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output = ''
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for row in list(zip(topk_values, shared.tokenizer.convert_ids_to_tokens(topk_indices))):
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output += f"{row[0]} - {row[1]}\n"
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return output, previous
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