19 lines
669 B
Python
19 lines
669 B
Python
import torch
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from torch import nn
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class Sampler(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, logits: torch.Tensor, temperatures: torch.Tensor):
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logits = logits.to(torch.float)
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greedy_tokens = logits.argmax(dim=-1)
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logits.div_(temperatures.unsqueeze(dim=1))
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probs = torch.softmax(logits, dim=-1, dtype=torch.float)
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# logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
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epsilon = 1e-10
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sample_tokens = probs.div_(torch.empty_like(probs).exponential_(1) + epsilon).argmax(dim=-1)
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return torch.where(temperatures == 0, greedy_tokens, sample_tokens)
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