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import numpy as np |
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from typing import Dict |
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def softmax(x: np.ndarray, axis: int): |
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x -= x.max(axis=axis, keepdims=True) |
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e: np.ndarray = np.exp(x) |
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return e / e.sum(axis=axis, keepdims=True) |
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def sample_logits(out, temperature: float = 1.0, top_p: float = 0.8, logit_bias: Dict[int, float] = None) -> int: |
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if hasattr(out, '__module__') and out.__module__ == 'torch': |
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out = out.cpu().numpy() |
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probs: np.ndarray = softmax(out, axis=-1) |
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return sample_probs(probs, temperature, top_p, logit_bias) |
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def sample_probs(probs: np.ndarray, temperature: float = 1.0, top_p: float = 0.8, logit_bias: Dict[int, float] = None) -> int: |
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if not (0.0 <= temperature): |
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raise ValueError('temperature') |
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if not (0.0 <= top_p <= 1.0): |
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raise ValueError('top_p') |
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if top_p == 0.0: |
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top_p = 1.0 |
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if logit_bias is not None and len(logit_bias) > 0: |
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logits: np.ndarray = np.log(probs) |
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ids, values = zip(*logit_bias.items()) |
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logits[list(ids)] += values |
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logits -= logits.max(axis=-1, keepdims=True) |
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probs = np.exp(logits) / np.sum(np.exp(logits)) |
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if temperature == 0.0: |
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return np.argmax(probs).item() |
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if top_p < 1.0: |
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sorted_probs = np.sort(probs)[::-1] |
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cumulative_probs = np.cumsum(sorted_probs) |
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cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)]) |
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probs[probs < cutoff] = 0 |
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if temperature != 1.0: |
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probs = np.power(probs, 1.0 / temperature) |
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probs = probs / np.sum(probs) |
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return np.random.choice(a=len(probs), p=probs) |
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