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c50971e860
* Min P implementation * Change default to 0 (no min_p) * nits * nits --------- Co-authored-by: Awni Hannun <awni@apple.com>
103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from functools import partial
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import mlx.core as mx
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@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
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def min_p_sampling(
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logits: mx.array,
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min_p: float,
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min_tokens_to_keep: int = 1,
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temperature=1.0,
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) -> mx.array:
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"""
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Apply min-p sampling to the logits.
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Min-p keeps all tokens that are above a minimum probability, scaled by the
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probability of the most likely token. As a result, the filter is more
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aggressive given a very high-probability token.
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Args:
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logits: The logits from the model's output.
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min_p (float): Minimum token probability. Typical values are in the
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0.01-0.2 range, comparably selective as setting `top_p` in the
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0.99-0.8 range.
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min_tokens_to_keep (int, optional): Minimum number of tokens that cannot
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be filtered. Default: ``1``.
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"""
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if not (0 <= min_p <= 1.0):
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raise ValueError(
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f"`min_p` has to be a float in the [0, 1] interval, but is {min_p}"
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)
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if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
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raise ValueError(
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f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"
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)
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# reference implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L531-L605
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# Softmax probabilities
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probs = mx.softmax(logits * (1 / temperature), axis=-1)
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# Indices sorted in decreasing order
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sorted_indices = mx.argsort(-logits).squeeze(0)
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sorted_probs = probs[..., sorted_indices]
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# Top probability
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top_probs = probs[..., sorted_indices[0]]
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# Calculate the min_p threshold
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scaled_min_p = min_p * top_probs
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# Mask tokens that have a probability less than the scaled min_p
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tokens_to_remove = sorted_probs < scaled_min_p
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tokens_to_remove[..., :min_tokens_to_keep] = False
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# Create pool of tokens with probability less than scaled min_p
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selected_probs = mx.where(tokens_to_remove, 0, sorted_probs)
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# Return sampled token
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sorted_token = mx.random.categorical(mx.log(selected_probs))
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return sorted_indices[sorted_token]
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@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
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def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.array:
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"""
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Apply top-p (nucleus) sampling to logits.
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Args:
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logits: The logits from the model's output.
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top_p: The cumulative probability threshold for top-p filtering.
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temperature: Temperature parameter for softmax distribution reshaping.
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Returns:
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token selected based on the top-p criterion.
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"""
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# referenced implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L449-L460
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probs = mx.softmax(logits * (1 / temperature), axis=-1)
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# sort probs in ascending order
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sorted_indices = mx.argsort(probs, axis=-1)
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sorted_probs = probs[..., sorted_indices.squeeze(0)]
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cumulative_probs = mx.cumsum(sorted_probs, axis=-1)
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# select tokens with cumulative probs below threshold
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top_probs = mx.where(
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cumulative_probs > 1 - top_p,
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sorted_probs,
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0,
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)
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sorted_token = mx.random.categorical(mx.log(top_probs))
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token = sorted_indices.squeeze(0)[sorted_token]
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return token
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@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
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def categorical_sampling(logits, temp):
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return mx.random.categorical(logits * (1 / temp))
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