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[BUGFIX]fix bug of top-p sampling #1503

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[BUGFIX]fix bug of top-p sampling #1503

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8364d7e
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Jan 26, 2021
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Jan 26, 2021
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Feb 3, 2021
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3 changes: 2 additions & 1 deletion scripts/generation/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ python3 generate_unconditional_gpt2_samples.py \
--gpu 0 \
--temperature 0.7 \
--top_k 40 \
--batch_size 2 \
--nsamples 1000 > samples
```

Expand Down Expand Up @@ -43,7 +44,7 @@ Some metrics for the unconditional generated text
| topk=40 | 0.4291 | 0.9666 | 0.0 |
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I think previously we have the results of t=0.9, we should remove that row.

| topk=640 | 0.3384 | 0.9623 | 0.0 |
| topk=40 t=0.7 | 0.4621 | 0.9586 | 1.1 |

| topp=0.95 | 0.2676 | 0.9519 | 0.0 |

Part of some interesting generated unconditional example

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19 changes: 14 additions & 5 deletions src/gluonnlp/sequence_sampler.py
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Expand Up @@ -717,11 +717,20 @@ def forward(self, samples, valid_length, outputs, scores, step, beam_alive_mask,
probs = mx.npx.softmax(outputs / self._temperature)

if self._sampling_topp > 0:
probs = mx.np.where(
probs > self._sampling_topp,
probs,
mx.np.zeros_like(probs)
)
sorted_probs, sorted_indices = mx.npx.topk(probs, axis=2, k=-1, ret_typ='both', is_ascend=False)
cumsum_probs = mx.np.cumsum(sorted_probs, axis=2)
masked_probs = mx.np.where(
cumsum_probs > self._sampling_topp,
sorted_probs,
mx.np.ones_like(probs)
)
# choose the borderline prob
p_prob = mx.np.min(masked_probs, axis=2, keepdims=True)
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Is it possible to use exactly the same implementation as https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317?

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I'm referring to the part in which they choose not to mask the top-1 probability:

sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()

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Sorry for the confusion. I see that both sort and argsort are implemented but I don't see a way to get both values and indices in one call. The usage of topk(k=-1) that assumes the return values to be sorted seems to be undocumented, which is a bit of a concern.

probs = mx.np.where(
probs >= p_prob,
probs,
mx.np.zeros_like(probs)
)
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@sxjscience sxjscience Feb 9, 2021 •
edited
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The major difference between the current implementation and the original pytorch-based implementation is that when sampling_topp < max(probs), it is not clear which probability will be picked.

The pytorch-based implementation will always choose the token that is most probable.

elif self._sampling_topk > 0:
topk_probs = mx.npx.topk(probs, axis=2, k=self._sampling_topk, ret_typ='value')
# choose the k max prob
Expand Down