Bump tokenizers from 0.21 to 0.22.2#2
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Bumps [tokenizers](https://github.com/huggingface/tokenizers) from 0.21 to 0.22.2. - [Release notes](https://github.com/huggingface/tokenizers/releases) - [Changelog](https://github.com/huggingface/tokenizers/blob/main/RELEASE.md) - [Commits](huggingface/tokenizers@v0.21.0...v0.22.2) --- updated-dependencies: - dependency-name: tokenizers dependency-version: 0.22.2 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>
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OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting If you change your mind, just re-open this PR and I'll resolve any conflicts on it. |
FortPercent
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Apr 29, 2026
Co-authored-by: Bihan Rana <bihan@Bihans-MacBook-Pro.local> Co-authored-by: peterschmidt85 <andrey.cheptsov@gmail.com>
FortPercent
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1. reward_models/hps.py: revert frame indexing to ``video_frames[0]``.
The earlier ``video_frames[:, 0]`` "fix" was based on a wrong layout
assumption (assumed (C, T, H, W)). split_video_frames(permute_to_tchw=
True) actually produces (T, C, H, W), matching aesthetic.py:130 — so
``video_frames[0]`` is the correct first-frame slice. The wrong fix
produced ``KeyError ((H, W, T), '|u1')`` from PIL on a T-channel array.
2. dp_actor.py: train_timesteps was ``int(N * fraction)`` which floored
to 0 for sampling_steps in {1, 2} (the rollout drops the final
sigma->0 step, leaving N=sampling_steps-1 trainable timesteps). The
policy update silently no-op'd. Changed to ``max(1, int(...))`` and
added a RuntimeError when N <= 0 so the failure is loud.
3. run_dancegrpo_smoke.sh: bump default SAMPLING_STEPS from 1 to 4.
With sampling_steps=1 the rollout produces 0 trainable timesteps
(see #2), which made the smoke run but never update the policy. 4
keeps the smoke fast while still giving the actor real gradient
signal under the default timestep_fraction=0.6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
FortPercent
pushed a commit
that referenced
this pull request
May 7, 2026
Co-authored-by: Bihan Rana <bihan@Bihans-MacBook-Pro.local> Co-authored-by: peterschmidt85 <andrey.cheptsov@gmail.com>
FortPercent
pushed a commit
that referenced
this pull request
May 7, 2026
1. reward_models/hps.py: revert frame indexing to ``video_frames[0]``.
The earlier ``video_frames[:, 0]`` "fix" was based on a wrong layout
assumption (assumed (C, T, H, W)). split_video_frames(permute_to_tchw=
True) actually produces (T, C, H, W), matching aesthetic.py:130 — so
``video_frames[0]`` is the correct first-frame slice. The wrong fix
produced ``KeyError ((H, W, T), '|u1')`` from PIL on a T-channel array.
2. dp_actor.py: train_timesteps was ``int(N * fraction)`` which floored
to 0 for sampling_steps in {1, 2} (the rollout drops the final
sigma->0 step, leaving N=sampling_steps-1 trainable timesteps). The
policy update silently no-op'd. Changed to ``max(1, int(...))`` and
added a RuntimeError when N <= 0 so the failure is loud.
3. run_dancegrpo_smoke.sh: bump default SAMPLING_STEPS from 1 to 4.
With sampling_steps=1 the rollout produces 0 trainable timesteps
(see #2), which made the smoke run but never update the policy. 4
keeps the smoke fast while still giving the actor real gradient
signal under the default timestep_fraction=0.6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Bumps tokenizers from 0.21 to 0.22.2.
Release notes
Sourced from tokenizers's releases.
... (truncated)
Commits
f383101fix max build?fb69151Python release fix (#1905)6573f2cadd lock1d9dbb8push the release95504c0add .lock and v0.22.28604740update stub for typing (#1896)a5e03baBump express in /tokenizers/examples/unstable_wasm/www (#1903)ebbc3c8bump PyO3 to 0.26 (#1901)b83d7c9DOCS: addadd_prefix_spacetoprocessors.ByteLevel(#1878)0607860Mark immutable pyclasses as frozen (#1861)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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