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Posted to notifications@texera.apache.org by "dependabot[bot] (via GitHub)" <gi...@apache.org> on 2026/07/20 19:40:02 UTC
[PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
dependabot[bot] opened a new pull request, #6614:
URL: https://github.com/apache/texera/pull/6614
Bumps [transformers](https://github.com/huggingface/transformers) from 5.3.0 to 5.5.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases">transformers's releases</a>.</em></p>
<blockquote>
<h1>Release v5.5.0</h1>
<!-- raw HTML omitted -->
<h2>New Model additions</h2>
<h3>Gemma4</h3>
<p><a href="https://github.com/huggingface/transformers/blob/HEAD/INSET_PAPER_LINK">Gemma 4</a> is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.</p>
<!-- raw HTML omitted -->
<p>You can find all the original Gemma 4 checkpoints under the <a href="https://huggingface.co/collections/google/gemma-4-release-67c6c6f89c4f76621268bb6d">Gemma 4</a> release.</p>
<p>The key difference from previous Gemma releases is the new design to process <strong>images of different sizes</strong> using a <strong>fixed-budget number of tokens</strong>. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:</p>
<ul>
<li>The total number of pixels must fit within a patch budget</li>
<li>Both height and width must be divisible by <strong>48</strong> (= patch size 16 × pooling kernel 3)</li>
</ul>
<blockquote>
<p>[!IMPORTANT]
Gemma 4 does <strong>not</strong> apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).</p>
</blockquote>
<p>The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is <strong>280 soft tokens</strong> per image.</p>
<table>
<thead>
<tr>
<th align="center">Soft Tokens</th>
<th align="center">Patches (before pooling)</th>
<th align="center">Approx. Image Area</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">70</td>
<td align="center">630</td>
<td align="center">~161K pixels</td>
</tr>
<tr>
<td align="center">140</td>
<td align="center">1,260</td>
<td align="center">~323K pixels</td>
</tr>
<tr>
<td align="center"><strong>280</strong></td>
<td align="center"><strong>2,520</strong></td>
<td align="center"><strong>~645K pixels</strong></td>
</tr>
<tr>
<td align="center">560</td>
<td align="center">5,040</td>
<td align="center">~1.3M pixels</td>
</tr>
<tr>
<td align="center">1,120</td>
<td align="center">10,080</td>
<td align="center">~2.6M pixels</td>
</tr>
</tbody>
</table>
<p>To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."</p>
<h3>NomicBERT</h3>
<p>NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.</p>
<p><strong>Links:</strong> <a href="https://huggingface.co/docs/transformers/main/en/model_doc/nomic_bert">Documentation</a> | <a href="https://arxiv.org/abs/2402.01613">Paper</a></p>
<ul>
<li>Internalise the NomicBERT model (<a href="https://redirect.github.com/huggingface/transformers/issues/43067">#43067</a>) by <a href="https://github.com/ed22699"><code>@ed22699</code></a> in <a href="https://redirect.github.com/huggingface/transformers/pull/43067">#43067</a></li>
</ul>
<h3>MusicFlamingo</h3>
<p>Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.</p>
<p><strong>Links:</strong> <a href="https://huggingface.co/docs/transformers/main/en/model_doc/musicflamingo">Documentation</a> | <a href="https://huggingface.co/papers/2511.10289">Paper</a></p>
<ul>
<li>Add Music Flamingo (<a href="https://redirect.github.com/huggingface/transformers/issues/43538">#43538</a>) by <a href="https://github.com/lashahub"><code>@lashahub</code></a> in <a href="https://redirect.github.com/huggingface/transformers/pull/43538">#43538</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/huggingface/transformers/commit/c1c34249fa27deefbd4a377dfbf883a39baf5c6d"><code>c1c3424</code></a> update</li>
<li><a href="https://github.com/huggingface/transformers/commit/20bff6865a756a074f5b893b57f0ae438b25ec46"><code>20bff68</code></a> update release workflow</li>
<li><a href="https://github.com/huggingface/transformers/commit/89564412a56ae6581f8aa48a533a835860dc9f43"><code>8956441</code></a> v5.5.0</li>
<li><a href="https://github.com/huggingface/transformers/commit/5135e5efa7203cd23aac0866de12dfeef038422d"><code>5135e5e</code></a> casually dropping the most capable open weights on the planet (<a href="https://redirect.github.com/huggingface/transformers/issues/45192">#45192</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/a594e09e3924120f1f5508e7d81946bf3504df2b"><code>a594e09</code></a> Internalise the NomicBERT model (<a href="https://redirect.github.com/huggingface/transformers/issues/43067">#43067</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/4932e9721e230bea915341e7f04db32885b6c6af"><code>4932e97</code></a> Fix resized LM head weights being overwritten by post_init (<a href="https://redirect.github.com/huggingface/transformers/issues/45079">#45079</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/57e84139542c8c297873f35fcd25f66ffcf132ae"><code>57e8413</code></a> [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (<a href="https://redirect.github.com/huggingface/transformers/issues/45124">#45124</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/b10552e99dc4974b30126995baea455df43f8476"><code>b10552e</code></a> Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...</li>
<li><a href="https://github.com/huggingface/transformers/commit/423f2a31d2bd05bdc1dc30dd938389edaa998fde"><code>423f2a3</code></a> fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...</li>
<li><a href="https://github.com/huggingface/transformers/commit/ade7a05a42bf53b183bb78c181743be063c5ff14"><code>ade7a05</code></a> Generalize gemma vision mask to videos (<a href="https://redirect.github.com/huggingface/transformers/issues/45185">#45185</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v5.3.0...v5.5.0">compare view</a></li>
</ul>
</details>
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "codecov-commenter (via GitHub)" <gi...@apache.org>.
codecov-commenter commented on PR #6614:
URL: https://github.com/apache/texera/pull/6614#issuecomment-5026569542
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<details><summary>Additional details and impacted files</summary>
```diff
@@ Coverage Diff @@
## main #6614 +/- ##
=========================================
Coverage 74.45% 74.45%
Complexity 3436 3436
=========================================
Files 1157 1157
Lines 45621 45621
Branches 5031 5031
=========================================
Hits 33966 33966
Misses 9995 9995
Partials 1660 1660
```
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #6614:
URL: https://github.com/apache/texera/pull/6614#issuecomment-5026488483
<!-- texera-benchmarks-comment -->
## ⚠️ Benchmark changes need a look
🟢 0 better · 🔴 4 worse · ⚪ 11 noise (<±5%) · 0 without baseline
> Compared against main `42027e8` benchmarked on **this same runner**, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.
[Dashboard](https://apache.github.io/texera/dev/bench/) · [Run](https://github.com/apache/texera/actions/runs/29772919363)
| | config | throughput | MB/s | latency | max Δ latest / 7d |
|---|---|---:|---:|---:|---:|
| 🔴 | bs=10 sw=10 sl=64 | 393 | 0.24 | 24,333/30,715/30,715 us | 🔴 +19.4% / 🔴 +96.5% |
| 🔴 | bs=100 sw=10 sl=64 | 791 | 0.483 | 125,870/144,119/144,119 us | 🔴 +7.1% / 🔴 +36.0% |
| ⚪ | bs=1000 sw=10 sl=64 | 919 | 0.561 | 1,090,519/1,127,732/1,127,732 us | ⚪ within ±5% / 🔴 +11.9% |
<details><summary>Baseline details</summary>
Latest main `42027e8` from same runner
| config | metric | PR | latest main | 7d avg | Δ latest | Δ 7d |
|---|---|---:|---:|---:|---:|---:|
| bs=10 sw=10 sl=64 | throughput | 393 tuples/sec | 436 tuples/sec | 767.23 tuples/sec | -9.9% | -48.8% |
| bs=10 sw=10 sl=64 | MB/s | 0.24 MB/s | 0.266 MB/s | 0.468 MB/s | -9.8% | -48.7% |
| bs=10 sw=10 sl=64 | p50 | 24,333 us | 20,383 us | 12,705 us | +19.4% | +91.5% |
| bs=10 sw=10 sl=64 | p95 | 30,715 us | 32,203 us | 15,633 us | -4.6% | +96.5% |
| bs=10 sw=10 sl=64 | p99 | 30,715 us | 32,203 us | 20,214 us | -4.6% | +51.9% |
| bs=100 sw=10 sl=64 | throughput | 791 tuples/sec | 827 tuples/sec | 1,001 tuples/sec | -4.4% | -21.0% |
| bs=100 sw=10 sl=64 | MB/s | 0.483 MB/s | 0.505 MB/s | 0.611 MB/s | -4.4% | -21.0% |
| bs=100 sw=10 sl=64 | p50 | 125,870 us | 117,552 us | 99,949 us | +7.1% | +25.9% |
| bs=100 sw=10 sl=64 | p95 | 144,119 us | 144,841 us | 105,996 us | -0.5% | +36.0% |
| bs=100 sw=10 sl=64 | p99 | 144,119 us | 144,841 us | 117,999 us | -0.5% | +22.1% |
| bs=1000 sw=10 sl=64 | throughput | 919 tuples/sec | 911 tuples/sec | 1,033 tuples/sec | +0.9% | -11.1% |
| bs=1000 sw=10 sl=64 | MB/s | 0.561 MB/s | 0.556 MB/s | 0.631 MB/s | +0.9% | -11.0% |
| bs=1000 sw=10 sl=64 | p50 | 1,090,519 us | 1,093,703 us | 974,571 us | -0.3% | +11.9% |
| bs=1000 sw=10 sl=64 | p95 | 1,127,732 us | 1,156,315 us | 1,016,467 us | -2.5% | +10.9% |
| bs=1000 sw=10 sl=64 | p99 | 1,127,732 us | 1,156,315 us | 1,048,798 us | -2.5% | +7.5% |
</details>
<details><summary>Raw CSV</summary>
```csv
config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,508.88,200,128000,393,0.240,24332.52,30714.79,30714.79
1,100,10,64,20,2528.03,2000,1280000,791,0.483,125870.00,144119.21,144119.21
2,1000,10,64,20,21752.94,20000,12800000,919,0.561,1090519.47,1127732.38,1127732.38
```
</details>
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "aglinxinyuan (via GitHub)" <gi...@apache.org>.
aglinxinyuan merged PR #6614:
URL: https://github.com/apache/texera/pull/6614
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "Copilot (via GitHub)" <gi...@apache.org>.
Copilot commented on code in PR #6614:
URL: https://github.com/apache/texera/pull/6614#discussion_r3617067787
##########
amber/operator-requirements.txt:
##########
@@ -26,7 +26,7 @@ torch==2.12.1+cpu ; platform_system == "Linux" and platform_machine == "x86_64"
torch==2.12.1 ; platform_system != "Linux" or platform_machine != "x86_64"
scikit-learn==1.7.2
-transformers==5.3.0
+transformers==5.5.0
Review Comment:
This dependency bump will make the Python binary-license drift check fail unless amber/LICENSE-binary-python is regenerated/updated to reflect the new resolved version. Currently amber/LICENSE-binary-python still lists "transformers==5.3.0" (line 236), and CI explicitly compares pip-licenses output against that file (see .github/workflows/build.yml:963-985 running bin/licensing/check_binary_deps.py).
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #6614:
URL: https://github.com/apache/texera/pull/6614#issuecomment-5026434988
<!-- texera-reviewer-suggestion -->
### Automated Reviewer Suggestions
Based on the `git blame` history of the changed files, we recommend the following reviewers:
- **Contributors with relevant context:** `@aglinxinyuan`
You can notify them by mentioning `@aglinxinyuan` in a comment.
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Re: [PR] fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber [texera]
Posted by "aglinxinyuan (via GitHub)" <gi...@apache.org>.
aglinxinyuan commented on code in PR #6614:
URL: https://github.com/apache/texera/pull/6614#discussion_r3617176209
##########
amber/operator-requirements.txt:
##########
@@ -26,7 +26,7 @@ torch==2.12.1+cpu ; platform_system == "Linux" and platform_machine == "x86_64"
torch==2.12.1 ; platform_system != "Linux" or platform_machine != "x86_64"
scikit-learn==1.7.2
-transformers==5.3.0
+transformers==5.5.0
Review Comment:
Addressed in 8d6bbadb9a — `amber/LICENSE-binary-python` now claims `transformers==5.5.0`, and the pyamber 3.12 leg (which runs `check_binary_deps.py`) passes on that commit.
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