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https://api.github.com/repos/huggingface/transformers/issues/22848 |
TITLE
Add LLaVA model
COMMENTS
11
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### Model description
[LLaVA](https://llava-vl.github.io/) is a multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, "achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4".
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
https://github.com/haotian-liu/LLaVA
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https://api.github.com/repos/huggingface/transformers/issues/22188 |
TITLE
XGLMForCausalLM does not support `device_map='auto'` for load 8 bit
COMMENTS
4
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### System Info
transformers: v4.27.0
### Who can help?
@sgugger @muellerzr
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
I was use this code.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "facebook/xglm-1.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map='auto')
```
Error:
```python
Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[5], line 3
1 model_name = "facebook/xglm-1.7B"
2 tokenizer = AutoTokenizer.from_pretrained(model_name)
----> 3 model_8bit = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map='auto')
File /usr/local/lib/python3.8/dist-packages/transformers/models/auto/auto_factory.py:471, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
469 elif type(config) in cls._model_mapping.keys():
470 model_class = _get_model_class(config, cls._model_mapping)
--> 471 return model_class.from_pretrained(
472 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
473 )
474 raise ValueError(
475 f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
476 f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
477 )
File /usr/local/lib/python3.8/dist-packages/transformers/modeling_utils.py:2556, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
2550 special_dtypes = {
2551 name: torch.float32
2552 for name, _ in model.named_parameters()
2553 if any(m in name for m in keep_in_fp32_modules)
2554 }
2555 if model._no_split_modules is None:
-> 2556 raise ValueError(f"{model.__class__.__name__} does not support `device_map='{device_map}'` yet.")
2557 no_split_modules = model._no_split_modules
2558 if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
ValueError: XGLMForCausalLM does not support `device_map='auto'` yet.
```
### Expected behavior
XGLMForCausalLM should support `device_map='auto'`. | [
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https://api.github.com/repos/huggingface/transformers/issues/24734 |
TITLE
bug: eval_accumulation_steps can lead to incorrect metrics
COMMENTS
3
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### System Info
- `transformers` version: 4.31.0.dev0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (True)
- Tensorflow version (GPU?): 2.11.1 (True)
- Flax version (CPU?/GPU?/TPU?): 0.5.3 (cpu)
- Jax version: 0.3.6
- JaxLib version: 0.3.5
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
### Who can help?
Hey @sgugger, I'm tagging you since this has to do with the trainer.
### Information
- [X] The official example scripts
- [ ] My own modified scripts
### Tasks
- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Using the `run_qa.py` script in the `examples/pytorch/question-answering/` folder
```bash
python run_qa.py \
--model_name_or_path "sjrhuschlee/flan-t5-base-squad2" \
--dataset_name squad_v2 \
--output_dir "tmp/eval_squad_v2/" \
--version_2_with_negative True \
--max_seq_length 512 \
--doc_stride 128 \
--do_eval \
--per_device_eval_batch_size 24 \
--tf32 True \
--dataloader_num_workers 6 \
--preprocessing_num_workers 6 \
--bf16_full_eval \
--eval_accumulation_steps 2 \
--overwrite_output_dir False
```
I found that the calculated metrics when using `eval_accumulation_steps` is not always correct. When not using `eval_accumulation_steps` with the above script I find that I get the expected metrics. However, I found that I needed to use `eval_accumulation_steps` for evaluation of the `flan-t5` models with the above parameters on my system otherwise the memory usage on the GPU would fluctuate from 4 - 8GB which could cause an OOM.
I believe I found the cause for the inconsistency in the metrics. Specifically this line https://github.com/huggingface/transformers/blob/a074a5d34d6411fb00e83a2ed30acf23d8c976b5/src/transformers/trainer.py#L3150
does not cover the edge case where the total number of batches in the evaluation is not exactly divisible by `eval_accumulation_steps`. For example, if `eval_accumulation_steps = 2` and the total number of batches is 613, then only the last batch is used when calculating `all_preds`.
I was able to partially fix this problem by adding a new variable called `total_steps` and updating the if statement
```python
logger.info(f"***** Running {description} *****")
if has_length(dataloader):
total_steps = len(dataloader)
logger.info(f" Num examples = {self.num_examples(dataloader)}")
else:
total_steps = None
logger.info(" Num examples: Unknown")
...
if args.eval_accumulation_steps is not None and (
(step + 1) % args.eval_accumulation_steps == 0 or (step + 1) == total_steps
):
```
However, this will still be a problem for dataloaders that don't have a defined length.
### Expected behavior
Using `eval_accumulation_steps` should work in every case even when the number of batches is not divisible by `eval_accumulation_steps`. | [
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https://api.github.com/repos/huggingface/transformers/issues/23655 |
TITLE
Add EnCodec model
COMMENTS
5
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# What does this PR do?
Adds the EnCodec neural codec from the [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) paper.
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier
Library:
- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger
Integrations:
- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
Documentation: @sgugger, @stevhliu and @MKhalusova
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1
-->
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https://api.github.com/repos/huggingface/transformers/issues/22106 |
TITLE
[i18n-<languageCode>] Translating docs to <languageName>
COMMENTS
0
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<!--
Note: Please search to see if an issue already exists for the language you are trying to translate.
-->
Hi!
Let's bring the documentation to all the <languageName>-speaking community 🌐 (currently 0 out of 267 complete)
Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
Some notes:
* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
* Please translate in a gender-neutral way.
* Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
* Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @ArthurZucker, @sgugger for review.
* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
## Get Started section
- [ ] [index.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.mdx) https://github.com/huggingface/transformers/pull/20180
- [ ] [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx) (waiting for initial PR to go through)
- [ ] [installation.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.mdx).
## Tutorial section
- [ ] [pipeline_tutorial.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.mdx)
- [ ] [autoclass_tutorial.mdx](https://github.com/huggingface/transformers/blob/master/docs/source/autoclass_tutorial.mdx)
- [ ] [preprocessing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.mdx)
- [ ] [training.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.mdx)
- [ ] [accelerate.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.mdx)
- [ ] [model_sharing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.mdx)
- [ ] [multilingual.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.mdx)
<!--
Keep on adding more as you go 🔥
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https://api.github.com/repos/huggingface/transformers/issues/24602 |
TITLE
Support gradient checkpointing for ESM models
COMMENTS
1
REACTIONS
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Would you please add `gradient_checkpointing_enable()` feature for ESM models?
These models currently are the best available pre-trained protein language models for researchers.
Many thanks. | [
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"Feature request"
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https://api.github.com/repos/huggingface/transformers/issues/22570 |
TITLE
Add MobileViT v2
COMMENTS
1
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### Model description
[MobileViT](https://openreview.net/forum?id=vh-0sUt8HlG) is a computer vision model that combines CNNs with transformers that has already been added to Transformers.
[MobileViT v2](https://arxiv.org/abs/2206.02680) is the second version; it is constructed by replacing multi-headed self-attention in MobileViT v1 with the proposed separable self-attention.
Does Hugging Face have plan to add MobileViT v2 to Transformers?
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
The official implementation is from Apple at this link: [https://github.com/apple/ml-cvnets](https://github.com/apple/ml-cvnets)
The timm library also implemented it and has pre-trained weights at this link: [https://github.com/huggingface/pytorch-image-models/blob/82cb47bcf360e1974c00c35c2aa9e242e6b5b565/timm/models/mobilevit.py](https://github.com/huggingface/pytorch-image-models/blob/82cb47bcf360e1974c00c35c2aa9e242e6b5b565/timm/models/mobilevit.py)
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https://api.github.com/repos/huggingface/transformers/issues/23407 |
TITLE
Fix translation no_trainer
COMMENTS
3
REACTIONS
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# What does this PR do?
This PR fixes the reason translation has been failing, by adding in the same `num_beams` that were found to be used in the test.
Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@sgugger, cc @ydshieh | [
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https://api.github.com/repos/huggingface/transformers/issues/22867 |
TITLE
`push_to_hub` with `branch` or `revision` keyword argument
COMMENTS
2
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### Feature request
In `datasets`, you can upload a dataset to a `branch`. In the `transformers` package, it doesn't seem like `branch` or `revision` [are supported](https://huggingface.co/docs/transformers/v4.28.1/en/main_classes/model#transformers.PreTrainedModel.push_to_hub)
### Motivation
To push a model to the hub and with a revision seems a little harder. It seems like I would need to find the cache directory of the model and use `upload_folder` from `huggingface_hub` to upload to the correct revision.
I could very well be missing the right documentation but I can't seem to figure out how/where to do this
### Your contribution
Maybe a PR? | [
19
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https://api.github.com/repos/huggingface/transformers/issues/24216 |
TITLE
Bump transformers from 3.5.1 to 4.30.0 in /examples/research_projects/pplm
COMMENTS
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Bumps [transformers](https://github.com/huggingface/transformers) from 3.5.1 to 4.30.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases">transformers's releases</a>.</em></p>
<blockquote>
<h2>v4.30.0: 100k, Agents improvements, Safetensors core dependency, Swiftformer, Autoformer, MobileViTv2, timm-as-a-backbone</h2>
<h2>100k</h2>
<p>Transformers has just reached 100k stars on GitHub, and to celebrate we wanted to highlight 100 projects in the vicinity of <code>transformers</code> and we have decided to create an <a href="https://github.com/huggingface/transformers/blob/main/awesome-transformers.md">awesome-transformers</a> page to do just that.</p>
<p>We accept PRs to add projects to the list!</p>
<ul>
<li>Top 100 by <a href="https://github.com/LysandreJik"><code>@LysandreJik</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/22912">#22912</a></li>
<li>Add LlamaIndex to awesome-transformers.md by <a href="https://github.com/ravi03071991"><code>@ravi03071991</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23484">#23484</a></li>
<li>add cleanlab to awesome-transformers tools list by <a href="https://github.com/jwmueller"><code>@jwmueller</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23440">#23440</a></li>
</ul>
<h2>4-bit quantization and QLoRA</h2>
<p>By leveraging the <code>bitsandbytes</code> library by <a href="https://github.com/TimDettmers"><code>@TimDettmers</code></a>, we add 4-bit support to <code>transformers</code> models!</p>
<ul>
<li>4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) by <a href="https://github.com/TimDettmers"><code>@TimDettmers</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23479">#23479</a></li>
</ul>
<h2>Agents</h2>
<p>The Agents framework has been improved and continues to be stabilized. Among bug fixes, here are the important new features that were added:</p>
<ul>
<li>Local agent capabilities, to load a generative model directly from <code>transformers</code> instead of relying on APIs.</li>
<li>Prompts are now hosted on the Hub, which means that anyone can fork the prompts and update them with theirs, to let other community contributors re-use them</li>
<li>We add an <code>AzureOpenAiAgent</code> class to support Azure OpenAI agents.</li>
</ul>
<ul>
<li>Add local agent by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23438">#23438</a></li>
<li>Enable prompts on the Hub by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23662">#23662</a></li>
<li>Add AzureOpenAiAgent by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/24058">#24058</a></li>
</ul>
<h2>Safetensors</h2>
<p>The <code>safetensors</code> library is a safe serialization framework for machine learning tensors. It has been audited and will become the default serialization framework for several organizations (Hugging Face, EleutherAI, Stability AI).</p>
<p>It has now become a core dependency of <code>transformers</code>.</p>
<ul>
<li>Making <code>safetensors</code> a core dependency. by <a href="https://github.com/Narsil"><code>@Narsil</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23254">#23254</a></li>
</ul>
<h2>New models</h2>
<h3>Swiftformer</h3>
<p>The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called ‘SwiftFormer’ is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.</p>
<ul>
<li>Add swiftformer by <a href="https://github.com/shehanmunasinghe"><code>@shehanmunasinghe</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/22686">#22686</a></li>
</ul>
<h3>Autoformer</h3>
<p>This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.</p>
<ul>
<li>[Time-Series] Autoformer model by <a href="https://github.com/elisim"><code>@elisim</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/21891">#21891</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/fe861e578f50dc9c06de33cd361d2f625017e624"><code>fe861e5</code></a> [<code>GPT2</code>] Add correct keys on <code>_keys_to_ignore_on_load_unexpected</code> on all chil...</li>
<li><a href="https://github.com/huggingface/transformers/commit/b3e27a80578d022301611363b890107244e12354"><code>b3e27a8</code></a> Update the pin on Accelerate (<a href="https://redirect.github.com/huggingface/transformers/issues/24110">#24110</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/53e1f5cf66d320b9c809f3940c707b6fef435d2d"><code>53e1f5c</code></a> [<code>Trainer</code>] Correct behavior of <code>_load_best_model</code> for PEFT models (<a href="https://redirect.github.com/huggingface/transformers/issues/24103">#24103</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/17db177714b03103bb94cd71b7dd414bc63bffd5"><code>17db177</code></a> reset accelerate env variables after each test (<a href="https://redirect.github.com/huggingface/transformers/issues/24107">#24107</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/905892f09027cab690918c7766fea1bb51bcdd26"><code>905892f</code></a> Release: v4.30.0</li>
<li><a href="https://github.com/huggingface/transformers/commit/c3572e6bfba13ce6dc3fedb05cd1a946ea109576"><code>c3572e6</code></a> Add AzureOpenAiAgent (<a href="https://redirect.github.com/huggingface/transformers/issues/24058">#24058</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/5eb3d3c7023ed0522d3c743ee2e13d896a3aa788"><code>5eb3d3c</code></a> Up pinned accelerate version (<a href="https://redirect.github.com/huggingface/transformers/issues/24089">#24089</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/d1c039e39864a41f6eb8b770a65f123c40164ea5"><code>d1c039e</code></a> fix accelerator prepare during eval only mode (<a href="https://redirect.github.com/huggingface/transformers/issues/24014">#24014</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/2c887cf8e0cb1ac96d28361ff3235a77f83c36ee"><code>2c887cf</code></a> Do not prepare lr scheduler as it as the right number of steps (<a href="https://redirect.github.com/huggingface/transformers/issues/24088">#24088</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/12298cb65c7e9d615b749dde935a0b4966f4ae49"><code>12298cb</code></a> fix executable batch size issue (<a href="https://redirect.github.com/huggingface/transformers/issues/24067">#24067</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v3.5.1...v4.30.0">compare view</a></li>
</ul>
</details>
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</details> | [
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] | [
"dependencies"
] |
https://api.github.com/repos/huggingface/transformers/issues/22786 |
TITLE
Implement a decode method in transformers.BasicTokenizer
COMMENTS
3
REACTIONS
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### Feature request
Transformers has provided a nice BasicTokenizer for basic tokenizing when we don't need BPE tokenizers. For data processing (like data format converting), it is better to offer a decode method for basic use.
### Motivation
When doing data format converting in some data processing problems, we usually meet the requirement to recover a list of tokens into continuous, readable text.
### Your contribution
None. | [
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] | [
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] | [
"Feature request"
] |
https://api.github.com/repos/huggingface/transformers/issues/23036 |
TITLE
[New model] Bark for realistic text-to-speech
COMMENTS
3
REACTIONS
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### Model description
As stated in their [README](https://github.com/suno-ai/bark/blob/main/README.md):
> Bark is a transformer-based text-to-audio model created by [Suno](https://suno.ai/). Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints ready for inference.
Some of their demos are quite amazing (albeit slightly creepy), being able to add "uhms" and "ahhs" in the synthesized audio. For example:
```
Hello, my name is Suno. And, uh — and I like pizza. [laughs]
But I also have other interests such as playing tic tac toe.
```
https://user-images.githubusercontent.com/34592747/238155864-cfa98e54-721c-4b9c-b962-688e09db684f.webm
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
GitHub repo: https://github.com/suno-ai/bark
Author: @gkucsko
Demo: https://huggingface.co/spaces/suno/bark
Model weights: Although not very well documented, [here](https://github.com/suno-ai/bark/blob/2c12023eb22868a633b76357b69d657b374736d9/bark/generation.py#L92-L119) is the portion of the code which links to the model weights. @Vaibhavs10 also looks to have uploaded them to the HF Hub [here](https://huggingface.co/reach-vb/bark-small) 🔥
| [
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] | [
"New model"
] |
https://api.github.com/repos/huggingface/transformers/issues/22240 |
TITLE
Add InternImage
COMMENTS
4
REACTIONS
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### Model description
InternImage is a new large-scale CNN-based foundation model, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that this model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.
It is worth noting that InternImage relies on a custom cuda operator, so if this causes problems for model addition, you can replace [the cuda operator](https://github.com/OpenGVLab/InternImage/blob/master/classification/ops_dcnv3/modules/dcnv3.py#L218) with [a pytorch implementation](https://github.com/OpenGVLab/InternImage/blob/master/classification/ops_dcnv3/modules/dcnv3.py#L91).
In fact, we have already submitted [a version of the code on transformers](https://huggingface.co/OpenGVLab/internimage_t_1k_224/tree/main), however, due to security reasons, the code we submitted cannot call your web inference api, so we would like you to add InternImage to transformers.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
https://github.com/OpenGVLab/InternImage | [
20
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"New model"
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https://api.github.com/repos/huggingface/transformers/issues/24849 |
TITLE
unscale_() has already been called on this optimizer since the last update().
COMMENTS
4
REACTIONS
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Hi all,
I'm facing the error in the subject. I saw this problem have been already solved but I still have this. This is how I configured the parameters for the trainer.
```
trainer = transformers.Trainer(
model=model, # model is decapoda-research/llama-7b-hf
train_dataset=data["train"],
args=transformers.TrainingArguments(
per_device_train_batch_size=MICRO_BATCH_SIZE, # 4 micro batch size
gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS, # 16
auto_find_batch_size=False, # set True to avoid unscale() problem
warmup_steps=100,
num_train_epochs=EPOCHS, #2 epochs
learning_rate=LEARNING_RATE, # 3e-4
fp16=True,
logging_steps=20,
optim="adamw_torch",
output_dir=NAME,
save_total_limit=3,
save_strategy="steps",
save_steps=200,
),
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
```
The strange behaviour is that the problem raises after the end of the first epoch.
```
{'loss': 0.8378, 'learning_rate': 0.00016153846153846153, 'epoch': 0.99}
50%|███████████████████████████████████████████▌ | 831/1660 [15:57<6:52:51, 29.88s/it]
Traceback (most recent call last):
File "/home/paco/dev/stambecco/train.py", line 138, in <module>
trainer.train(resume_from_checkpoint=checkpoint_flag)
File "/home/paco/.local/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "/home/paco/.local/lib/python3.10/site-packages/transformers/trainer.py", line 1850, in _inner_training_loop
self.accelerator.clip_grad_norm_(
File "/home/paco/.local/lib/python3.10/site-packages/accelerate/accelerator.py", line 1893, in clip_grad_norm_
self.unscale_gradients()
File "/home/paco/.local/lib/python3.10/site-packages/accelerate/accelerator.py", line 1856, in unscale_gradients
self.scaler.unscale_(opt)
File "/home/paco/.local/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py", line 275, in unscale_
raise RuntimeError("unscale_() has already been called on this optimizer since the last update().")
RuntimeError: unscale_() has already been called on this optimizer since the last update().
50%|█████ | 831/1660 [16:27<16:24, 1.19s/it]
```
### System Info
The environment is WSL
`Linux 5.15.90.1-microsoft-standard-WSL2 #1 SMP Fri Jan 27 02:56:13 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
`
**pip list**
```
Package Version
------------------------ -------------
accelerate 0.20.3
aiohttp 3.8.4
aiosignal 1.3.1
async-timeout 4.0.2
attrs 23.1.0
bitsandbytes 0.39.1
blinker 1.4
certifi 2022.12.7
charset-normalizer 2.1.1
cmake 3.25.0
command-not-found 0.3
cryptography 3.4.8
datasets 2.13.0
dbus-python 1.2.18
dill 0.3.6
distro 1.7.0
distro-info 1.1build1
filelock 3.9.0
frozenlist 1.3.3
fsspec 2023.6.0
httplib2 0.20.2
huggingface-hub 0.15.1
idna 3.4
importlib-metadata 4.6.4
jeepney 0.7.1
Jinja2 3.1.2
keyring 23.5.0
launchpadlib 1.10.16
lazr.restfulclient 0.14.4
lazr.uri 1.0.6
lit 15.0.7
loralib 0.1.1
MarkupSafe 2.1.2
more-itertools 8.10.0
mpmath 1.2.1
multidict 6.0.4
multiprocess 0.70.14
netifaces 0.11.0
networkx 3.0
numpy 1.24.1
nvidia-cublas-cu11 11.10.3.66
nvidia-cuda-cupti-cu11 11.7.101
nvidia-cuda-nvrtc-cu11 11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11 8.5.0.96
nvidia-cufft-cu11 10.9.0.58
nvidia-curand-cu11 10.2.10.91
nvidia-cusolver-cu11 11.4.0.1
nvidia-cusparse-cu11 11.7.4.91
nvidia-nccl-cu11 2.14.3
nvidia-nvtx-cu11 11.7.91
oauthlib 3.2.0
packaging 23.1
pandas 2.0.2
peft 0.4.0.dev0
Pillow 9.3.0
pip 22.0.2
psutil 5.9.5
pyarrow 12.0.1
PyGObject 3.42.1
PyJWT 2.3.0
pyparsing 2.4.7
python-apt 2.4.0+ubuntu1
python-dateutil 2.8.2
pytz 2023.3
PyYAML 5.4.1
regex 2023.6.3
requests 2.28.1
safetensors 0.3.1
scipy 1.10.1
SecretStorage 3.3.1
sentencepiece 0.1.99
setuptools 59.6.0
six 1.16.0
ssh-import-id 5.11
sympy 1.11.1
systemd-python 234
tokenizers 0.13.3
torch 2.0.1+cu117
torchaudio 2.0.2+cu117
torchvision 0.15.2+cu117
tqdm 4.65.0
transformers 4.31.0.dev0
triton 2.0.0
typing_extensions 4.4.0
tzdata 2023.3
ubuntu-advantage-tools 8001
ufw 0.36.1
unattended-upgrades 0.1
urllib3 1.26.13
wadllib 1.3.6
wheel 0.37.1
xxhash 3.2.0
yarl 1.9.2
zipp 1.0.0
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```
tokenizer = LlamaTokenizer.from_pretrained(
BASE_MODEL, add_eos_token=True
)
model = prepare_model_for_int8_training(model)
print("Preparing LoRA weights")
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=["q_proj", "v_proj"],
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
tokenizer.pad_token_id = 0 # We want this to be different from the eos token
if DATA_PATH.endswith(".json") or DATA_PATH.endswith(".jsonl"):
data = load_dataset("json", data_files=DATA_PATH)
else:
data = load_dataset(DATA_PATH)
# Functions tokenize() and generate_prompt() read the json file with the following format:
# {
# "instruction": "",
# "input": "",
# "output": ""
# },
data = data.shuffle().map(lambda x: tokenize(generate_prompt(x)))
model.print_trainable_parameters()
trainer = transformers.Trainer(
model=model,
train_dataset=data["train"],
args=transformers.TrainingArguments(
per_device_train_batch_size=MICRO_BATCH_SIZE,
gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
auto_find_batch_size=False,
warmup_steps=100,
num_train_epochs=EPOCHS,
learning_rate=LEARNING_RATE,
fp16=True,
logging_steps=20,
optim="adamw_torch",
output_dir=NAME,
save_total_limit=3,
save_strategy="steps",
save_steps=200,
),
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False
checkpoint_folder = os.path.join(os.getcwd(), NAME)
# check if the checkpoint folder exists and is not empty
checkpoint_flag = os.path.isdir(checkpoint_folder) and len(os.listdir(checkpoint_folder))> 0
print(f"Does a checkpoint folder exists? {checkpoint_flag}\n")
trainer.train(resume_from_checkpoint=checkpoint_flag)
model.save_pretrained(f"models/{NAME}")
```
### Expected behavior
Not raising the error and continue with the epoch #2 | [
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"solved"
] |
https://api.github.com/repos/huggingface/transformers/issues/24679 |
TITLE
Custom vision encoder-decoder problem
COMMENTS
1
REACTIONS
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### Model description
I'm trying to make a custom vision encoder-decoder model.
I want to use pre-trained encoder but use decoder from scratch, So I cannot use `VisionEncoderDecoderModel.from_pretrained()`.
Specifically, I want to use pre-trained `deit` model as a encoder, and custom trained `Electra` as a decoder.
I write code like below. In train step, there is no problem.
But I got a problem which says "model have no attribute 'generate'". How can I implement or import `generate` function?
```
class CustomEncoderDecoderModel(nn.Module):
config_class = VisionEncoderDecoderConfig
def __init__(self, encoder_name, decoder_config,
config=None):
super(CustomEncoderDecoderModel, self).__init__()
self.encoder = AutoModel.from_pretrained(encoder_name)
self.decoder_config = decoder_config
self.decoder = AutoModelForCausalLM.from_config(self.decoder_config)
self.config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(self.encoder.config, self.decoder.config)
self.criterion = nn.CrossEntropyLoss()
self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)
def forward(self, pixel_values, labels, decoder_input_ids=None,
decoder_input_embeds=None,
decoder_attention_mask=None,
decoder_inputs_embeds=None,
past_key_values=None):
encoder_outputs = self.encoder(pixel_values,
output_attentions=True)
encoder_hidden_states = encoder_outputs[0]
encoder_attention_mask = None
if decoder_input_ids is None and decoder_input_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.decoder.config.pad_token_id, decoder_start_token_id=2
)
if self.encoder.config.hidden_size != self.decoder.config.hidden_size:
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
decoder_outputs = self.decoder(
input_ids = decoder_input_ids,
attention_mask = decoder_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
output_attentions=True,
use_cache=True,
past_key_values=past_key_values,
)
logits = decoder_outputs[0]
loss = self.criterion(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))
return {'loss': loss, 'logits': logits,
'past_key_values': decoder_outputs.past_key_values,
'decoder_hidden_states': decoder_outputs.hidden_states,
'decoder_attentions': decoder_outputs.attentions,
'cross_attentions': decoder_outputs.cross_attentions,
'encoder_hidden_state': encoder_outputs.hidden_states,
'encoder_attentions': encoder_attention_mask,
'encoder_attentions': encoder_outputs.attentions,
}
```
### Open source status
- [ ] The model implementation is available
- [ ] The model weights are available
### Provide useful links for the implementation
_No response_ | [
20
] | [
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"New model"
] |
https://api.github.com/repos/huggingface/transformers/issues/22156 |
TITLE
[i18n-it] Translating docs to it
COMMENTS
0
REACTIONS
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<!--
Note: Please search to see if an issue already exists for the language you are trying to translate.
-->
Hi!
Let's bring the documentation to all the <languageName>-speaking community 🌐 (currently 0 out of 267 complete)
Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
Some notes:
* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
* Please translate in a gender-neutral way.
* Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
* Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @ArthurZucker, @sgugger for review.
* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
## Get Started section
- [ ] [index.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.mdx) https://github.com/huggingface/transformers/pull/20180
- [ ] [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx) (waiting for initial PR to go through)
- [ ] [installation.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.mdx).
## Tutorial section
- [ ] [pipeline_tutorial.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.mdx)
- [ ] [autoclass_tutorial.mdx](https://github.com/huggingface/transformers/blob/master/docs/source/autoclass_tutorial.mdx)
- [ ] [preprocessing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.mdx)
- [ ] [training.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.mdx)
- [ ] [accelerate.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.mdx)
- [ ] [model_sharing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.mdx)
- [ ] [multilingual.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.mdx)
<!--
Keep on adding more as you go 🔥
-->
## How-to Guide
- [] [big_models](https://github.com/huggingface/transformers/blob/main/docs/source/en/big_models.mdx)
## How-to guides
- [] [big_models](https://github.com/huggingface/transformers/blob/main/docs/source/en/big_models.mdx)
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"WIP"
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https://api.github.com/repos/huggingface/transformers/issues/22490 |
TITLE
Adding a skip_special_tokens Parameter to .encode() in Transformers
COMMENTS
2
REACTIONS
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### Feature request
I would like to propose adding a skip_special_tokens parameter to the .encode() method in Transformers. Currently, in order to achieve this behavior, I have to either create two different tokenizers or use a workaround such as inserting a character in the middle of a special token and then removing it to simulate the desired behavior.
### Motivation
The motivation for this feature request is that in real-world scenarios, users may enter any type of textual data, including special tokens used by the tokenizer. If the tokenizer were to tokenize the user's input as is, it would cause confusion for the whole model and impact the performance of the product. The skip_special_tokens parameter is essential for ensuring the correct processing of user inputs, not just for the `decode()` method but also for the `encode()` and `__call__()` methods.
### Your contribution
I have implemented my own tokenizer that inherits from Transformers and simulates this behavior by removing the special tokens from the vocab before encoding. However, I believe this approach **would not be efficient** for scaling up, as it would cause a lot of memory allocations and deallocations.
To address this issue, I suggest implementing **two separate dictionaries**, one for special tokens and one for the vocabulary, and incorporating an if-statement to test for the skip_special_tokens parameter. This would make the implementation performant and efficient.
Thank you for considering this feature request. | [
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"Feature request"
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https://api.github.com/repos/huggingface/transformers/issues/24841 |
TITLE
Support for caching prompt hidden states through multiple calls of `generate()`
COMMENTS
6
REACTIONS
+1: 1
-1: 0
laugh: 0
hooray: 0
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BODY
### Feature request
Hi there,
I'd like to be able to re-use the hidden states for a common (potentially long) prompt across multiple calls to `model.generate()` in order to reduce redundant computation. Here is how I envision a final API, though I'm sure there are multiple ways to do it.
```python
# Load stuff
model = AutoModel.from_pretrained('huggyllama/llama-7b')
tokenizer = AutoTokenizer.from_pretrained('huggyllama/llama-7b')
# Common prompt that we'd prepend to every example
prompt = "This is a common prompt in every example."
prompt_ids = tokenizer(prompt, return_tensors='pt')
# Examples to pass to generate
examples = ["Ackbar went to", "Billaba enjoys", "Cody is eating some"]
# Generation loop
outputs = []
prompt_hidden_state = None
for ex in examples:
# Current way of doing things
out = model.generate(
**tokenizer(prompt + ex, return_tensors='pt'),
)
# Proposed method to re-use prompt_hidden_state
out = model.generate(
**tokenizer(x, return_tensors='pt'),
common_prompt_ids=prompt_ids,
prompt_hidden_state=prompt_hidden_state
)
prompt_hidden_state = out.prompt_hidden_state
outputs.append(out.sequences)
```
Thanks in advance.
### Motivation
A very common pattern for LLM usage is having a common prompt (e.g., instructions and input/output pairs), a sample input, and asking it to generate the sample output. For example:
```
You are a programmer's assistant which converts English descriptions to Python functions.
English: <example 1 description>
Python: <example 1 function>
English: <example 2 description>
Python: <example 2 function>
English: <example 3 description>
Python: <example 3 function>
English: <input description>
Python:
```
I'd like to be able to cache the common part of the prompt across inputs, that is, everything before `<input description>` which appears in every example to avoid potentially expensive re-computation.
### Your contribution
The only existing info I could find is the short discussion [here](https://discuss.huggingface.co/t/avoid-recalculating-hidden-states-between-generate-calls/34209). I tried messing around a bit to get this to work but had little luck. I'm not familiar with the inner-workings of `transformers` and ran into numerous errors. One problem is padding, which if we're using left padding, can cause some misalignment with the prompt hidden states, e.g.:
```
<p> <p> <p> common prompt x_1 x_2 x_3
<p> <p> common prompt x_1 x_2 x_3 x_4
<p> <p> <p> <p> common prompt x_1 x_2
```
I don't know the best way to solve this. Do we dynamically pad every tensor in `past_key_values`? That seems slow but I don't know if it actually is.
If someone can suggest a better/easier way or maybe give some more pointers on how to solve padding. I'd be happy to try again myself.
Thanks in advance. | [
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"WIP"
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https://api.github.com/repos/huggingface/transformers/issues/22315 |
TITLE
Add MegatronT5
COMMENTS
0
REACTIONS
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### Model description
In NeMo Megatron, the T5 model is available, but there is currently no MegatronT5 class for huggingface, such as MegatronBERT or MegatronGPT2. I have recently finished the porting work and have tested the model internally. I would like to share this model with the community.
### Open source status
- [X] The model implementation is available
- [ ] The model weights are available
### Provide useful links for the implementation
- [NeMo Megatron models](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html)
- [NeMo](https://github.com/NVIDIA/NeMo)
- [Megatron-LM T5 model](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/t5_model.py) | [
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"New model"
] |
https://api.github.com/repos/huggingface/transformers/issues/24063 |
TITLE
Add option for `trust_remote_code=True` on transformers-cli download
COMMENTS
3
REACTIONS
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### Feature request
Currently is very convenient to download models using `transformers-cli download`, however some models need an extra argument for `trust_remote_code=True`
for example
`transformers-cli download "tiiuae/falcon-40b"`
### Motivation
Would it make sense to add
`transformers-cli download "tiiuae/falcon-40b" --trust_remote_code`
### Your contribution
PR | [
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"Feature request"
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https://api.github.com/repos/huggingface/transformers/issues/22317 |
TITLE
Add `MegatronT5ForConditionalGeneration`
COMMENTS
7
REACTIONS
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# What does this PR do?
This PR adds the `MegatronT5ForConditionalGeneration` class, which among standard applications can be used for pretrained T5 model from NVIDIA NeMo MegatronT5 :)
I also add converting script from NeMo MegatronT5 to Huggingface MegatronT5ForConditionalGeneration model
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Fixes #22315
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
cc @ArthurZucker and @younesbelkada
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
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Models:
- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier
Library:
- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger
Integrations:
- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
Documentation: @sgugger, @stevhliu and @MKhalusova
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1
-->
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"PR for Model Addition"
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https://api.github.com/repos/huggingface/transformers/issues/22885 |
TITLE
KeyError: eval_loss when using Trainer (SpeechT5 fine-tuning)
COMMENTS
3
REACTIONS
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### System Info
current main branch of Transformers (4.29.0.dev0, 20 Apr 2023)
### Who can help?
@hollance
### Information
- [X] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
We recently published a Colab notebook for fine-tuning SpeechT5 for TTS. https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ
This notebook worked fine previously but now it gives an error in `trainer.py` because the `eval_loss` is not part of the metrics. This happens when saving the checkpoint. The progress bar in the notebook shows "No log" for the Validation Loss.
I will look into this issue myself first and try to get a smaller reproducible case. My hunch is that something changed in Trainer in between the time I wrote the notebook and now (for example, it now requires Accelerate).
### Expected behavior
The notebook should work as before. | [
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"bug"
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https://api.github.com/repos/huggingface/transformers/issues/24065 |
TITLE
Add CPMBee model
COMMENTS
4
REACTIONS
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# What does this PR do?
Adds the [CPM-Bee](https://github.com/OpenBMB/CPM-Bee/tree/main) pytorch model.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier
Library:
- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger
Integrations:
- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
Documentation: @sgugger, @stevhliu and @MKhalusova
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1
-->
| [
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"Model on the Hub"
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https://api.github.com/repos/huggingface/transformers/issues/24304 |
TITLE
SpikeGPT
COMMENTS
5
REACTIONS
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### Feature request
Extract the spiking nature of the LLM and port that [set] of features over for training/inference,.
https://github.com/ridgerchu/SpikeGPT
### Motivation
the benefits would result in more efficient computational costs (x22 reduction).
### Your contribution
I am willing to test, trace down bugs, and push. I'm still new in the world of llm backend coding. | [
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"New model"
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https://api.github.com/repos/huggingface/transformers/issues/22821 |
TITLE
set fsdp and bf16 don't save memory
COMMENTS
4
REACTIONS
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### System Info
- `transformers` version: 4.28.0
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.12
- Huggingface_hub version: 0.13.4
- Safetensors version: not installed
- PyTorch version (GPU?): 1.13.1+cu117 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script? yes
- Using distributed or parallel set-up in script? yes
### Who can help?
@ArthurZucker @sgu
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. download the dataset
```
lang = "Python"
import subprocess
subprocess.call(["wget", f"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/{lang}.zip"])
subprocess.call(["unzip", f"/content/{lang}.zip"])
!mkdir "log"
log_dir = "/content/log"
!mkdir "data"
data_dir = "/content/data"
!mkdir "model"
model_dir = "/content/model"
!mkdir "tokenizer"
tokenizer_dir = "/content/tokenizer"
```
2. data preprocess
```
import os
import json
import torch
from pathlib import Path
from transformers import (Trainer,
pipeline,
RobertaConfig,
TrainingArguments,
RobertaForMaskedLM,
RobertaTokenizerFast,
LineByLineTextDataset,
DataCollatorForLanguageModeling)
from tokenizers import ByteLevelBPETokenizer
from tokenizers.processors import BertProcessing
from tokenizers.implementations import ByteLevelBPETokenizer
def prepare_text(dir_path):
for path in os.listdir(dir_path):
os.system(f"gunzip -k {dir_path}/{path}")
texts = ""
for path in os.listdir(dir_path):
if path.endswith(".jsonl"):
with open(dir_path + "/" + path, 'r') as f:
sample_file = f.readlines()
for sample in sample_file:
obj = json.loads(sample)
texts += obj["original_string"].replace("\n", "").replace("\t", "") + "\n"
return texts
train1_texts = prepare_text(f"/content/{lang}/final/jsonl/train")
train2_texts = prepare_text(f"/content/{lang}/final/jsonl/valid")
train_texts = train1_texts + "\n" + train2_texts
valid_texts = prepare_text(f"/content/{lang}/final/jsonl/test")
for path, text in zip(["train_texts.txt", "valid_texts.txt"],
[train_texts, valid_texts]):
with open(f"{data_dir}/{path}","w") as f:
f.write(text)
```
3. Train a tokenizer
```
paths = [str(x) for x in Path(f"{data_dir}/").glob("**/*.txt")]
tokenizer = ByteLevelBPETokenizer()
tokenizer.train(files=paths, vocab_size=52_000, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
tokenizer.save_model(tokenizer_dir)
tokenizer = ByteLevelBPETokenizer(
"tokenizer/vocab.json",
"tokenizer/merges.txt",
)
tokenizer._tokenizer.post_processor = BertProcessing(
("</s>", tokenizer.token_to_id("</s>")),
("<s>", tokenizer.token_to_id("<s>")),
)
tokenizer.enable_truncation(max_length=512)
```
4. Build model
```
config = RobertaConfig(
vocab_size=52_000,
max_position_embeddings=514,
num_attention_heads=12,
num_hidden_layers=6,
type_vocab_size=1,
)
tokenizer = RobertaTokenizerFast.from_pretrained(tokenizer_dir, max_len=512)
model = RobertaForMaskedLM(config=config)
model.num_parameters()
train_dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path=f"{data_dir}/train_texts.txt",
block_size=128,
)
test_dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path=f"{data_dir}/valid_texts.txt",
block_size=128,
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)
training_args = TrainingArguments(
output_dir=model_dir,
overwrite_output_dir=True,
num_train_epochs=4,
per_gpu_train_batch_size=64,
save_steps=5000,
do_eval=True,
logging_dir=log_dir,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset = test_dataset
)
trainer.train()
trainer.save_model(model_dir)
tokenizer.save_pretrained(tokenizer_dir)
```
### Expected behavior
before set fsdp and bf16:
```
training_args = TrainingArguments(
output_dir=model_dir,
overwrite_output_dir=True,
num_train_epochs=4,
per_gpu_train_batch_size=64,
save_steps=5000,
do_eval=True,
logging_dir=log_dir,
)
```
<img width="417" alt="Snipaste_2023-04-18_15-42-22" src="https://user-images.githubusercontent.com/41561936/232707188-2579965b-92fd-4ba6-87de-b82ca948ec54.png">
after set fsdp and bf16:
```
training_args = TrainingArguments(
output_dir=model_dir,
overwrite_output_dir=True,
num_train_epochs=4,
per_gpu_train_batch_size=64,
save_steps=5000,
do_eval=True,
logging_dir=log_dir,
fsdp=True,
bf16=True,
)
```
<img width="415" alt="Snipaste_2023-04-18_15-42-45" src="https://user-images.githubusercontent.com/41561936/232707483-2b89c658-172d-4a23-a7fc-fe40cd1dfe83.png">
The memory usage is not much different and does not achieve the desired effect. Why?
I also try to set `per_gpu_train_batch_size=4` when `fsdp=True, bf16=True`:
<img width="426" alt="Snipaste_2023-04-18_15-49-23" src="https://user-images.githubusercontent.com/41561936/232708818-efa676d9-4e6b-440a-b0e0-e66e54026da5.png">
Compared with the results of the previous set of experiments, the increase of memory usage is much greater than the increase of batch size. Why?
| [
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"trainer"
] |
https://api.github.com/repos/huggingface/transformers/issues/24031 |
TITLE
Add scGPT Model
COMMENTS
4
REACTIONS
+1: 0
-1: 0
laugh: 0
hooray: 0
heart: 1
rocket: 0
eyes: 0
BODY
### Model description
scGPT is a single celled foundation model, based off the GPT architecture. The model is shown to have captured meaningful biological insights into cells and genes. The authors state the model can be fine tuned to downstream tasks included, cell-type annotation, genetic perturbation etc.
I'd like to add scGPT to HuggingFace Transformers.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
The paper [scGPT: Towards Building a Foundation Model for Single-Cell
2 Multi-omics Using Generative AI](https://www.biorxiv.org/content/10.1101/2023.04.30.538439v1.full.pdf) by [Haotian Cui](https://www.researchgate.net/scientific-contributions/Haotian-Cui-2193100667), [Chloe Wang](https://www.linkedin.com/in/chloe-xueqi-wang-979712158/?originalSubdomain=ca) , [Hassaan Maan](https://hsmaan.com/), [Bo Wang](https://bowang87.github.io/)
Github link: [scGPT by subercui](https://github.com/bowang-lab/scGPT)
Model Checkpoint: [Google Drive](https://drive.google.com/drive/folders/1kkug5C7NjvXIwQGGaGoqXTk_Lb_pDrBU)
- From this checkpoint I can generate the model weights | [
20
] | [
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"New model"
] |
https://api.github.com/repos/huggingface/transformers/issues/22328 |
TITLE
PyTorch/XLA FSDP doesn't seem to work on TPU-v3-8 VM
COMMENTS
2
REACTIONS
+1: 0
-1: 0
laugh: 0
hooray: 0
heart: 0
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BODY
### System Info
GCP TPU-v3-8 VM
Operating System: Ubuntu 20.04.4 LTS
Kernel: Linux 5.13.0-1027-gcp
transformers 4.28.0.dev0 (pip install git+https://github.com/huggingface/transformers.git on 03/22/2023)
torch 2.0.0
torch-xla 2.0
### Who can help?
People from #21406 that is @AlexWertheim, possibly @pacman100 and @ArthurZucker
### Information
- [X] The official example scripts
- [ ] My own modified scripts
### Tasks
- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
The [glue example with Trainer for TPUs](https://github.com/huggingface/transformers/tree/main/examples/pytorch#running-on-tpus) without FSTP worked flawlessly in my TPU-v3-8 VM with xlm-roberta-base (because the model and batch fit properly within each core).
Now that FSTP was integrated thanks to @AlexWertheim, I tried running facebook/xlm-roberta-xl on this example with the additional parameters.
```bash
python xla_spawn.py --num_cores 8 \
run_glue.py \
--model_name_or_path facebook/xlm-roberta-xl \
--task_name mnli \
--do_train \
--max_seq_length 128 \
--per_device_train_batch_size 4 \
--learning_rate 2e-5 \
--num_train_epochs 10.0 \
--output_dir mnli_output \
--report_to all \
--fsdp 'shard_grad_op' \
--fsdp_config '../fstp_config.json' \
--debug 'tpu_metrics_debug' \
--logging_steps 100 \
--gradient_accumulation_steps 8
```
fstp_config.json:
```json
{
"fsdp_min_num_params": 10000000,
"xla": true,
"xla_fsdp_settings": {}
}
```
I also tried using `"fsdp_transformer_layer_cls_to_wrap": ["XLMRobertaXLModel","XLMRobertaXLClassificationHead"]` instead of `"fsdp_min_num_params": 10000000`. Also `full_shard` instead of `shard_grad_op` and some other variations but they're all giving me the following error:
```bash
0%| | 1/3068000 [08:09<416756:07:35, 489.02s/it]2023-03-23 02:02:19.905715: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:22.081681: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] StackTrace:
2023-03-23 02:02:22.081762: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] *** Begin stack trace ***
2023-03-23 02:02:22.081770: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] tsl::CurrentStackTrace()
2023-03-23 02:02:22.081777: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] xla::util::ReportComputationError(tsl::Status const&, absl::lts_20220623::Span<xla::XlaComputation const* const>, absl::lts_20220623::Span<xla::Shape const* const>)
2023-03-23 02:02:22.081783: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] xla::XrtComputationClient::ExecuteComputation(xla::ComputationClient::Computation const&, absl::lts_20220623::Span<std::shared_ptr<xla::ComputationClient::Data> const>, std::string const&, xla::ComputationClient::ExecuteComputationOptions const&)
2023-03-23 02:02:22.081790: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] torch_xla::XlaBackendImpl::ExecuteComputation(std::shared_ptr<torch::lazy::Computation>, c10::ArrayRef<std::shared_ptr<torch::lazy::BackendData> >, torch::lazy::BackendDevice const&) const
2023-03-23 02:02:22.081809: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081818: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] torch::lazy::MultiWait::Complete(std::function<void ()> const&)
2023-03-23 02:02:22.081825: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081831: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081836: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081842: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] clone
2023-03-23 02:02:22.081847: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] *** End stack trace ***
2023-03-23 02:02:22.081854: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081862: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Status: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2023-03-23 02:02:22.081870: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 2 root error(s) found.
2023-03-23 02:02:22.081878: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] (0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:22.081891: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[{{node XRTExecute}}]]
2023-03-23 02:02:22.081898: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:22.081905: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081911: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[XRTExecute_G10]]
2023-03-23 02:02:22.081920: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:22.081928: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081937: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] (1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:22.081944: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[{{node XRTExecute}}]]
2023-03-23 02:02:22.081951: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:22.081959: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:22.081967: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 0 successful operations.
2023-03-23 02:02:22.081975: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 0 derived errors ignored.
2023-03-23 02:02:22.081983: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Recent warning and error logs:
2023-03-23 02:02:22.081989: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
Exception in device=TPU:1: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2 root error(s) found.
(0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[[XRTExecute_G10]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
(1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
0 successful operations.
0 derived errors ignored.
Recent warning and error logs:
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
Traceback (most recent call last):
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 334, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 328, in _start_fn
fn(gindex, *args)
File "/datadrive/test/run_glue.py", line 622, in _mp_fn
main()
File "/datadrive/test/run_glue.py", line 534, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/trainer.py", line 1644, in train
return inner_training_loop(
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/trainer.py", line 1881, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/parallel_loader.py", line 30, in __next__
return self.next()
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/parallel_loader.py", line 42, in next
xm.mark_step()
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/core/xla_model.py", line 949, in mark_step
torch_xla._XLAC._xla_step_marker(
RuntimeError: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2 root error(s) found.
(0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[[XRTExecute_G10]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
(1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
0 successful operations.
0 derived errors ignored.
Recent warning and error logs:
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:23.050198: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
https://symbolize.stripped_domain/r/?trace=7f7627be9376,7f7627bee41f,0&map=
*** SIGTERM received by PID 89268 (TID 89268) on cpu 51 from PID 89123; stack trace: ***
PC: @ 0x7f7627be9376 (unknown) pthread_cond_wait@@GLIBC_2.3.2
@ 0x7f74d8c2aa1a 1152 (unknown)
@ 0x7f7627bee420 (unknown) (unknown)
@ 0x1 (unknown) (unknown)
https://symbolize.stripped_domain/r/?trace=7f7627be9376,7f74d8c2aa19,7f7627bee41f,0&map=ceee8fa20ddf9c34af43f587221e91de:7f74cbd02000-7f74d8e41840
E0323 02:02:23.479201 89268 coredump_hook.cc:360] RAW: Remote crash gathering disabled for SIGTERM.
E0323 02:02:24.172933 89268 process_state.cc:784] RAW: Raising signal 15 with default behavior
2023-03-23 02:02:25.056856: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] StackTrace:
2023-03-23 02:02:25.056942: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] *** Begin stack trace ***
2023-03-23 02:02:25.056952: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] tsl::CurrentStackTrace()
2023-03-23 02:02:25.056959: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] xla::util::ReportComputationError(tsl::Status const&, absl::lts_20220623::Span<xla::XlaComputation const* const>, absl::lts_20220623::Span<xla::Shape const* const>)
2023-03-23 02:02:25.056967: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] xla::XrtComputationClient::ExecuteComputation(xla::ComputationClient::Computation const&, absl::lts_20220623::Span<std::shared_ptr<xla::ComputationClient::Data> const>, std::string const&, xla::ComputationClient::ExecuteComputationOptions const&)
2023-03-23 02:02:25.056976: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] torch_xla::XlaBackendImpl::ExecuteComputation(std::shared_ptr<torch::lazy::Computation>, c10::ArrayRef<std::shared_ptr<torch::lazy::BackendData> >, torch::lazy::BackendDevice const&) const
2023-03-23 02:02:25.056984: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.056997: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] torch::lazy::MultiWait::Complete(std::function<void ()> const&)
2023-03-23 02:02:25.057005: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057011: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057018: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057025: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] clone
2023-03-23 02:02:25.057033: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] *** End stack trace ***
2023-03-23 02:02:25.057041: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057050: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Status: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2023-03-23 02:02:25.057058: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 2 root error(s) found.
2023-03-23 02:02:25.057067: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] (0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:25.057075: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[{{node XRTExecute}}]]
2023-03-23 02:02:25.057085: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:25.057094: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057102: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[XRTExecute_G12]]
2023-03-23 02:02:25.057111: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:25.057135: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057143: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] (1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:25.057151: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] [[{{node XRTExecute}}]]
2023-03-23 02:02:25.057160: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
2023-03-23 02:02:25.057168: E tensorflow/compiler/xla/xla_client/xla_util.cc:90]
2023-03-23 02:02:25.057176: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 0 successful operations.
2023-03-23 02:02:25.057186: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] 0 derived errors ignored.
2023-03-23 02:02:25.057194: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] Recent warning and error logs:
2023-03-23 02:02:25.057202: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:25.057209: E tensorflow/compiler/xla/xla_client/xla_util.cc:90] OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
Exception in device=TPU:6: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2 root error(s) found.
(0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[[XRTExecute_G12]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
(1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
0 successful operations.
0 derived errors ignored.
Recent warning and error logs:
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
Traceback (most recent call last):
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 334, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 328, in _start_fn
fn(gindex, *args)
File "/datadrive/test/run_glue.py", line 622, in _mp_fn
main()
File "/datadrive/test/run_glue.py", line 534, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/trainer.py", line 1644, in train
return inner_training_loop(
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/transformers/trainer.py", line 1881, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/parallel_loader.py", line 30, in __next__
return self.next()
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/parallel_loader.py", line 42, in next
xm.mark_step()
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/core/xla_model.py", line 949, in mark_step
torch_xla._XLAC._xla_step_marker(
RuntimeError: RESOURCE_EXHAUSTED: From /job:localservice/replica:0/task:0:
2 root error(s) found.
(0) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[[XRTExecute_G12]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
(1) RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
[[{{node XRTExecute}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
0 successful operations.
0 derived errors ignored.
Recent warning and error logs:
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
OP_REQUIRES failed at tpu_execute_op.cc:266 : RESOURCE_EXHAUSTED: Attempting to reserve 10.51G at the bottom of memory. That was not possible. There are 8.97G free, 0B reserved, and 8.97G reservable.
2023-03-23 02:02:29.834867: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Socket closed" and grpc_error_string = "{"created":"@1679536949.834650343","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Socket closed","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835007: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Socket closed" and grpc_error_string = "{"created":"@1679536949.834795697","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Socket closed","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835038: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834893793","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835095: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834956775","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835197: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Socket closed" and grpc_error_string = "{"created":"@1679536949.835008010","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Socket closed","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835206: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834976683","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835408: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Socket closed" and grpc_error_string = "{"created":"@1679536949.835235487","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Socket closed","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835456: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834964014","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835480: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Socket closed" and grpc_error_string = "{"created":"@1679536949.835338354","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Socket closed","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835540: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834899794","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835614: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.834992684","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835687: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.835345000","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
2023-03-23 02:02:29.835752: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Connection reset by peer" and grpc_error_string = "{"created":"@1679536949.835176851","description":"Error received from peer ipv4:127.0.0.1:51011","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Connection reset by peer","grpc_status":14}", maybe retrying the RPC
Traceback (most recent call last):
File "xla_spawn.py", line 83, in <module>
main()
File "xla_spawn.py", line 79, in main
xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 397, in spawn
result = torch.multiprocessing.start_processes(
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 197, in start_processes
while not context.join():
File "/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 149, in join
raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 1 terminated with exit code 17
/home/vitor_jeronymo/miniconda3/envs/torch-xla/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 6 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
```
### Expected behavior
From my understanding, the model was supposed to be split loaded onto the TPU cores, along with whatever `full_shard` entails, but it doesn't seem to be happening. | [
18
] | [
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"PyTorch FSDP"
] |
https://api.github.com/repos/huggingface/transformers/issues/24936 |
TITLE
Add support for Llama-2-70b-chat-hf in transformers
COMMENTS
1
REACTIONS
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### Model description
Not sure if it is a bug, or that it is intentionally not supported yet. In either case: there have been 0 confirmations of people being able to successfully run the official **Llama-2-70b-chat-hf** model in transformers.
### Open source status
- [ ] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
Official model weights:
https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
Related open bug:
https://github.com/facebookresearch/llama/issues/423 | [
20
] | [
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"New model"
] |
https://api.github.com/repos/huggingface/transformers/issues/22257 |
TITLE
Ernie-M for pretraining multilingual models
COMMENTS
4
REACTIONS
+1: 0
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### Feature request
Two things that might help in that regard:
- To train TSDAE, one needs support as class ErnieMForPreTraining, just as for Ernie
https://huggingface.co/docs/transformers/model_doc/ernie#transformers.ErnieForPreTraining
- To train cross-encoders with contrastive loss, a bit like SimCSE, one needs standard support for getting the 'attention_mask' out of the tokenizer sbert uses. Sbert just expects those. Tried to hack it in into sbert, but failed.
### Motivation
Suspect getting Ernie-M-large for pretraining multilingual sentence embeddings will yield close to sota results.
According to mSimCSE, we can get top multilingual embeddings just on training on their 300k dataset of english pairs, alone (worked better than cross-lingual training).
With a stronger base model (they used xlm-roberta), sota embeddings might just lie on the streets.
https://github.com/yaushian/mSimCSE
### Your contribution
Can't do it alone, plz help. | [
20,
19
] | [
0,
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"New model",
"Feature request"
] |
https://api.github.com/repos/huggingface/transformers/issues/25040 |
TITLE
Add ViTMatte model
COMMENTS
0
REACTIONS
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### Model description
ViTMatte is a recently released model for alpha matting on images i.e. background removal.
The model accepts an input image and trimap (manually labelled grayscale image outlining the rough border of the foreground object) and predicts the alpha mate for each pixel.
It introduces a series of small adaptations to the ViT architecture - selective global attention + window attention; adding convolutional blocks between transformers blocks - to reduce computational complexity and enhancing the high-frequency information passed through the network.
At the time of publishing, ViTMatte showed SOTA performance on Distinctions-646 and strong performance (> Mattformer) on Composition-1K.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
Github: https://github.com/hustvl/ViTMatte
Paper: https://arxiv.org/pdf/2305.15272.pdf
Demo: https://colab.research.google.com/drive/1Dc2qoJueNZQyrTU19sIcrPyRDmvuMTF3?usp=sharing | [
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14
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"New model",
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https://api.github.com/repos/huggingface/transformers/issues/24675 |
TITLE
Bump grpcio from 1.44.0 to 1.53.0 in /examples/research_projects/decision_transformer
COMMENTS
2
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Bumps [grpcio](https://github.com/grpc/grpc) from 1.44.0 to 1.53.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/grpc/grpc/releases">grpcio's releases</a>.</em></p>
<blockquote>
<h2>Release v1.53.0</h2>
<p>This is release 1.53.0 (<a href="https://github.com/grpc/grpc/blob/master/doc/g_stands_for.md">glockenspiel</a>) of gRPC Core.</p>
<p>For gRPC documentation, see <a href="https://grpc.io/">grpc.io</a>. For previous releases, see <a href="https://github.com/grpc/grpc/releases">Releases</a>.</p>
<p>This release contains refinements, improvements, and bug fixes, with highlights listed below.</p>
<h2>Core</h2>
<ul>
<li>xDS: fix crash when removing the last endpoint from the last locality in weighted_target. (<a href="https://redirect.github.com/grpc/grpc/pull/32592">#32592</a>)</li>
<li>filter stack: pass peer name up via recv_initial_metadata batch. (<a href="https://redirect.github.com/grpc/grpc/pull/31933">#31933</a>)</li>
<li>[EventEngine] Add advice against blocking work in callbacks. (<a href="https://redirect.github.com/grpc/grpc/pull/32397">#32397</a>)</li>
<li>[http2] Dont drop connections on metadata limit exceeded. (<a href="https://redirect.github.com/grpc/grpc/pull/32309">#32309</a>)</li>
<li>xDS: reject aggregate cluster with empty cluster list. (<a href="https://redirect.github.com/grpc/grpc/pull/32238">#32238</a>)</li>
<li>Fix Python epoll1 Fork Support. (<a href="https://redirect.github.com/grpc/grpc/pull/32196">#32196</a>)</li>
<li>server: introduce ServerMetricRecorder API and move per-call reporting from a C++ interceptor to a C-core filter. (<a href="https://redirect.github.com/grpc/grpc/pull/32106">#32106</a>)</li>
<li>[EventEngine] Add invalid handle types to the public API. (<a href="https://redirect.github.com/grpc/grpc/pull/32202">#32202</a>)</li>
<li>[EventEngine] Refactoring the EventEngine Test Suite: Part 1. (<a href="https://redirect.github.com/grpc/grpc/pull/32127">#32127</a>)</li>
<li>xDS: fix WeightedClusters total weight handling. (<a href="https://redirect.github.com/grpc/grpc/pull/32134">#32134</a>)</li>
</ul>
<h2>C++</h2>
<ul>
<li>Update minimum MSVC version to 2019. (<a href="https://redirect.github.com/grpc/grpc/pull/32615">#32615</a>)</li>
<li>Use CMake variables for paths in pkg-config files. (<a href="https://redirect.github.com/grpc/grpc/pull/31671">#31671</a>)</li>
</ul>
<h2>C#</h2>
<ul>
<li>Grpc.Tools: Use x86 protoc binaries on arm64 Windows. (<a href="https://redirect.github.com/grpc/grpc/pull/32017">#32017</a>)</li>
</ul>
<h2>Python</h2>
<ul>
<li>Support python 3.11 on aarch64. (<a href="https://redirect.github.com/grpc/grpc/pull/32270">#32270</a>)</li>
<li>Include .pyi file. (<a href="https://redirect.github.com/grpc/grpc/pull/32268">#32268</a>)</li>
<li>De-experimentalize wait-for-ready. (<a href="https://redirect.github.com/grpc/grpc/pull/32143">#32143</a>)</li>
<li>De-experimentalize compression. (<a href="https://redirect.github.com/grpc/grpc/pull/32138">#32138</a>)</li>
</ul>
<h2>Ruby</h2>
<ul>
<li>[ruby]: add pre-compiled binaries for ruby 3.2; drop them for ruby 2.6. (<a href="https://redirect.github.com/grpc/grpc/pull/32089">#32089</a>)</li>
</ul>
<h2>Release v1.53.0-pre2</h2>
<p>This is a prerelease of gRPC Core 1.53.0 (glockenspiel).</p>
<p>For gRPC documentation, see <a href="https://grpc.io/">grpc.io</a>. For previous releases, see <a href="https://github.com/grpc/grpc/releases">Releases</a>.</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/grpc/grpc/blob/master/doc/grpc_release_schedule.md">grpcio's changelog</a>.</em></p>
<blockquote>
<h1>gRPC Release Schedule</h1>
<p>Below is the release schedule for gRPC <a href="https://github.com/grpc/grpc-java/releases">Java</a>, <a href="https://github.com/grpc/grpc-go/releases">Go</a> and <a href="https://github.com/grpc/grpc/releases">Core</a> and its dependent languages C++, C#, Objective-C, PHP, Python and Ruby.</p>
<p>Releases are scheduled every six weeks on Tuesdays on a best effort basis. In some unavoidable situations a release may be delayed or released early or a language may skip a release altogether and do the next release to catch up with other languages. See the past releases in the links above. A six-week cycle gives us a good balance between delivering new features/fixes quickly and keeping the release overhead low.</p>
<p>The gRPC release support policy can be found <a href="https://grpc.io/docs/what-is-grpc/faq/#how-long-are-grpc-releases-supported-for">here</a>.</p>
<p>Releases are cut from release branches. For Core and Java repos, the release branch is cut two weeks before the scheduled release date. For Go, the branch is cut just before the release. An RC (release candidate) is published for Core and its dependent languages just after the branch cut. This RC is later promoted to release version if no further changes are made to the release branch. We do our best to keep head of master branch stable at all times regardless of release schedule. Daily build packages from master branch for C#, PHP, Python, Ruby and Protoc plugins are published on <a href="https://packages.grpc.io/">packages.grpc.io</a>. If you depend on gRPC in production we recommend to set up your CI system to test the RCs and, if possible, the daily builds.</p>
<p>Names of gRPC releases are <a href="https://github.com/grpc/grpc/blob/master/doc/g_stands_for.md">here</a>.</p>
<table>
<thead>
<tr>
<th>Release</th>
<th>Scheduled Branch Cut</th>
<th>Scheduled Release Date</th>
</tr>
</thead>
<tbody>
<tr>
<td>v1.17.0</td>
<td>Nov 19, 2018</td>
<td>Dec 4, 2018</td>
</tr>
<tr>
<td>v1.18.0</td>
<td>Jan 2, 2019</td>
<td>Jan 15, 2019</td>
</tr>
<tr>
<td>v1.19.0</td>
<td>Feb 12, 2019</td>
<td>Feb 26, 2019</td>
</tr>
<tr>
<td>v1.20.0</td>
<td>Mar 26, 2019</td>
<td>Apr 9, 2019</td>
</tr>
<tr>
<td>v1.21.0</td>
<td>May 7, 2019</td>
<td>May 21, 2019</td>
</tr>
<tr>
<td>v1.22.0</td>
<td>Jun 18, 2019</td>
<td>Jul 2, 2019</td>
</tr>
<tr>
<td>v1.23.0</td>
<td>Jul 30, 2019</td>
<td>Aug 13, 2019</td>
</tr>
<tr>
<td>v1.24.0</td>
<td>Sept 10, 2019</td>
<td>Sept 24, 2019</td>
</tr>
<tr>
<td>v1.25.0</td>
<td>Oct 22, 2019</td>
<td>Nov 5, 2019</td>
</tr>
<tr>
<td>v1.26.0</td>
<td>Dec 3, 2019</td>
<td>Dec 17, 2019</td>
</tr>
<tr>
<td>v1.27.0</td>
<td>Jan 14, 2020</td>
<td>Jan 28, 2020</td>
</tr>
<tr>
<td>v1.28.0</td>
<td>Feb 25, 2020</td>
<td>Mar 10, 2020</td>
</tr>
<tr>
<td>v1.29.0</td>
<td>Apr 7, 2020</td>
<td>Apr 21, 2020</td>
</tr>
<tr>
<td>v1.30.0</td>
<td>May 19, 2020</td>
<td>Jun 2, 2020</td>
</tr>
<tr>
<td>v1.31.0</td>
<td>Jul 14, 2020</td>
<td>Jul 28, 2020</td>
</tr>
<tr>
<td>v1.32.0</td>
<td>Aug 25, 2020</td>
<td>Sep 8, 2020</td>
</tr>
<tr>
<td>v1.33.0</td>
<td>Oct 6, 2020</td>
<td>Oct 20, 2020</td>
</tr>
<tr>
<td>v1.34.0</td>
<td>Nov 17, 2020</td>
<td>Dec 1, 2020</td>
</tr>
<tr>
<td>v1.35.0</td>
<td>Dec 29, 2020</td>
<td>Jan 12, 2021</td>
</tr>
<tr>
<td>v1.36.0</td>
<td>Feb 9, 2021</td>
<td>Feb 23, 2021</td>
</tr>
<tr>
<td>v1.37.0</td>
<td>Mar 23, 2021</td>
<td>Apr 6, 2021</td>
</tr>
<tr>
<td>v1.38.0</td>
<td>May 4, 2021</td>
<td>May 18, 2021</td>
</tr>
<tr>
<td>v1.39.0</td>
<td>Jun 15, 2021</td>
<td>Jun 29, 2021</td>
</tr>
<tr>
<td>v1.40.0</td>
<td>Jul 27, 2021</td>
<td>Aug 10, 2021</td>
</tr>
<tr>
<td>v1.41.0</td>
<td>Sep 7, 2021</td>
<td>Sep 21, 2021</td>
</tr>
<tr>
<td>v1.42.0</td>
<td>Oct 19, 2021</td>
<td>Nov 2, 2021</td>
</tr>
<tr>
<td>v1.43.0</td>
<td>Nov 30, 2021</td>
<td>Dec 14, 2021</td>
</tr>
</tbody>
</table>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/grpc/grpc/commit/358bfb581feeda5bf17dd3b96da1074d84a6ef8d"><code>358bfb5</code></a> Bump version to 1.53.0 (<a href="https://redirect.github.com/grpc/grpc/issues/32685">#32685</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/6e1ebe76d87a2e9b643c08b3e234d374edcd9e92"><code>6e1ebe7</code></a> Backport: Ensure compatibility with the new custom kokoro win2019 image (<a href="https://redirect.github.com/grpc/grpc/issues/326">#326</a>...</li>
<li><a href="https://github.com/grpc/grpc/commit/44a77f6e911b95e1bc2c909b348123b2da2c4375"><code>44a77f6</code></a> Backport 1.53: Update minimum MSVC version to 2019 (<a href="https://redirect.github.com/grpc/grpc/issues/32615">#32615</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/c11153cb4ef01ca5f83304b2e28edd0182b3c0d0"><code>c11153c</code></a> backport to 1.53: xDS: fix crash when removing the last endpoint from the las...</li>
<li><a href="https://github.com/grpc/grpc/commit/7c7712a6b08ebf1bdc18fc43dc871b47b3dffe97"><code>7c7712a</code></a> Bump version to 1.53.0-pre2. (<a href="https://redirect.github.com/grpc/grpc/issues/32545">#32545</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/a4017dc45e342064722a36181ed14e6d7b469d29"><code>a4017dc</code></a> backport to 1.53: [promises] Make Poll<T> its own type, not a variant<> (<a href="https://redirect.github.com/grpc/grpc/issues/32540">#32540</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/3f93c1667280e6f11a1eb35cccfb8c81c698bee5"><code>3f93c16</code></a> Fuzzer fix backport to v1.53 (<a href="https://redirect.github.com/grpc/grpc/issues/32511">#32511</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/5b244b25c2b87a85781ceeecd34ce0f8e8e7e840"><code>5b244b2</code></a> Bump release version to 1.53.0-pre1 (<a href="https://redirect.github.com/grpc/grpc/issues/32428">#32428</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/6589340efc39b87c94897d221eaf949213cdac87"><code>6589340</code></a> Bump core version 202302161703 (<a href="https://redirect.github.com/grpc/grpc/issues/32416">#32416</a>)</li>
<li><a href="https://github.com/grpc/grpc/commit/d49e1513063e6624e08eb6f59049596178a28783"><code>d49e151</code></a> [backoff] Add random early detection classifier (<a href="https://redirect.github.com/grpc/grpc/issues/32354">#32354</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/grpc/grpc/compare/v1.44.0...v1.53.0">compare view</a></li>
</ul>
</details>
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</details> | [
21
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"dependencies"
] |
https://api.github.com/repos/huggingface/transformers/issues/24201 |
TITLE
Finish dataloader integration
COMMENTS
1
REACTIONS
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# What does this PR do?
Follow up to https://github.com/huggingface/transformers/pull/24028, which removes the TPU-specific dataloader bits.
The `MpDeviceLoader` does already what `Trainer` was doing before, it's just wrapped:
```python
class MpDeviceLoader(object):
"""Wraps an existing PyTorch DataLoader with background data upload.
This class should only be using with multi-processing data parallelism.
Args:
loader (:class:`torch.utils.data.DataLoader`): The PyTorch DataLoader to be
wrapped.
device (`torch.device`...): The device where the data has to be sent.
kwargs: Named arguments for the `ParallelLoader` constructor.
"""
def __init__(self, loader, device, **kwargs):
self._loader = loader
self._device = device
self._parallel_loader_kwargs = kwargs
def __iter__(self):
parallel_loader = ParallelLoader(self._loader, [self._device],
**self._parallel_loader_kwargs)
return parallel_loader.per_device_loader(self._device)
def __len__(self):
return len(self._loader)
```
So the native Accelerate integration will work just fine
Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@sgugger | [
24
] | [
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] | [
"trainer"
] |
https://api.github.com/repos/huggingface/transformers/issues/24038 |
TITLE
Add VGCN-BERT model
COMMENTS
1
REACTIONS
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### Model description
HI, I am the author of [VGCN-BERT paper](https://arxiv.org/abs/2004.05707), the original implementation is in my gitlab [vgcn-bert repo](https://github.com/Louis-udm/VGCN-BERT), but recently I updated the algorithm and implemented a new version for integrating in Transformer.
> Much progress has been made recently on text classification with methods based on neural networks. In particular, models using attention mechanism such as BERT have shown to have the capability of capturing the contextual information within a sentence or document. However, their ability of capturing the global information about the vocabulary of a language is more limited. This latter is the strength of Graph Convolutional Networks (GCN). In this paper, we propose VGCN-BERT model which combines the capability of BERT with a Vocabulary Graph Convolutional Network (VGCN). Local information and global information interact through different layers of BERT, allowing them to influence mutually and to build together a final representation for classification. In our experiments on several text classification datasets, our approach outperforms BERT and GCN alone, and achieve higher effectiveness than that reported in previous studies.
I actually finished the integration of my new version and opened the PR. This new VGCN-BERT algorithm has the following improvements:
- Greatly speeds up the calculation speed of embedding vocabulary graph convolutinal network (or Word Graph embedding). Taking CoLa as an example, the new model only increases the training time by 11% compared with the base model
- Updated subgraph selection algorithm.
- Currently using DistilBert as the base model, but it is easy to migrate to other models.
- Provide two graph construction methods in vgcn_bert/modeling_graph.py (the same NPMI statistical method as the paper, and the predefined entity-relationship mapping method)
I hope that after integrating into transformers, someone can discover some more practical use case. I am ashamed to say that I have not discovered too much real use cases myself, mainly because the word-grounded graph obtained through statistical methods has limited improvement on the LLM model. I think its potential application should be when there are specific/customized graphs that need to be integrated into LLM.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
https://arxiv.org/abs/2004.05707
https://github.com/Louis-udm/VGCN-BERT | [
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"New model"
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https://api.github.com/repos/huggingface/transformers/issues/24671 |
TITLE
Is there any plan to add kosmos-2 to the transformers.
COMMENTS
10
REACTIONS
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### Model description
Kosmos-2 is a grounded multimodal large language model, which integrates grounding and referring capabilities compared with Kosmos-1. The model can accept image regions selected by the user using bounding boxes as input, provide visual answers (i.e., bounding boxes), and ground the text output to the visual world.
**Is there any plan to add this model to the transformers.**
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
Code: https://github.com/microsoft/unilm/tree/master/kosmos-2
Paper: https://arxiv.org/abs/2306.14824
Weight: the checkpoint can be downloaded from [here](https://conversationhub.blob.core.windows.net/beit-share-public/kosmos-2/kosmos-2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | [
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"New model"
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https://api.github.com/repos/huggingface/transformers/issues/24722 |
TITLE
Feature Request: To add nested hierarchy retrieval from Donut response
COMMENTS
2
REACTIONS
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### Feature request
### Donut for hierarchy extraction (Document Parsing)
While preprocessing the ground truth json to the tokens for Donut the processor function (json2token) handles nested hierarchy but the same doesn't hold true for token2json.
Below is an example json:
`
{
"header": "This is 1st header",
"elements": [
{
"text_block": "This is a textblock"
},
{
"header": "1st nested header",
"elements": [
{
"text_block": "This is a sentence"
},
{
"text_block": "Another sentence...."
},
{
"itallic_header": "This is an itallic header",
"elements": [
{
"text_block": "Text 1 inside itallic header.."
},
{
"text_block": "Text 2 inside itallic header.."
}
]
}
]
}
]
}
`
Consider the above json. Applying the json2token function gives the following token sequence.
Function Call:
`output = json2token(temp_test)`
> <s_header>This is 1st header</s_header><s_elements><s_text_block>This is a textblock</s_text_block><sep/><s_header>1st nested header</s_header><s_elements><s_text_block>This is a sentence</s_text_block><sep/><s_text_block>Another sentence....</s_text_block><sep/><s_itallic_header>This is an itallic header</s_itallic_header><s_elements><s_text_block>Text 1 inside itallic header..</s_text_block><sep/><s_text_block>Text 2 inside itallic header..</s_text_block></s_elements></s_elements></s_elements>
This maintains the hierarchy (like parenthesis matching). So, if donut is trained on such data it will give response which parses the information & also retains the hierarchy but the token2json function doesn't handle the conversion properly. Below is the output of the function id passed the token sequence present above.
Function Call:
`processor.token2json(output)`
Output
`
[
{
'header': 'This is 1st header',
'elements': [
{
'text_block': 'This is a textblock'
},
{
'header': '1st nested header',
'text_block': 'This is a sentence'
},
{
'text_block': 'Another sentence....'
},
{
'itallic_header': 'This is an itallic header',
'text_block': 'Text 1 inside itallic header..'
},
{
'text_block': 'Text 2 inside itallic header..'
}
]
}
]
`
Updated Function Results (Preserving the hierarchy):
`
[
{
'header': 'This is 1st header',
'elements': [
{
'text_block': 'This is a textblock'
},
{
'header': '1st nested header',
'elements': [
{
'text_block': 'This is a sentence'
},
{
'text_block': 'Another sentence....'
},
{
'itallic_header': 'This is an itallic header',
'elements': [
{
'text_block': 'Text 1 inside itallic header..'
},
{
'text_block': 'Text 2 inside itallic header..'
}
]
}
]
}
]
}
]
`
Example from CORD:
> temp_test = {
"company": "ADVANCO COMPANY",
"date": "17/01/2018",
"address": "NO 1&3, JALAN WANGSA DELIMA 12, WANGSA LINK, WANGSA MAJU, 53300 KUALA LUMPUR",
"total": "7.00"
}
Updated Function Output:
`
[
{
'company': 'ADVANCO COMPANY',
'date': '17/01/2018',
'address': 'NO 1&3, JALAN WANGSA DELIMA 12, WANGSA LINK, WANGSA MAJU, 53300 KUALA LUMPUR',
'total': '7.00'
}
]
`
### Motivation
Found out about this while working on a project to extract information from images also maintaining the hierarchy/structure of it.
Going through the CORD dataset made me realize that the data itself is not nested in nature. So, thought of testing on a sample the postprocessing logics json -> token & token -> json conversion.
Updated the token2json to get the hierarchy as it is from the token but wasn't sure about the model performance on nested jsons but long story short Donut predicts the hierarchy pretty good.
### Your contribution
`
def token2json(tokens, is_inner_value=False, nested_key = 'elements'):
"""
Convert a (generated) token seuqnce into an ordered JSON format
"""
output = dict()
while tokens:
start_token = re.search(r"<s_(.*?)>", tokens, re.IGNORECASE)
if start_token is None:
break
key = start_token.group(1)
start_matches = re.finditer(fr"<s_{key}>", tokens)
end_matches = re.finditer(fr"</s_{key}>", tokens)
start_tups = [(match.group(), match.start(), match.end()) for match in start_matches]
end_tups = [(match.group(), match.start(), match.end()) for match in end_matches]
mergeTups = start_tups + end_tups
sortedMergeTups = sorted(mergeTups, key=lambda x: x[1])
# remove any unattended close tag for the key present before the current focus start key
updatedIdx = -1
for idx in range(len(sortedMergeTups)):
if start_token.span()[0] == sortedMergeTups[idx][1]:
updatedIdx = idx
break
sortedMergeTups = sortedMergeTups[updatedIdx:]
start_main = sortedMergeTups[0]
match_tracker = 0
end_token = None
if key == nested_key :
if start_main[0] == f'<s_{key}>':
for tup in sortedMergeTups[1:]:
if tup[0] == f'</s_{key}>':
if match_tracker == 0:
end_token = tup
break
else:
match_tracker -= 1
elif tup[0] == f'<s_{key}>':
match_tracker += 1
elif len(sortedMergeTups) > 1:
nextTup = sortedMergeTups[1]
if nextTup[0] == f'</s_{key}>':
end_token = nextTup
if end_token is None:
tokens = tokens.replace(start_token[0], "", 1)
else:
start_token_word = start_main[0]
start_token_id = start_main[2]
end_token_word = end_token[0]
end_token_id = end_token[1]
content = tokens[start_token_id: end_token_id]
if content is not None:
if r"<s_" in content and r"</s_" in content: # non-leaf node
value = token2json(content, is_inner_value=True)
if value:
if len(value) == 1:
value = value[0]
output[key] = value
else: # leaf nodes
if key in output.keys():
if isinstance(output[key], str):
tempVal = output[key]
output[key] = [tempVal]
else:
output[key] = []
for leaf in content.split(r"<sep/>"):
leaf = leaf.strip()
if (
leaf in processor.tokenizer.get_added_vocab()
and leaf[0] == "<"
and leaf[-2:] == "/>"
):
leaf = leaf[1:-2] # for categorical special tokens
output[key].append(leaf)
if len(output[key]) == 1:
output[key] = output[key][0]
tokens = tokens[end_token[2]:]
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + token2json(tokens[6:], is_inner_value=True)
if len(output):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
` | [
19
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"Feature request"
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https://api.github.com/repos/huggingface/transformers/issues/23906 |
TITLE
Move import check to before state reset
COMMENTS
5
REACTIONS
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# What does this PR do?
This PR moves the reset of the `AcceleratorState` to be under the check for if it is available, and guards the resetting better.
Fixes # (issue)
Fixes https://github.com/huggingface/transformers/issues/23898
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@sgugger | [
27
] | [
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"bug"
] |
https://api.github.com/repos/huggingface/transformers/issues/23673 |
TITLE
Bump requests from 2.27.1 to 2.31.0 in /examples/research_projects/decision_transformer
COMMENTS
1
REACTIONS
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Bumps [requests](https://github.com/psf/requests) from 2.27.1 to 2.31.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/psf/requests/releases">requests's releases</a>.</em></p>
<blockquote>
<h2>v2.31.0</h2>
<h2>2.31.0 (2023-05-22)</h2>
<p><strong>Security</strong></p>
<ul>
<li>
<p>Versions of Requests between v2.3.0 and v2.30.0 are vulnerable to potential
forwarding of <code>Proxy-Authorization</code> headers to destination servers when
following HTTPS redirects.</p>
<p>When proxies are defined with user info (<a href="https://user:pass@proxy:8080">https://user:pass@proxy:8080</a>), Requests
will construct a <code>Proxy-Authorization</code> header that is attached to the request to
authenticate with the proxy.</p>
<p>In cases where Requests receives a redirect response, it previously reattached
the <code>Proxy-Authorization</code> header incorrectly, resulting in the value being
sent through the tunneled connection to the destination server. Users who rely on
defining their proxy credentials in the URL are <em>strongly</em> encouraged to upgrade
to Requests 2.31.0+ to prevent unintentional leakage and rotate their proxy
credentials once the change has been fully deployed.</p>
<p>Users who do not use a proxy or do not supply their proxy credentials through
the user information portion of their proxy URL are not subject to this
vulnerability.</p>
<p>Full details can be read in our <a href="https://github.com/psf/requests/security/advisories/GHSA-j8r2-6x86-q33q">Github Security Advisory</a>
and <a href="https://nvd.nist.gov/vuln/detail/CVE-2023-32681">CVE-2023-32681</a>.</p>
</li>
</ul>
<h2>v2.30.0</h2>
<h2>2.30.0 (2023-05-03)</h2>
<p><strong>Dependencies</strong></p>
<ul>
<li>
<p>⚠️ Added support for urllib3 2.0. ⚠️</p>
<p>This may contain minor breaking changes so we advise careful testing and
reviewing <a href="https://urllib3.readthedocs.io/en/latest/v2-migration-guide.html">https://urllib3.readthedocs.io/en/latest/v2-migration-guide.html</a>
prior to upgrading.</p>
<p>Users who wish to stay on urllib3 1.x can pin to <code>urllib3<2</code>.</p>
</li>
</ul>
<h2>v2.29.0</h2>
<h2>2.29.0 (2023-04-26)</h2>
<p><strong>Improvements</strong></p>
<ul>
<li>Requests now defers chunked requests to the urllib3 implementation to improve
standardization. (<a href="https://redirect.github.com/psf/requests/issues/6226">#6226</a>)</li>
<li>Requests relaxes header component requirements to support bytes/str subclasses. (<a href="https://redirect.github.com/psf/requests/issues/6356">#6356</a>)</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/psf/requests/blob/main/HISTORY.md">requests's changelog</a>.</em></p>
<blockquote>
<h2>2.31.0 (2023-05-22)</h2>
<p><strong>Security</strong></p>
<ul>
<li>
<p>Versions of Requests between v2.3.0 and v2.30.0 are vulnerable to potential
forwarding of <code>Proxy-Authorization</code> headers to destination servers when
following HTTPS redirects.</p>
<p>When proxies are defined with user info (<a href="https://user:pass@proxy:8080">https://user:pass@proxy:8080</a>), Requests
will construct a <code>Proxy-Authorization</code> header that is attached to the request to
authenticate with the proxy.</p>
<p>In cases where Requests receives a redirect response, it previously reattached
the <code>Proxy-Authorization</code> header incorrectly, resulting in the value being
sent through the tunneled connection to the destination server. Users who rely on
defining their proxy credentials in the URL are <em>strongly</em> encouraged to upgrade
to Requests 2.31.0+ to prevent unintentional leakage and rotate their proxy
credentials once the change has been fully deployed.</p>
<p>Users who do not use a proxy or do not supply their proxy credentials through
the user information portion of their proxy URL are not subject to this
vulnerability.</p>
<p>Full details can be read in our <a href="https://github.com/psf/requests/security/advisories/GHSA-j8r2-6x86-q33q">Github Security Advisory</a>
and <a href="https://nvd.nist.gov/vuln/detail/CVE-2023-32681">CVE-2023-32681</a>.</p>
</li>
</ul>
<h2>2.30.0 (2023-05-03)</h2>
<p><strong>Dependencies</strong></p>
<ul>
<li>
<p>⚠️ Added support for urllib3 2.0. ⚠️</p>
<p>This may contain minor breaking changes so we advise careful testing and
reviewing <a href="https://urllib3.readthedocs.io/en/latest/v2-migration-guide.html">https://urllib3.readthedocs.io/en/latest/v2-migration-guide.html</a>
prior to upgrading.</p>
<p>Users who wish to stay on urllib3 1.x can pin to <code>urllib3<2</code>.</p>
</li>
</ul>
<h2>2.29.0 (2023-04-26)</h2>
<p><strong>Improvements</strong></p>
<ul>
<li>Requests now defers chunked requests to the urllib3 implementation to improve
standardization. (<a href="https://redirect.github.com/psf/requests/issues/6226">#6226</a>)</li>
<li>Requests relaxes header component requirements to support bytes/str subclasses. (<a href="https://redirect.github.com/psf/requests/issues/6356">#6356</a>)</li>
</ul>
<h2>2.28.2 (2023-01-12)</h2>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/psf/requests/commit/147c8511ddbfa5e8f71bbf5c18ede0c4ceb3bba4"><code>147c851</code></a> v2.31.0</li>
<li><a href="https://github.com/psf/requests/commit/74ea7cf7a6a27a4eeb2ae24e162bcc942a6706d5"><code>74ea7cf</code></a> Merge pull request from GHSA-j8r2-6x86-q33q</li>
<li><a href="https://github.com/psf/requests/commit/302225334678490ec66b3614a9dddb8a02c5f4fe"><code>3022253</code></a> test on pypy 3.8 and pypy 3.9 on windows and macos (<a href="https://redirect.github.com/psf/requests/issues/6424">#6424</a>)</li>
<li><a href="https://github.com/psf/requests/commit/b639e66c816514e40604d46f0088fbceec1a5149"><code>b639e66</code></a> test on py3.12 (<a href="https://redirect.github.com/psf/requests/issues/6448">#6448</a>)</li>
<li><a href="https://github.com/psf/requests/commit/d3d504436ef0c2ac7ec8af13738b04dcc8c694be"><code>d3d5044</code></a> Fixed a small typo (<a href="https://redirect.github.com/psf/requests/issues/6452">#6452</a>)</li>
<li><a href="https://github.com/psf/requests/commit/2ad18e0e10e7d7ecd5384c378f25ec8821a10a29"><code>2ad18e0</code></a> v2.30.0</li>
<li><a href="https://github.com/psf/requests/commit/f2629e9e3c7ce3c3c8c025bcd8db551101cbc773"><code>f2629e9</code></a> Remove strict parameter (<a href="https://redirect.github.com/psf/requests/issues/6434">#6434</a>)</li>
<li><a href="https://github.com/psf/requests/commit/87d63de8739263bbe17034fba2285c79780da7e8"><code>87d63de</code></a> v2.29.0</li>
<li><a href="https://github.com/psf/requests/commit/51716c4ef390136b0d4b800ec7665dd5503e64fc"><code>51716c4</code></a> enable the warnings plugin (<a href="https://redirect.github.com/psf/requests/issues/6416">#6416</a>)</li>
<li><a href="https://github.com/psf/requests/commit/a7da1ab3498b10ec3a3582244c94b2845f8a8e71"><code>a7da1ab</code></a> try on ubuntu 22.04 (<a href="https://redirect.github.com/psf/requests/issues/6418">#6418</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/psf/requests/compare/v2.27.1...v2.31.0">compare view</a></li>
</ul>
</details>
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</details> | [
21
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"dependencies"
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https://api.github.com/repos/huggingface/transformers/issues/23845 |
TITLE
forced_decoder_ids in Whisper models significantly impacts performance, use decoder_input_ids instead
COMMENTS
8
REACTIONS
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### Feature request
@ArthurZucker probably one for you based on commit logs.
Using `forced_decoder_ids` to provide "prompt" and or "prefix" to the whisper model is very inefficient as a forward pass and sampling is done for each token in the `forced_decoder_ids` but the result is already known. Instead the model parameter `decoder_input_ids` could be used which only uses one forward pass to initialise the kv cache with all the input tokens and immediately is sampling useful next tokens.
Openai's whisper limits prompt to half the context length (448 // 2 - 1 = 223) , so if you want to use transformers whisper to behave like openai's whisper and you expect 20 words + EOS in your input feature then forward pass counts are:
- transformers: 244
- openai-whisper: 21
I'm raising this as a feature request rather than a bug or PR as I think `forced_decoder_ids` is already pretty well embedded in the code and the community so I assume it can't just be ripped out and a discussion is probably required before a PR.
Here's some code that demonstrates the issue in IPython:
```python
from transformers import (
WhisperForConditionalGeneration,
WhisperTokenizerFast,
WhisperFeatureExtractor,
)
from datasets import load_dataset
import torch
feature_extractor = WhisperFeatureExtractor()
tokenizer = WhisperTokenizerFast.from_pretrained("openai/whisper-tiny.en", language="english")
# Patch WhisperForConditionalGeneration._prepare_decoder_input_ids_for_generation because the one on GenerationMixin doesn't handle whisper properly.
def prepare_decoder_input_ids_for_generation_patch(self, batch_size, model_input_name, model_kwargs, decoder_start_token_id, bos_token_id, device):
if 'decoder_input_ids' not in model_kwargs:
return torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id, model_kwargs
else:
return model_kwargs.pop('decoder_input_ids'), model_kwargs
WhisperForConditionalGeneration._prepare_decoder_input_ids_for_generation = prepare_decoder_input_ids_for_generation_patch
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
audio = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")[3]["audio"]["array"]
input_features = feature_extractor(audio, sampling_rate=16000, return_tensors="pt").input_features
# A custom logits processor to show how many times the forward pass and sample are run
def logits_processor_count_factory():
count = 0
def logits_processor_count(input_ids, scores):
nonlocal count
count += 1
print(count)
return scores
return logits_processor_count
PREV_TOKEN = 50360 # <|startofprev|>
prompt_tokens = [PREV_TOKEN, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284, 7062, 465, 21443, 13, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236, 878, 514, 11, 985, 2915, 7428, 422, 6600, 290, 663, 2482, 3051, 749, 14704, 284, 262, 2000, 13]
# note prompt_ids is prefixed to forced_decoder_ids inside generate
# counts to 106
forced_decoder_ids_output = model.generate(input_features=input_features, return_timestamps=False, prompt_ids=torch.LongTensor(prompt_tokens), logits_processor=[logits_processor_count_factory()])[0]
print(tokenizer.decode(forced_decoder_ids_output, decode_with_timestamps=False))
SOT_TOKEN = 50257 # <|startoftranscript|>
NO_TIMESTAMPS_TOKEN = 50362 # <|notimestamps|>
decoder_input_ids = torch.LongTensor([prompt_tokens + [SOT_TOKEN, NO_TIMESTAMPS_TOKEN]])
# counts to 31
decoder_input_ids_output = model.generate(input_features=input_features, return_timestamps=False, forced_decoder_ids=None, begin_suppress_tokens=None, decoder_input_ids=decoder_input_ids, logits_processor=[logits_processor_count_factory()])[0]
print(tokenizer.decode(decoder_input_ids_output, decode_with_timestamps=False))
```
You can get performance for bothing in IPython doing:
```python
%timeit model.generate(input_features=input_features, return_timestamps=False, prompt_ids=torch.LongTensor(prompt_tokens))[0]
%timeit model.generate(input_features=input_features, return_timestamps=False, forced_decoder_ids=None, begin_suppress_tokens=None, decoder_input_ids=decoder_input_ids)[0]
```
On CPU for me using decoder_input_ids is 2x faster with this input.
### Motivation
I want to be able to use the transformers implementation of whisper in a production system where cost and processing time will be critical, due to the way we are using whisper this issue impact performance a lot more than the 2x I quoted above, its more like 5x in our use case. Obviously we can code around it but if it's possible to change transformers and avoid custom code I'd prefer that.
### Your contribution
I'd be able to create a PR but without knowing more about how the maintainers would like to handle backward compatibility etc I don't think its the right place to start.
I'd be very happy to be involved in a discussion, offer opinions or testing etc. | [
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https://api.github.com/repos/huggingface/transformers/issues/22372 |
TITLE
Add Restormer
COMMENTS
1
REACTIONS
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### Model description
**Restormer: Efficient Transformer for High-Resolution Image Restoration** was published in CVPR 2022, which introduced a new Vision Transformer based architecture for Image Restoration tasks like Deraining, Motion Deblurring, Defocus Deblurring and Denoising. It reduced the time complexity of Self Attention in Vision Transformers from O(n<sup>2</sup>) to O(n) by introducing **Multi-Dconv Head Transposed Attention**. It also introduced **Gated-Dconv Feed-Forward Network**.
@manyana72 and I would like to add this model to Huggingface.
cc: @NielsRogge
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
[Paper](https://arxiv.org/pdf/2111.09881.pdf), [Code Implementation](https://github.com/swz30/Restormer) and [pretrained model weights](https://github.com/swz30/Restormer/releases/tag/v1.0) | [
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https://api.github.com/repos/huggingface/transformers/issues/22160 |
TITLE
[i18n-it] Translating docs to it
COMMENTS
1
REACTIONS
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<!--
Note: Please search to see if an issue already exists for the language you are trying to translate.
-->
Hi!
Let's bring the documentation to all the <languageName>-speaking community 🌐 (currently 0 out of 267 complete)
Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
Some notes:
* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
* Please translate in a gender-neutral way.
* Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
* Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @ArthurZucker, @sgugger for review.
* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
## Get Started section
- [ ] [index.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.mdx) https://github.com/huggingface/transformers/pull/20180
- [ ] [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx) (waiting for initial PR to go through)
- [ ] [installation.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.mdx).
## Tutorial section
- [ ] [pipeline_tutorial.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.mdx)
- [ ] [autoclass_tutorial.mdx](https://github.com/huggingface/transformers/blob/master/docs/source/autoclass_tutorial.mdx)
- [ ] [preprocessing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.mdx)
- [ ] [training.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.mdx)
- [ ] [accelerate.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.mdx)
- [ ] [model_sharing.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.mdx)
- [ ] [multilingual.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.mdx)
<!--
Keep on adding more as you go 🔥
-->
## How-to guides
- [] [perf_infer_gpu_one](https://github.com/huggingface/transformers/blob/main/docs/source/en/perf_infer_gpu_one.mdx)
| [
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https://api.github.com/repos/huggingface/transformers/issues/22410 |
TITLE
Bump redis from 4.1.4 to 4.5.3 in /examples/research_projects/decision_transformer
COMMENTS
1
REACTIONS
+1: 0
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Bumps [redis](https://github.com/redis/redis-py) from 4.1.4 to 4.5.3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/redis/redis-py/releases">redis's releases</a>.</em></p>
<blockquote>
<h2>4.5.3</h2>
<h1>Changes</h1>
<p>Update urgency: HIGH: There is a critical bug that may affect a subset of users. Upgrade!</p>
<h2>🐛 Bug Fixes</h2>
<ul>
<li><a href="https://cwe.mitre.org/data/definitions/404.html">CWE-404</a> AsyncIO Race Condition Fix (<a href="https://redirect.github.com/redis/redis-py/issues/2624">#2624</a>, <a href="https://redirect.github.com/redis/redis-py/issues/2579">#2579</a>)</li>
</ul>
<h2>4.5.2</h2>
<h1>Changes</h1>
<h2>🚀 New Features</h2>
<ul>
<li>Introduce AbstractConnection so that UnixDomainSocketConnection can call super().<strong>init</strong> (<a href="https://redirect.github.com/redis/redis-py/issues/2588">#2588</a>)</li>
<li>Added queue_class to REDIS_ALLOWED_KEYS (<a href="https://redirect.github.com/redis/redis-py/issues/2577">#2577</a>)</li>
<li>Made search document subscriptable (<a href="https://redirect.github.com/redis/redis-py/issues/2615">#2615</a>)</li>
<li>Sped up the protocol parsing (<a href="https://redirect.github.com/redis/redis-py/issues/2596">#2596</a>)</li>
</ul>
<h2>🐛 Bug Fixes</h2>
<ul>
<li>Fix behaviour of async PythonParser to match RedisParser as for issue <a href="https://redirect.github.com/redis/redis-py/issues/2349">#2349</a> (<a href="https://redirect.github.com/redis/redis-py/issues/2582">#2582</a>)</li>
<li>Replace async_timeout by asyncio.timeout (<a href="https://redirect.github.com/redis/redis-py/issues/2602">#2602</a>)</li>
<li>Update json().arrindex() default values (<a href="https://redirect.github.com/redis/redis-py/issues/2611">#2611</a>)</li>
</ul>
<h2>🧰 Maintenance</h2>
<ul>
<li>Coverage for pypy-3.9 (<a href="https://redirect.github.com/redis/redis-py/issues/2608">#2608</a>)</li>
<li>Developer Experience: Adding redis version compatibility details to the README (<a href="https://redirect.github.com/redis/redis-py/issues/2621">#2621</a>)</li>
<li>Remove redundant assignment to RedisCluster.nodes_manager. (<a href="https://redirect.github.com/redis/redis-py/issues/2620">#2620</a>)</li>
<li>Developer Experience: [types] update return type of smismember to list[int] (<a href="https://redirect.github.com/redis/redis-py/issues/2617">#2617</a>)</li>
<li>Developer Experience: [docs] ConnectionPool SSL example (<a href="https://redirect.github.com/redis/redis-py/issues/2605">#2605</a>)</li>
<li>Developer Experience: Fixed CredentialsProvider examples (<a href="https://redirect.github.com/redis/redis-py/issues/2587">#2587</a>)</li>
<li>Developer Experience: Update README to make pip install copy-pastable on zsh (<a href="https://redirect.github.com/redis/redis-py/issues/2584">#2584</a>)</li>
<li>Developer Experience: Fix for <code>lpop</code> and <code>rpop</code> return typing (<a href="https://redirect.github.com/redis/redis-py/issues/2590">#2590</a>)</li>
</ul>
<h2>Contributors</h2>
<p>We'd like to thank all the contributors who worked on this release!</p>
<p><a href="https://github.com/CrimsonGlory"><code>@CrimsonGlory</code></a>, <a href="https://github.com/Galtozzy"><code>@Galtozzy</code></a>, <a href="https://github.com/aksinha334"><code>@aksinha334</code></a>, <a href="https://github.com/barshaul"><code>@barshaul</code></a>, <a href="https://github.com/chayim"><code>@chayim</code></a>, <a href="https://github.com/davemcphee"><code>@davemcphee</code></a>, <a href="https://github.com/dvora-h"><code>@dvora-h</code></a>, <a href="https://github.com/kristjanvalur"><code>@kristjanvalur</code></a>, <a href="https://github.com/ryin1"><code>@ryin1</code></a>, <a href="https://github.com/sileht"><code>@sileht</code></a>, <a href="https://github.com/thebarbershop"><code>@thebarbershop</code></a>, <a href="https://github.com/uglide"><code>@uglide</code></a>, <a href="https://github.com/woutdenolf"><code>@woutdenolf</code></a> and <a href="https://github.com/zakaf"><code>@zakaf</code></a></p>
<h2>4.5.1</h2>
<h1>Changes</h1>
<h2>🐛 Bug Fixes</h2>
<ul>
<li>Fix <a href="https://redirect.github.com/redis/redis-py/issues/2581">#2581</a> <code>UnixDomainSocketConnection</code> object has no attribute <code>_command_packer</code> (<a href="https://redirect.github.com/redis/redis-py/issues/2583">#2583</a>)</li>
</ul>
<h2>Contributors</h2>
<p>We'd like to thank all the contributors who worked on this release!</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/redis/redis-py/commit/66a4d6b2a493dd3a20cc299ab5fef3c14baad965"><code>66a4d6b</code></a> AsyncIO Race Condition Fix (<a href="https://redirect.github.com/redis/redis-py/issues/2641">#2641</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/318b114f4da9846a2a7c150e1fb702e9bebd9fdf"><code>318b114</code></a> Version 4.5.2 (<a href="https://redirect.github.com/redis/redis-py/issues/2627">#2627</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/1b2f408259405d412d7530291902f9e0c8bd34b3"><code>1b2f408</code></a> Fix behaviour of async PythonParser to match RedisParser as for issue <a href="https://redirect.github.com/redis/redis-py/issues/2349">#2349</a> (...</li>
<li><a href="https://github.com/redis/redis-py/commit/7d474f90453c7b90bd06c94e0250b618120a599d"><code>7d474f9</code></a> introduce AbstractConnection so that UnixDomainSocketConnection can call supe...</li>
<li><a href="https://github.com/redis/redis-py/commit/c87172347584301f453c601c483126e4800257b7"><code>c871723</code></a> pypy-3.9 CI (<a href="https://redirect.github.com/redis/redis-py/issues/2608">#2608</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/d63313bf6080acaf18d61e072c78303adc0d4166"><code>d63313b</code></a> add queue_class to REDIS_ALLOWED_KEYS (<a href="https://redirect.github.com/redis/redis-py/issues/2577">#2577</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/c61eeb2e3b5dff1f01eb1e665f424c7e75354f56"><code>c61eeb2</code></a> Adding supported redis/library details (<a href="https://redirect.github.com/redis/redis-py/issues/2621">#2621</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/25e85e51e57b7aae9eb8fc77cfb0a45a07a501a7"><code>25e85e5</code></a> fix: replace async_timeout by asyncio.timeout (<a href="https://redirect.github.com/redis/redis-py/issues/2602">#2602</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/91ab12a0f1bdf0e433131e1a51578e9fa2f89718"><code>91ab12a</code></a> Remove redundant assignment. (<a href="https://redirect.github.com/redis/redis-py/issues/2620">#2620</a>)</li>
<li><a href="https://github.com/redis/redis-py/commit/8bfd492240fd33489a86cd3d353e3ece1fc94c10"><code>8bfd492</code></a> Making search document subscriptable (<a href="https://redirect.github.com/redis/redis-py/issues/2615">#2615</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/redis/redis-py/compare/v4.1.4...v4.5.3">compare view</a></li>
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https://api.github.com/repos/huggingface/transformers/issues/24842 |
TITLE
Request support for RWKV-4-World model.
COMMENTS
7
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### Model description
As RWKV-4-World is using a different tokenizer and vocabs, the current RWKV support in transformers is incompatible.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
https://huggingface.co/StarRing2022/RWKV-4-World-1.5B
@StarRing2022 | [
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https://api.github.com/repos/huggingface/transformers/issues/24699 |
TITLE
Bump scipy from 1.8.0 to 1.10.0 in /examples/research_projects/decision_transformer
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Bumps [scipy](https://github.com/scipy/scipy) from 1.8.0 to 1.10.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/scipy/scipy/releases">scipy's releases</a>.</em></p>
<blockquote>
<h1>SciPy 1.10.0 Release Notes</h1>
<p>SciPy <code>1.10.0</code> is the culmination of <code>6</code> months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with <code>python -Wd</code> and check for <code>DeprecationWarning</code> s).
Our development attention will now shift to bug-fix releases on the
1.10.x branch, and on adding new features on the main branch.</p>
<p>This release requires Python <code>3.8+</code> and NumPy <code>1.19.5</code> or greater.</p>
<p>For running on PyPy, PyPy3 <code>6.0+</code> is required.</p>
<h1>Highlights of this release</h1>
<ul>
<li>A new dedicated datasets submodule (<code>scipy.datasets</code>) has been added, and is
now preferred over usage of <code>scipy.misc</code> for dataset retrieval.</li>
<li>A new <code>scipy.interpolate.make_smoothing_spline</code> function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.</li>
<li><code>scipy.stats</code> has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.</li>
</ul>
<h1>New features</h1>
<h1><code>scipy.datasets</code> introduction</h1>
<ul>
<li>A new dedicated <code>datasets</code> submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from <code>scipy.misc</code> have been added to <code>scipy.datasets</code>
(and deprecated in <code>scipy.misc</code>).</li>
<li>The submodule is based on <a href="https://www.fatiando.org/pooch/latest/">Pooch</a>
(a new optional dependency for SciPy), a Python package to simplify fetching
data files. This move will, in a subsequent release, facilitate SciPy
to trim down the sdist/wheel sizes, by decoupling the data files and
moving them out of the SciPy repository, hosting them externally and</li>
</ul>
<!-- raw HTML omitted -->
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</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/scipy/scipy/commit/dde50595862a4f9cede24b5d1c86935c30f1f88a"><code>dde5059</code></a> REL: 1.10.0 final [wheel build]</li>
<li><a href="https://github.com/scipy/scipy/commit/7856f281b016c585b82d03723c4494bcdbdcd4a5"><code>7856f28</code></a> Merge pull request <a href="https://redirect.github.com/scipy/scipy/issues/17696">#17696</a> from tylerjereddy/treddy_110_final_prep</li>
<li><a href="https://github.com/scipy/scipy/commit/205b6243c6d075d05695e7ac6d007e0f03bfbf42"><code>205b624</code></a> DOC: add missing author</li>
<li><a href="https://github.com/scipy/scipy/commit/1ab9f1b10145f0a974d5531700e72d1fb4229b76"><code>1ab9f1b</code></a> DOC: update 1.10.0 relnotes</li>
<li><a href="https://github.com/scipy/scipy/commit/ac2f45fbe1e39a8f52c1ea2e68764009f02973c0"><code>ac2f45f</code></a> MAINT: integrate._qmc_quad: mark as private with preceding underscore</li>
<li><a href="https://github.com/scipy/scipy/commit/3e0ae1a21f51ebee3a77733c42700d87a0c35d7d"><code>3e0ae1a</code></a> REV: integrate.qmc_quad: delay release to SciPy 1.11.0</li>
<li><a href="https://github.com/scipy/scipy/commit/34cdf05c86548de1c4ca1b2798cdc23885af807b"><code>34cdf05</code></a> MAINT: FFT pybind11 fixups</li>
<li><a href="https://github.com/scipy/scipy/commit/843500aabde17aaf1eec65c589d50bd12ee35039"><code>843500a</code></a> Merge pull request <a href="https://redirect.github.com/scipy/scipy/issues/17689">#17689</a> from mdhaber/gh17686</li>
<li><a href="https://github.com/scipy/scipy/commit/089924b61012a106ffa4f58939b0180124051a0b"><code>089924b</code></a> REL: integrate.qmc_quad: remove from release notes</li>
<li><a href="https://github.com/scipy/scipy/commit/3e47110f10e3267d228e9da84174f3cee325e7c3"><code>3e47110</code></a> REL: 1.10.0rc3 unreleased</li>
<li>Additional commits viewable in <a href="https://github.com/scipy/scipy/compare/v1.8.0...v1.10.0">compare view</a></li>
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https://api.github.com/repos/huggingface/transformers/issues/25096 |
TITLE
Bump certifi from 2022.12.7 to 2023.7.22 in /examples/research_projects/lxmert
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Bumps [certifi](https://github.com/certifi/python-certifi) from 2022.12.7 to 2023.7.22.
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/certifi/python-certifi/commit/8fb96ed81f71e7097ed11bc4d9b19afd7ea5c909"><code>8fb96ed</code></a> 2023.07.22</li>
<li><a href="https://github.com/certifi/python-certifi/commit/afe77220e0eaa722593fc5d294213ff5275d1b40"><code>afe7722</code></a> Bump actions/setup-python from 4.6.1 to 4.7.0 (<a href="https://redirect.github.com/certifi/python-certifi/issues/230">#230</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/2038739ad56abec7aaddfa90ad2ce6b3ed7f5c7b"><code>2038739</code></a> Bump dessant/lock-threads from 3.0.0 to 4.0.1 (<a href="https://redirect.github.com/certifi/python-certifi/issues/229">#229</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/44df761f4c09d19f32b3cc09208a739043a5e25b"><code>44df761</code></a> Hash pin Actions and enable dependabot (<a href="https://redirect.github.com/certifi/python-certifi/issues/228">#228</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/8b3d7bae85bbc87c9181cc1d39548db3d31627f0"><code>8b3d7ba</code></a> 2023.05.07</li>
<li><a href="https://github.com/certifi/python-certifi/commit/53da2405b1af430f6bafa21ba45d8dd8dfc726b8"><code>53da240</code></a> ci: Add Python 3.12-dev to the testing (<a href="https://redirect.github.com/certifi/python-certifi/issues/224">#224</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/c2fc3b1f64d6946f1057971ee897ea828ae848d8"><code>c2fc3b1</code></a> Create a Security Policy (<a href="https://redirect.github.com/certifi/python-certifi/issues/222">#222</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/c211ef482a01aff5f1bc92c4128bfa0c955f4a01"><code>c211ef4</code></a> Set up permissions to github workflows (<a href="https://redirect.github.com/certifi/python-certifi/issues/218">#218</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/2087de5d0aa1d472145fc1dbdfece3fe652bbac5"><code>2087de5</code></a> Don't let deprecation warning fail CI (<a href="https://redirect.github.com/certifi/python-certifi/issues/219">#219</a>)</li>
<li><a href="https://github.com/certifi/python-certifi/commit/e0b9fc5c8f52ac8c300da502e5760ce3d41429ec"><code>e0b9fc5</code></a> remove paragraphs about 1024-bit roots from README</li>
<li>Additional commits viewable in <a href="https://github.com/certifi/python-certifi/compare/2022.12.07...2023.07.22">compare view</a></li>
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https://api.github.com/repos/huggingface/transformers/issues/24915 |
TITLE
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
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### Model description
ViTPose is used in 2D human pose estimation, a subset of the keypoint detection task #24044
It provides a simple baseline for vision transformer-based human pose estimation. It utilises a pretrained vision transformer backbone to extract features and a simple decoder head to process the extracted features. Despite no elaborate designs in the model, ViTPose obtained state-of-the-art (SOTA) performance of 80.9 AP on the MS COCO Keypoint test-dev set.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
Code and weights: https://github.com/ViTAE-Transformer/ViTPose
Paper: https://arxiv.org/abs/2204.12484
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https://api.github.com/repos/huggingface/transformers/issues/24221 |
TITLE
Bump transformers from 4.26.0 to 4.30.0 in /examples/research_projects/vqgan-clip
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Bumps [transformers](https://github.com/huggingface/transformers) from 4.26.0 to 4.30.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases">transformers's releases</a>.</em></p>
<blockquote>
<h2>v4.30.0: 100k, Agents improvements, Safetensors core dependency, Swiftformer, Autoformer, MobileViTv2, timm-as-a-backbone</h2>
<h2>100k</h2>
<p>Transformers has just reached 100k stars on GitHub, and to celebrate we wanted to highlight 100 projects in the vicinity of <code>transformers</code> and we have decided to create an <a href="https://github.com/huggingface/transformers/blob/main/awesome-transformers.md">awesome-transformers</a> page to do just that.</p>
<p>We accept PRs to add projects to the list!</p>
<ul>
<li>Top 100 by <a href="https://github.com/LysandreJik"><code>@LysandreJik</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/22912">#22912</a></li>
<li>Add LlamaIndex to awesome-transformers.md by <a href="https://github.com/ravi03071991"><code>@ravi03071991</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23484">#23484</a></li>
<li>add cleanlab to awesome-transformers tools list by <a href="https://github.com/jwmueller"><code>@jwmueller</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23440">#23440</a></li>
</ul>
<h2>4-bit quantization and QLoRA</h2>
<p>By leveraging the <code>bitsandbytes</code> library by <a href="https://github.com/TimDettmers"><code>@TimDettmers</code></a>, we add 4-bit support to <code>transformers</code> models!</p>
<ul>
<li>4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) by <a href="https://github.com/TimDettmers"><code>@TimDettmers</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23479">#23479</a></li>
</ul>
<h2>Agents</h2>
<p>The Agents framework has been improved and continues to be stabilized. Among bug fixes, here are the important new features that were added:</p>
<ul>
<li>Local agent capabilities, to load a generative model directly from <code>transformers</code> instead of relying on APIs.</li>
<li>Prompts are now hosted on the Hub, which means that anyone can fork the prompts and update them with theirs, to let other community contributors re-use them</li>
<li>We add an <code>AzureOpenAiAgent</code> class to support Azure OpenAI agents.</li>
</ul>
<ul>
<li>Add local agent by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23438">#23438</a></li>
<li>Enable prompts on the Hub by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23662">#23662</a></li>
<li>Add AzureOpenAiAgent by <a href="https://github.com/sgugger"><code>@sgugger</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/24058">#24058</a></li>
</ul>
<h2>Safetensors</h2>
<p>The <code>safetensors</code> library is a safe serialization framework for machine learning tensors. It has been audited and will become the default serialization framework for several organizations (Hugging Face, EleutherAI, Stability AI).</p>
<p>It has now become a core dependency of <code>transformers</code>.</p>
<ul>
<li>Making <code>safetensors</code> a core dependency. by <a href="https://github.com/Narsil"><code>@Narsil</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/23254">#23254</a></li>
</ul>
<h2>New models</h2>
<h3>Swiftformer</h3>
<p>The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called ‘SwiftFormer’ is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.</p>
<ul>
<li>Add swiftformer by <a href="https://github.com/shehanmunasinghe"><code>@shehanmunasinghe</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/22686">#22686</a></li>
</ul>
<h3>Autoformer</h3>
<p>This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.</p>
<ul>
<li>[Time-Series] Autoformer model by <a href="https://github.com/elisim"><code>@elisim</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/21891">#21891</a></li>
</ul>
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<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/huggingface/transformers/commit/fe861e578f50dc9c06de33cd361d2f625017e624"><code>fe861e5</code></a> [<code>GPT2</code>] Add correct keys on <code>_keys_to_ignore_on_load_unexpected</code> on all chil...</li>
<li><a href="https://github.com/huggingface/transformers/commit/b3e27a80578d022301611363b890107244e12354"><code>b3e27a8</code></a> Update the pin on Accelerate (<a href="https://redirect.github.com/huggingface/transformers/issues/24110">#24110</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/53e1f5cf66d320b9c809f3940c707b6fef435d2d"><code>53e1f5c</code></a> [<code>Trainer</code>] Correct behavior of <code>_load_best_model</code> for PEFT models (<a href="https://redirect.github.com/huggingface/transformers/issues/24103">#24103</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/17db177714b03103bb94cd71b7dd414bc63bffd5"><code>17db177</code></a> reset accelerate env variables after each test (<a href="https://redirect.github.com/huggingface/transformers/issues/24107">#24107</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/905892f09027cab690918c7766fea1bb51bcdd26"><code>905892f</code></a> Release: v4.30.0</li>
<li><a href="https://github.com/huggingface/transformers/commit/c3572e6bfba13ce6dc3fedb05cd1a946ea109576"><code>c3572e6</code></a> Add AzureOpenAiAgent (<a href="https://redirect.github.com/huggingface/transformers/issues/24058">#24058</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/5eb3d3c7023ed0522d3c743ee2e13d896a3aa788"><code>5eb3d3c</code></a> Up pinned accelerate version (<a href="https://redirect.github.com/huggingface/transformers/issues/24089">#24089</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/d1c039e39864a41f6eb8b770a65f123c40164ea5"><code>d1c039e</code></a> fix accelerator prepare during eval only mode (<a href="https://redirect.github.com/huggingface/transformers/issues/24014">#24014</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/2c887cf8e0cb1ac96d28361ff3235a77f83c36ee"><code>2c887cf</code></a> Do not prepare lr scheduler as it as the right number of steps (<a href="https://redirect.github.com/huggingface/transformers/issues/24088">#24088</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/12298cb65c7e9d615b749dde935a0b4966f4ae49"><code>12298cb</code></a> fix executable batch size issue (<a href="https://redirect.github.com/huggingface/transformers/issues/24067">#24067</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v4.26.0...v4.30.0">compare view</a></li>
</ul>
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https://api.github.com/repos/huggingface/transformers/issues/22608 |
TITLE
[DO NOT MERGE] Add Crop Transformation
COMMENTS
1
REACTIONS
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BODY
# What does this PR do?
Abstracts out cropping logic to be a more generic `crop` function which other, more specific cropping functions e.g. `center_crop` can call.
Motivation:
* The output of the CLIP feature extractor changed after #17628. This was due to a difference in how the `top` and `left` coordinates were calculated resulting in some values being off by one.
* The original CLIP feature extractor matched the original implementation
* Having a more generic `crop` method enables each image processor to have its own center_crop logic with minimal code replication.
[BEFORE MERGING]: Verify this doesn't have large impact on any popular CLIP dependant pipelines
Fixes #22505
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests? | [
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