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https://api.github.com/repos/huggingface/transformers/issues/38326 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38326/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38326/comments | https://api.github.com/repos/huggingface/transformers/issues/38326/events | https://github.com/huggingface/transformers/issues/38326 | 3,086,751,343 | I_kwDOCUB6oc63_BZv | 38,326 | Allow `MllamaModel` to accept `pixel_values` and `inputs_embeds` | {
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] | closed | false | null | [] | null | [] | 2025-05-23T15:26:28 | 2025-05-27T16:33:57 | 2025-05-27T16:33:57 | CONTRIBUTOR | null | null | null | null | ### Feature request
`MllamaModel` does not allow users to pass `pixel_values` and `inputs_embeds` simultaneously:
https://github.com/huggingface/transformers/blob/54cd86708d2b63a1f696ee1c59384a2f04100f57/src/transformers/models/mllama/modeling_mllama.py#L1702-L1705
However, commenting out those lines and running the follow script does generate the same logits:
```python
import torch
from transformers import MllamaForConditionalGeneration, AutoProcessor
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16
)
processor = AutoProcessor.from_pretrained(model_id)
messages = [
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://llava-vl.github.io/static/images/view.jpg",
},
{"type": "text", "text": "What does the image show?"},
],
}
],
]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model(**inputs)
# Manually compute inputs_embeds
input_ids = inputs.pop("input_ids")
inputs_embeds = model.get_input_embeddings()(input_ids)
new_outputs = model(inputs_embeds=inputs_embeds, **inputs)
assert torch.allclose(outputs.logits, new_outputs.logits)
```
### Motivation
Being able to pass `inputs_embeds` along with `pixel_values` enables soft embeddings to be passed to the model in addition to images, which is useful for prompt tuning.
### Your contribution
Could contribute a PR removing the check assuming there isn't something I'm unaware of about the check. | {
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So they can trigger a CI by leaving a comment on a PR. | {
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] | closed | false | null | [] | null | [] | 2025-05-23T14:24:34 | 2025-05-26T14:29:17 | 2025-05-26T14:29:15 | NONE | null | null | null | null | ### System Info
I am currently having the latest package install.
torch 2.6.0+cu124
transformers 4.51.3
sentence-transformers 4.1.0
### 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
Error:
```python
File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
File "D:\Dataset\AgentAI\.venv\Lib\site-packages\transformers\modeling_utils.py", line 5494, in <module>
class SQuADHead(nn.Module):
...<113 lines>...
)
File "D:\Dataset\AgentAI\.venv\Lib\site-packages\transformers\modeling_utils.py", line 5513, in SQuADHead
@replace_return_docstrings(output_type=SquadHeadOutput, config_class=PretrainedConfig)
~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Dataset\AgentAI\.venv\Lib\site-packages\transformers\utils\doc.py", line 1194, in docstring_decorator
lines = func_doc.split("\n")
^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'split'
```
A simple reproduction:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
embedding = model.encode("What is the capital of France?")
print(embedding.shape)
```
### Expected behavior
This is not actually an issue, but I expect a documentation update from `transformers` maintainer to any end-users who use or develop a `transformers-` based library on the function `replace_return_docstrings` at `src/transformers/utils/doc.py` is to don't strip out the docstring by switching the option `PYTHONOPTIMIZE=2` to reduce the size of the bytecode. The use of `PYTHONOPTIMIZE=1` is OK
The reason is that the function `replace_return_docstrings` is expecting to be a decorator function without supporting the case of empty docstring. In some case, such as web hosting on Docker or production environment, or hosting an LLM without tool call where we usually strip out the docstring.
In the reproduction above (my use-case), I am just need to run the RAG search and thus don't need the docstring to be there. | {
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} | # What does this PR do?
Adds a few minor improvements:
- `<think>` and `</think>` are now printed in the chat session. More generally, any pattern matching `<word_maybe_with_underscores>` (with optional `/` at the start of the word) should now be printed. Note that tokenizer decoding in `chat` skips special tokens like EOS by default.
- By default, warnings (e.g. minor generation issues) and progress bars (e.g. loading kernels) are suppressed in the interactive session, but not when loading a model. There is a `verbose` flag to control it.
- The initial help message is clearer about what `!help` does
- negative numbers in flags are now properly handled (e.g. `!set eos_token_id=-1`)
- added a new example, `numbers2` (9.11 > 9.9?)
- bad commands starting with `!example` are now properly handled, instead of throwing an exception
_____________________________
Example of an output with a `</think>`:
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} | The comment in the test describes pretty well what the issue is:
```
# Flex attention relies on triton on compilation
# However, triton cannot handle hidden dimensions of less than 16
# --> forcing at least a hidden dim of 16
```
Flex attention tests were failing because triton needs bigger hidden dimensions to work when compiling. This made a lot of models fail. There still are some models failing but it is at least way more reliable, robust, and manageable what is failing. | {
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https://api.github.com/repos/huggingface/transformers/issues/38320 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38320/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38320/comments | https://api.github.com/repos/huggingface/transformers/issues/38320/events | https://github.com/huggingface/transformers/pull/38320 | 3,086,133,733 | PR_kwDOCUB6oc6XX_qb | 38,320 | [Falcon H1] Fix slow path forward pass | {
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} | This PR:
1. adds **MuP** **multipliers** in the fwd pass of the **slow path** `torch_forward`
2. Fixes the **repeat pattern** in **Mamba heads** , `repeat` **->** `repeat_interleave` see https://github.com/huggingface/transformers/pull/37533
@ArthurZucker
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https://api.github.com/repos/huggingface/transformers/issues/38319 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38319/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38319/comments | https://api.github.com/repos/huggingface/transformers/issues/38319/events | https://github.com/huggingface/transformers/pull/38319 | 3,086,111,360 | PR_kwDOCUB6oc6XX6xj | 38,319 | Fix some tests (especially compile with fullgraph=True on Python<3.11) | {
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} | # What does this PR do?
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## Before submitting
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https://api.github.com/repos/huggingface/transformers/issues/38318 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38318/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38318/comments | https://api.github.com/repos/huggingface/transformers/issues/38318/events | https://github.com/huggingface/transformers/issues/38318 | 3,086,055,611 | I_kwDOCUB6oc638Xi7 | 38,318 | Potential mix-up with IMAGENET_STANDARD and IMAGENET_DEFAULT values | {
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In the constants [file](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/constants.py) used for image pre-processing for CLIP, SigLIP, SigLip 2 etc. there are 2 default/standard values defined:
```
IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5]
IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5]
OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
```
I think there is a mix-up caused by this. That sometimes the one or the other value is used. In my experience using the STANDARD variants led to way worse results, when using SigLIP and SigLIP2 (they are also the ones currently used in code). When I changed to DEFAULT, the results were way better. This might just be for my use-case, but I wanted to report it anyway.
It feels like it STANDARD variants were programmed to be place-holders and somehow made it to the production, but I that is only my feeling 😄
You can see the difference for the resulting images here:
I dove deeper into the results topic in the "Image Procesing" part of the article: https://medium.com/@szym.kulpinski/clustering-football-players-using-image-embeddings-umap-and-k-means-part-2-cf0c28c87632
If there is anything unclear, I am happy to help 😄
### Who can help?
@NielsRogge
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
This is more of a theoretical consideration in general, but here is the code that you can use to print the info about the different pre-processers and re-create the images above.
https://colab.research.google.com/drive/1xdjzHh8JSna3AJmLqvHFjz4pwK6GFa9g?usp=sharing
### Expected behavior
Probably those should be removed
```
IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5]
IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5]
```
and fully replaced by those:
```
IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
```
in all the implementations. | {
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} | # What does this PR do?
I forgot to change a few more things weren't changed in previous PRs. Specifically:
- Attention refactor of Qwen and Persimmon (for Fuyu)
- The helper `get_image_features` has to return images of certain shape and format. May be breaking and we had the helper as public prev 🙈
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} | # What does this PR do?
#38232 (follow-through of the deprecation of `forced_decoder_ids`) killed TF/FLAX generation, this PR fixes it :)
failures from the daily CI are now passing, e.g.
```
CUDA_VISIBLE_DEVICES="" py.test tests/models/bart/test_modeling_tf_bart.py::TFBartModelTest::test_pipeline_text2text_generation_fp16
``` | {
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} | # What does this PR do?
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Continuation of #38311
## Before submitting
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} | # What does this PR do?
`run_slow` was renamed to `slow` | {
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- `transformers` version: 4.52.3
- Platform: Linux-6.8.0-59-generic-x86_64-with-glibc2.39
- Python version: 3.12.2
- Huggingface_hub version: 0.31.4
- Safetensors version: 0.4.3
- Accelerate version: 0.30.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: No
- Using GPU in script?: No
- GPU type: NVIDIA GeForce RTX 3090
### 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
from transformers import AutoModelForCausalLM
from transformers.utils.fx import symbolic_trace
import torch
import sys, types, torch.fx._symbolic_trace as st
from types import CodeType
from transformers import AutoTokenizer
import sys, types
import torch.fx._symbolic_trace as sym
import transformers.utils.fx as tfx
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model.eval()
prompt = """\
Context: Mount Everest attracts many climbers, including highly experienced mountaineers. There are two main climbing routes, one approaching the summit from the southeast in Nepal (known as the standard route) and the other from the north in Tibet. While not posing substantial technical climbing challenges on the standard route, Everest presents dangers such as altitude sickness, weather, and wind, as well as hazards from avalanches and the Khumbu Icefall. As of November 2022, 310 people have died on Everest. Over 200 bodies remain on the mountain and have not been removed due to the dangerous conditions. The first recorded efforts to reach Everest's summit were made by British mountaineers. As Nepal did not allow foreigners to enter the country at the time, the British made several attempts on the north ridge route from the Tibetan side. After the first reconnaissance expedition by the British in 1921 reached 7,000 m (22,970 ft) on the North Col, the 1922 expedition pushed the north ridge route up to 8,320 m (27,300 ft), marking the first time a human had climbed above 8,000 m (26,247 ft). The 1924 expedition resulted in one of the greatest mysteries on Everest to this day: George Mallory and Andrew Irvine made a final summit attempt on 8 June but never returned, sparking debate as to whether they were the first to reach the top. Tenzing Norgay and Edmund Hillary made the first documented ascent of Everest in 1953, using the southeast ridge route. Norgay had reached 8,595 m (28,199 ft) the previous year as a member of the 1952 Swiss expedition. The Chinese mountaineering team of Wang Fuzhou, Gonpo, and Qu Yinhua made the first reported ascent of the peak from the north ridge on 25 May 1960. \
Question: How high did they climb in 1922? According to the text, the 1922 expedition reached 8,"""
input_ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device)
traced = tfx.symbolic_trace(model) <-- ValueError
print(traced.graph)
The error message is :
File "/home/sangyu/Desktop/Master/delphi-main/trace_test.py", line 28, in <module>
traced = tfx.symbolic_trace(model)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sangyu/anaconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/fx.py", line 1503, in symbolic_trace
traced_graph = tracer.trace(model, concrete_args=concrete_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sangyu/anaconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/fx.py", line 1326, in trace
self.graph = super().trace(root, concrete_args=concrete_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sangyu/anaconda3/envs/py312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 776, in trace
fn, args = self.create_args_for_root(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sangyu/anaconda3/envs/py312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 663, in create_args_for_root
root_fn = _patch_function(root_fn, len(args))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sangyu/anaconda3/envs/py312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 189, in _patch_function
new_code = CodeType(*co_args) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^
ValueError: code: co_varnames is too small
### Expected behavior
It should not return error.
Even though this is the simplest implemention using transformers.utils.fx.symbolic_trace, it returns error.
However, when I run the same code with python==3.10 and transformers==4.45.1 It runs successfully.
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] | open | false | null | [] | null | [] | 2025-05-23T03:54:42 | 2025-06-09T03:12:44 | null | NONE | null | null | null | null | ### Feature request
Integrated the LagKV for KV compression.
https://github.com/AI-Lab-China-Merchants-Bank/LagKV
### Motivation
Right now, the Cache utils is expanding rapidly. But, it still lack an efficient token eviction algorithm except SinkCache and SlidingWindowCache, which show limited performance in information retrieval tasks.
LagKV provides a compression strategy which offers follow features:
1. High accuracy performance in Needle-In-A-Haystack Passkey retrieval and other normal tasks.
2. Training free: no finetune required.
3. Warmup free: no pre warm up required, no additional parameters attached.
4. Efficient: negligible computation overhead, Flash-Attention compatible.
### Your contribution
Author of LagKV.
Implement a version of Cache Interface. | null | {
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} | # What does this PR do?
For Janus Processor Tests we are importing `CHAT_TEMPLATE` from conversion which is not ideal. Hence this PR fixes that issue.
Fixes #38270
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} | Updates a few models to use the new `GradientCheckpointingLayer`. The models changed in this PR are leveraged by IBM model architectures (granite vision / bamba) or share code, either directly or through modular. Happy to submit a follow-up with other models too if that would be helpful!
Updated models:
- jamba
- bamba
- blip
- blip2
- instructblip
- instructblipvideo
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} | Add `AutoModelForTimeSeriesPrediction` to `__all__` in `modeling_auto` so that it can be imported:
```
from transformers import AutoModelForTimeSeriesPrediction
```
This class will be the portal to all time series models in Hugging Face.
It is a missing part of #34082 and this PR aims to fix it.
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https://api.github.com/repos/huggingface/transformers/issues/38306 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38306/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38306/comments | https://api.github.com/repos/huggingface/transformers/issues/38306/events | https://github.com/huggingface/transformers/pull/38306 | 3,084,432,999 | PR_kwDOCUB6oc6XSQWh | 38,306 | Update altCLIP model card | {
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"patch_url": "https://github.com/huggingface/transformers/pull/38306.patch",
"merged_at": "2025-06-11T21:48:34"
} | # What does this PR do?
As suggested in this issue - https://github.com/huggingface/transformers/issues/36979#issuecomment-2902300855 - this PR rewrites the altCLIP model card so it matches the standardized template that @stevhliu introduced in #36979.
Key points:
- Friend-style overview + sidebar badges.
- <hfoptions> usage tabs — Pipeline tab explains why it’s missing, AutoModel tab contains a runnable similarity example, transformers-cli tab shows one-liner quantisation.
- Quantisation section with dynamic INT-8 snippet (Linear-only, Embeddings left FP32) and link to the Quantization guide.
- Attention visualisation section: explains current AttentionMaskVisualizer limitation and shows a manual ViT-attention workaround; embeds a PNG heat-map (CLS attention, last layer, head
- All text follows Steve’s template verbatim where required.
Check list
- [DONE] Brief model description
- [ DONE] Ready-to-use code examples (Pipeline ✗, AutoModel ✓, transformers-cli ✓)
- [ DONE] Quantisation example for a large model (dynamic INT-8)
- [DONE ] Attention mask visualiser (note + workaround ) # I did not provide an image because it is not produced from the HF visualizer, but I can if required.
# Who can review?
Hi @stevhliu !
I updated the altCLIP model card following the standard format.
Let me know what you think of it.
Happy to revise. Thanks!
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} | Duplicate of the line above. @stevhliu
# What does this PR do?
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Fixes # (issue)
## Before submitting
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[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).
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## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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- vision models: @amyeroberts, @qubvel
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Library:
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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- tensorflow: @gante and @Rocketknight1
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- chat templates: @Rocketknight1
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HF projects:
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- 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: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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https://api.github.com/repos/huggingface/transformers/issues/38304 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38304/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38304/comments | https://api.github.com/repos/huggingface/transformers/issues/38304/events | https://github.com/huggingface/transformers/pull/38304 | 3,083,908,492 | PR_kwDOCUB6oc6XQeHO | 38,304 | [custom_generate] don't forward `custom_generate` and `trust_remote_code` | {
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} | # What does this PR do?
When calling a `custom_generate` method, don't forwards the following `generate` arguments: `custom_generate` and `trust_remote_code`.
This is done to enable naive pre/post processing extensions to the original `generate`, i.e. a custom generate doing:
```py
def generate(model, **kwargs):
# some preprocessing
return model.generate(**kwargs)
```
(previously, this resulted in an infinite loop) | {
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} | # What does this PR do?
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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.
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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#create-a-pull-request),
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[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
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- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
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: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
As suggested in this issue - https://github.com/huggingface/transformers/issues/36979#issue-2947704577 - this PR updates the documentation of the [ViTMAE](https://huggingface.co/docs/transformers/v4.52.1/en/model_doc/vit_mae) model, which will now be aligned with the standardized format for all the docs.
## Check list
- [x] Include a brief description of the model
- [x] Ready to use code examples (`Pipeline` **not** available, `AutoModel` available, and `transformers-cli` **not** available)
- [ ] For large models, provide a quantization example
- [ ] Include an attention mask visualizer
## Who can review?
Hello @stevhliu! 👋
I updated the ViTMAE model card following the standard format. Since `ViTMAEForPreTraining` isn’t compatible with `Pipeline`, because it is designed for masked autoencoding rather than classification tasks.
I’ve tried to follow all formatting and content guidelines, but I'm not sure if I did it correctly.
Happy to revise. Thanks!
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} | Keeping track of the models that are done:
- [x] Bert
- [x] Roberta
- [x] Albert
- [x] Data2VecText
- [x] Xmod
- [x] Electra
- [x] XLM Roberta
- [x] Roberta Prelayernorm
- [x] Ernie
- [x] Camembert
- [x] Bert Generation
- [x] XLM Robert XL
- [x] RoCBert
- [x] Mobile Bert
Up to discussion:
- Flash attention is flaky; I suspect the norm layers to be responsible (encountered similar things with gemma3)
Would need another round; questionable if worth it (ordered by prio):
- [ ] Tapas
- [ ] Vision x Text models, e.g.
- [ ] Bridgetower (text would be good to go, would need image counterpart)
- [ ] Altclip
- [ ] ...
- [ ] RemBert
- [ ] Megatron Bert
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Question: Are there plans from Google or Huggingface to implement Gemma 3n in other frameworks?
I've seen the LiteRT weights and Android App Link on Huggingface, and was wandering if it would be possible to convert the model architecture in the *.task file to a transformer pytorch Module?
Personally I'll really interested in the Per-Layer Embeddings and MatFormer implementation they used, but do not have any experience with Tensorflow Lite
### Open source status
- [ ] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
https://huggingface.co/google/gemma-3n-E4B-it-litert-preview | {
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} | # What does this PR do?
As per the title!
It aligns HybridChunkedCache on what was done in https://github.com/huggingface/transformers/pull/37972 (i.e. simply take the min between max_len and window), which was causing lengths mismatches with llama4 in `get_mask_sizes` with the default HybridChunkedCache!
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} | # What does this PR do?
Create and upload new test failure reports for all CI jobs | {
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} | # What does this PR do?
Fixes https://github.com/huggingface/transformers/issues/38121 and updates the weights on the hub. Note, hub PR is not yet merged | {
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} | # What does this PR do?
Fix issue where kwargs given to `from_pretrained` for processors would not be taken into account in the processor's `__init__` directly, but would override the processor's attributes after the fact, resulting in unexpected behaviors.
This might be slightly breaking, as before the kwargs, and even the processor_dict items would be used even if not valid, but I guess this shouldn't be the case :)
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} | # What does this PR do?
As per the title. It got fixed in https://github.com/huggingface/transformers/pull/38080 in the middle of https://github.com/huggingface/transformers/pull/37866/. This PR corrects the additional mask. See images for correctness for both sliding and full cases.
Before https://github.com/huggingface/transformers/pull/37866 however, the masks were first created with full attention, then were truncated with sliding window in the DecoderLayer, so I'm pretty sure the added positions to the mask were removed in the sliding layers (i.e. even with https://github.com/huggingface/transformers/pull/38080 it was not correct)-> kind of similar issue as with the Qwens. This not the case anymore, and everything is correctly generated and passed along 🤗
Of course both squares corresponds to 2 images of 5 image tokens.
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} | # What does this PR do?
This PR fixes an issue when the torch distributed is already initialized. current_device is referenced before assignment.
Getting the following traceback in accelerate when testing tp training.
```
[rank1]: tp_plan, device_map, device_mesh = initialize_tensor_parallelism(tp_plan, tp_size=None)
[rank1]: File "/home/marc/transformers/src/transformers/integrations/tensor_parallel.py", line 79, in initialize_tensor_parallelism
[rank1]: index = current_device.current_device() if device_type != "cpu" else None
[rank1]: UnboundLocalError: local variable 'current_device' referenced before assignment
```
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4.52.2
```
inference.sh: failed to run task: Unexpected type in sourceless builder transformers.models.gemma3.configuration_gemma3.Gemma3TextConfig
from user code:
File "/app/venv/3.12/lib/python3.12/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 1345, in forward
outputs = self.model(
File "/app/venv/3.12/lib/python3.12/site-packages/transformers/utils/generic.py", line 969, in wrapper
output = func(self, *args, **kwargs)
File "/app/venv/3.12/lib/python3.12/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 1205, in forward
causal_mask = self._update_causal_mask(
File "/app/venv/3.12/lib/python3.12/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 1024, in _update_causal_mask
if self.config.text_config._attn_implementation == "flash_attention_2":
File "/app/venv/3.12/lib/python3.12/site-packages/transformers/configuration_utils.py", line 211, in __getattribute__
return super().__getattribute__(key)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
```
Following the AutoModel implementation from https://huggingface.co/docs/transformers/en/model_doc/gemma3?usage=AutoModel
Passing `attn_implementation` has no effect.
### Who can help?
@ArthurZucker, @amyeroberts , @qubvel
### 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
https://huggingface.co/docs/transformers/en/model_doc/gemma3?usage=AutoModel
### Expected behavior
Model doesn't give out an error | {
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} | # What does this PR do?
As discussed offline | {
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>>> import transformers
>>> print(transformers.__version__)
4.52.2
### 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
>>> import transformers
>>> print(transformers.__version__)
4.52.2
>>> from transformers import Qwen3ForCausalLM
raise ModuleNotFoundError(
ModuleNotFoundError: Could not import module 'Qwen3ForCausalLM'. Are this object's requirements defined correctly?
### Expected behavior
>>> import transformers
>>> print(transformers.__version__)
4.52.2
>>> from transformers import Qwen3ForCausalLM
raise ModuleNotFoundError(
ModuleNotFoundError: Could not import module 'Qwen3ForCausalLM'. Are this object's requirements defined correctly? | {
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} | Scaling has been applied twice to OPT. This fixes it by passing `1.0` explicitly to avoid sdpa and co. to create a default scaling.
Fixes #38277 | {
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I am encountering a persistent issue when trying to use the Inference API for the 'gpt2' model, consistently receiving an HTTP 401 Unauthorized error with the message: {"error": "Invalid credentials in Authorization header"}.
I have been trying to set up an API connection from my Bubble application, but I'm replicating the exact same error when testing directly with Postman.
Here are the details of my setup and the troubleshooting steps I have already taken:
1. API Endpoint: https://api-inference.huggingface.co/models/gpt2 (confirmed correct).
2. HTTP Method: POST.
3. Headers:
o Authorization: Bearer YOUR_HF_TOKEN (I have verified the exact format, including the single space between Bearer and the token, multiple times to ensure no extra spaces or hidden characters).
o Content-Type: application/json (confirmed set correctly).
4. Request Body Example (for testing):
JSON
{
"inputs": "Hello, my name is"
}
5. Token Status:
o I have regenerated my API token 4 times (from https://huggingface.co/settings/tokens), trying different roles (read, write, custom), but the error persists with all of them.
6. Observed Behavior:
o When sending OPTIONS requests to the endpoint via Postman, I receive a 200 OK response, indicating the server is reachable and CORS is configured.
o However, all POST requests consistently return the 401 Unauthorized error with the specific message {"error": "Invalid credentials in Authorization header"}.
o The exact same error occurs when attempting the connection from my Bubble application.
o I have also attempted to use a different public model (e.g., distilbert-base-uncased) with the same token and received the same 401 error, suggesting the issue is not specific to the gpt2 model.
Given the extensive troubleshooting on my end across different platforms, it appears the issue might be related to the token's validation on Hugging Face's server side for my account, or a specific configuration I am missing.
Could you please investigate why my valid API token is being rejected with "Invalid credentials in Authorization header" for the Inference API?
Thank you for your time and assistance
@SunMarc
### 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
o When sending OPTIONS requests to the endpoint via Postman, I receive a 200 OK response, indicating the server is reachable and CORS is configured.
o However, all POST requests consistently return the 401 Unauthorized error with the specific message {"error": "Invalid credentials in Authorization header"}.
o The exact same error occurs when attempting the connection from my Bubble application.
o I have also attempted to use a different public model (e.g., distilbert-base-uncased) with the same token and received the same 401 error, suggesting the issue is not specific to the gpt2 model.
### Expected behavior
Here's the updated "Expected behavior" description, including the mention of Bubble:
Expected behavior:
When attempting to integrate with a Bubble application, the API call should be successfully authenticated using the provided Hugging Face API token. Subsequently, the gpt2 model should process the input, and the API should return a successful HTTP 200 OK response containing the generated text output. | {
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} | # What does this PR do?
this is aligned with our roadmap to reduce the bloating, this allows us to remove codepaths for warning:
<img width="1116" alt="image" src="https://github.com/user-attachments/assets/cd1d9480-667a-48bd-941c-f57f54442799" />
Instead of having it in all of the forwards | {
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} | # Resolve DTensor Import Path Inconsistency Across PyTorch Versions
Summary:
This pull request addresses an import issue for DTensor arising from its path change across different PyTorch versions. It implements a conditional import mechanism to ensure compatibility.
Problem:
The location of the DTensor module has been refactored within the PyTorch library:
In PyTorch versions prior to 2.5, DTensor is accessible via torch.distributed._tensor.DTensor.
In PyTorch versions 2.5 and newer, DTensor has been moved to torch.distributed.tensor.DTensor.
This discrepancy can cause ImportError exceptions when code expecting one path is run with a different PyTorch version.
Solution:
Only when pytorch version is lagger or equal to 2.5, we will use (import) DTensor.
Evidence of PyTorch Internal Change:
The change in DTensor's import path is evident in PyTorch's own codebase. The following links point to the relevant lines in torch/_dynamo/variables/torch.py for different versions, illustrating the path modification:
PyTorch 2.4.x (and earlier relevant versions):
DTensor is referenced from torch.distributed._tensor.
See: [[GitHub - pytorch/pytorch (branch dcfa77... for v2.4)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)
PyTorch 2.5.x (and later relevant versions):
DTensor is referenced from torch.distributed.tensor.
See: [[GitHub - pytorch/pytorch (branch cfc227... for v2.5)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631) | {
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} | # What does this PR do?
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<!-- Remove if not applicable -->
Fixes https://github.com/deepspeedai/DeepSpeed/issues/7298
cc @SunMarc @zach-huggingface
## 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#create-a-pull-request),
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Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Library:
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Maintained examples (not research project or legacy):
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https://api.github.com/repos/huggingface/transformers/issues/38285 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38285/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38285/comments | https://api.github.com/repos/huggingface/transformers/issues/38285/events | https://github.com/huggingface/transformers/pull/38285 | 3,082,464,902 | PR_kwDOCUB6oc6XLi3U | 38,285 | Utility script for generating CI reports locally | {
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} | # What does this PR do?
This PR adds a small util that lets us perform CI runs locally with report generation as it happens during the prod run. This is used for debugging purposes and for debugging new hardware.
Tied to @ivarflakstad's PR: https://github.com/huggingface/transformers/pull/37976 .
## 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#create-a-pull-request),
Pull Request section?
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to it if that's the case.
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[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
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
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- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
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- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
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- TensorFlow: @Rocketknight1
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https://api.github.com/repos/huggingface/transformers/issues/38283 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38283/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38283/comments | https://api.github.com/repos/huggingface/transformers/issues/38283/events | https://github.com/huggingface/transformers/issues/38283 | 3,082,161,933 | I_kwDOCUB6oc63tg8N | 38,283 | TypeError in Llama-4-Maverick-17B-128E-Instruct-FP8 Resolved with Workaround | {
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Loading `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8` with `transformers==4.53.0.dev0` or `4.51.0` initially failed with `TypeError: argument of type 'NoneType' is not iterable` in `transformers.modeling_utils.post_init`. This occurred with `Llama4ForConditionalGeneration` and `AutoModelForCausalLM` on a 4x NVIDIA RTX A6000 setup (~196GB VRAM, CUDA 12.4, Python 3.12.3, Ubuntu 24.04.2). A similar `TypeError` was seen previously with 2x GPUs. The issue was resolved using `transformers==4.51.0` and `config={"parallel_style": "none"}`.
### Steps to Reproduce
1. Install dependencies:
```bash
pip install torch==2.4.1 torchvision==0.19.1 accelerate==1.7.0 compressed-tensors==0.9.4 transformers==4.51.0
2. Confirm model files (~389GB, 84 .safetensors) at /mnt/data/ai_super_palace/models/llama4/.
3. Run (failed):
from transformers import Llama4ForConditionalGeneration
model = Llama4ForConditionalGeneration.from_pretrained(
'/mnt/data/ai_super_palace/models/llama4',
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True
)
print('Model OK')
4. Error:
TypeError: argument of type 'NoneType' is not iterable
5: Workaround (succeeded):
import os
import torch
os.environ["TORCHVISION_DISABLE_NMS"] = "1"
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
'/mnt/data/ai_super_palace/models/llama4',
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True,
offload_folder="/mnt/data/ai_super_palace/models/llama4/offload",
config={"parallel_style": "none"}
)
print('Model OK')
**Environment**
Transformers: 4.51.0 (or 4.53.0.dev0, commit b369a654)
Python: 3.12.3
PyTorch: 2.4.1
CUDA: 12.4
Accelerate: 1.7.0
Compressed-tensors: 0.9.4
OS: Ubuntu 24.04.2 LTS
Hardware: 4x NVIDIA RTX A6000 (~196GB VRAM)
Model: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
**Additional Details**
Model card requires transformers>=4.51.0, supports FP8 via compressed-tensors.
Previous errors: NameError: init_empty_weights, ValueError: requires accelerate, ImportError: requires compressed-tensors.
Config check (cat config.json | grep -E "parallel|tp_plan") empty.
Warnings: Uninitialized weights (feed_forward.experts.*) and offloaded parameters (expected due to VRAM).
**Request**
Is the TypeError a known llama4 bug? Should parallel_style be explicitly set in config.json?
Guidance on fine-tuning for sentiment analysis (~100–150GB/day, ~85–90% accuracy).
Sharing workaround for community benefit.
**Logs**
See traceback above. config.json (40KB) available if needed.
Thank you! | {
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} | @ydshieh , pls help review, thx very much. | {
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https://api.github.com/repos/huggingface/transformers/issues/38281 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38281/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38281/comments | https://api.github.com/repos/huggingface/transformers/issues/38281/events | https://github.com/huggingface/transformers/issues/38281 | 3,082,127,479 | I_kwDOCUB6oc63tYh3 | 38,281 | KeyError in Llama-4-Maverick-17B-128E-Instruct-FP8 Inference with Offloading | {
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Loading `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8` succeeds with `transformers==4.51.0`, but inference fails with `KeyError: 'model.layers.37.feed_forward.experts.gate_up_proj'` during `model.generate`. This occurs on 4x NVIDIA RTX A6000 (~196GB VRAM, CUDA 12.4, Python 3.12.3, Ubuntu 24.04.2) with offloading, critical for sentiment analysis (~100–150GB/day, ~85–90% accuracy). Disabling MoE (`num_experts=0`) didn’t resolve it.
### Steps to Reproduce
1. Install dependencies:
```bash
pip install torch==2.4.1 accelerate==1.7.0 compressed-tensors==0.9.4 transformers==4.51.0
2. Confirm model files (~389GB, 84 .safetensors) at /mnt/data/ai_super_palace/models/llama4/.
3. Run:
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
os.environ["TORCHVISION_DISABLE_NMS"] = "1"
model = AutoModelForCausalLM.from_pretrained(
'/mnt/data/ai_super_palace/models/llama4',
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True,
offload_folder="/mnt/data/ai_super_palace/models/llama4/offload",
config={"parallel_style": "none"}
)
tokenizer = AutoTokenizer.from_pretrained('/mnt/data/ai_super_palace/models/llama4')
prompt = "What is the sentiment of this text: 'I love this product, it's amazing!'"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
4. Error:
KeyError: 'model.layers.37.feed_forward.experts.gate_up_proj'
**Environment**
Transformers: 4.51.0
Python: 3.12.3
PyTorch: 2.4.1
CUDA: 12.4
Accelerate: 1.7.0
Compressed-tensors: 0.9.4
OS: Ubuntu 24.04.2 LTS
Hardware: 4x NVIDIA RTX A6000 (~196GB VRAM)
Model: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
**Additional Details**
Model card requires transformers>=4.51.0, supports FP8 via compressed-tensors.
Warnings: Uninitialized MoE weights (feed_forward.experts.*), offloaded parameters (VRAM limit).
Prior errors (TypeError: NoneType not iterable) resolved with config={"parallel_style": "none"}.
Suspect bug in accelerate offloading or MoE weight initialization.
**Request**
Is this a known llama4 MoE offloading issue?
Can MoE weights be initialized or offloading fixed?
Workaround for inference without re-downloading (~389GB)?
Urgent for sentiment analysis.
**Logs**
See traceback above. config.json (40KB) available.
Thank you!
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} | The cuda or triton kernel can not support this case: dimensions of size is 0, but traditional SDPA is implemented using PyTorch's tensor operations, which have robust support for tensors with dimensions of size 0. We need to add this check and error tips for developers when using flash_attention. Related issue is in https://github.com/deepspeedai/DeepSpeed/issues/7275 | {
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When attempting to load `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8` using `transformers==4.53.0.dev0` (or `4.51.0`), I encounter a `TypeError: argument of type 'NoneType' is not iterable` in `transformers.modeling_utils.post_init`. This occurs with both `Llama4ForConditionalGeneration` and `AutoModelForCausalLM`, blocking model verification and inference. The error persists despite installing required dependencies (`accelerate`, `compressed-tensors`) and trying various configurations (e.g., `tp_plan=None`, FP16 quantization, offloading).
This is a recurring issue, as I previously encountered a `TypeError` with this model on a 2x GPU setup. Current setup uses 4x NVIDIA RTX A6000 GPUs (~196GB VRAM), CUDA 12.4, Python 3.12.3.
### Steps to Reproduce
1. Install dependencies in a Python 3.12.3 virtual environment:
```bash
pip install torch==2.4.1 torchvision==0.19.1 accelerate==1.7.0 compressed-tensors==0.9.4
pip install git+https://github.com/huggingface/transformers.git
2. Confirm model files (~389GB, 84 .safetensors) at /mnt/data/ai_super_palace/models/llama4/.
3 Run:
import os
import torch
os.environ["TORCHVISION_DISABLE_NMS"] = "1"
from transformers import Llama4ForConditionalGeneration
model = Llama4ForConditionalGeneration.from_pretrained(
'/mnt/data/ai_super_palace/models/llama4',
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True,
offload_folder="/mnt/data/ai_super_palace/models/llama4/offload",
tp_plan=None
)
print('Model OK')
4. Error:
Traceback (most recent call last):
File "verify_llama4.py", line 5, in <module>
model = Llama4ForConditionalGeneration.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".../transformers/modeling_utils.py", line 309, in _wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File ".../transformers/modeling_utils.py", line 4507, in from_pretrained
model = cls(config, *model_args, **model_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".../transformers/models/llama4/modeling_llama4.py", line 1490, in __init__
self.vision_model = Llama4VisionModel(config.vision_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".../transformers/models/llama4/modeling_llama4.py", line 1376, in __init__
self.post_init()
File ".../transformers/modeling_utils.py", line 1968, in post_init
if v not in ALL_PARALLEL_STYLES:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: argument of type 'NoneType' is not iterable
Environment
Transformers: 4.53.0.dev0 (commit b369a654) or 4.51.0
Python: 3.12.3
PyTorch: 2.4.1
CUDA: 12.4
Accelerate: 1.7.0
Compressed-tensors: 0.9.4
OS: Ubuntu 24.04.2 LTS
Hardware: 4x NVIDIA RTX A6000 (~196GB VRAM)
Model: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
Additional Details
Model card requires transformers>=4.51.0, supports FP8 via compressed-tensors.
Config check (cat config.json | grep -E "parallel|tp_plan") empty.
Previous errors resolved: NameError: init_empty_weights, ValueError: requires accelerate, ImportError: requires compressed-tensors.
Recurring TypeError from 2x GPU setup suggests llama4 architecture issue.
Suspect bug in post_init for Llama4VisionModel or Llama4TextModel.
Expected Behavior
Model loads, prints Model OK.
Actual Behavior
TypeError due to NoneType in parallel style check.
Request
Is this a known llama4 bug?
Are specific config.json settings needed?
Can parallel checks be bypassed or text-only loading isolated?
Urgent for sentiment analysis; need workaround or fix ASAP.
Logs
See traceback. config.json (40KB) available if needed.
Thank you for urgent help!
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} | [] | closed | false | null | [] | null | [] | 2025-05-22T03:43:28 | 2025-08-14T01:52:40 | 2025-05-23T15:24:52 | CONTRIBUTOR | null | null | false | {
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} | # What does this PR do?
During analyzing performance of flash attention on Ascend NPU, we find that a fixed-size 2048*2048 upper triangular matrix `attn_mask_npu` is frequently defined in func `npu_flash_attn_func` and `npu_flash_attn_varlen_func`, which will cost a lot of time.
This PR is committed for solving this problem, by defining and using global variable `ATTN_MASK_NPU`, this matrix will not be defined every time func `npu_flash_attn_func` or `npu_flash_attn_varlen_func` is called.
Thanks @CurryRice233 for proposing this idea.
Fixes # (issue)
Not related.
## 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#create-a-pull-request),
Pull Request section?
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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).
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lm-eval 0.48
A100
Ubuntu
### Reproduction
~~~bash
lm_eval --model hf --model_args pretrained="facebook/opt-125m" --tasks lambada_openai
~~~
V4.52.2
| Tasks |Version|Filter|n-shot| Metric | | Value | | Stderr |
|--------------|------:|------|-----:|----------|---|----------:|---|---------:|
|lambada_openai| 1|none | 0|acc |↑ | 0.0004|± | 0.0003|
| | |none | 0|perplexity|↓ |388418.4098|± |29176.4556|
V4.51.3
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------------|------:|------|-----:|----------|---|------:|---|-----:|
|lambada_openai| 1|none | 0|acc |↑ | 0.3788|± |0.0068|
| | |none | 0|perplexity|↓ |26.0214|± |0.9383|
### Expected behavior
similar accuracy is expected | {
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} | # What does this PR do?
Adds a new architecture called normalized Transformer (nGPT) ([Paper](https://arxiv.org/abs/2410.01131)).
Fixes #38275
## 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#create-a-pull-request),
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- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
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- [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.
@ArthurZucker
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Normalized Transformer (nGPT) is a novel architecture with representation learning on the hypersphere. In nGPT, all vectors forming the embeddings, MLP, attention matrices and hidden states are unit norm normalized. The input stream of tokens travels on the surface of a hypersphere, with each layer contributing a displacement towards the target output predictions. These displacements are defined by the MLP and attention blocks, whose vector components also reside on the same hypersphere.
Paper: https://arxiv.org/abs/2410.01131
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
Weights will be shared on HF soon. | {
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} | @ydshieh @Rocketknight1 , sorry i mis-closed https://github.com/huggingface/transformers/pull/38252, pls help review with this PR, thx very much. | {
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} | # What does this PR do?
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<!-- Remove if not applicable -->
This PR fixes a typo in the CircleCI configuration file ([config.yml](vscode-file://vscode-app/c:/Users/PC/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-sandbox/workbench/workbench.html)), replacing the incorrect env key with the correct environment key. This ensures that environment variables are properly set in the CI jobs.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
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## Who can review?
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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 @
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Models:
- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
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Documentation: @stevhliu
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} | # What does this PR do?
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Fix the shape of `ModernBertForMaskedLM`'s output `hidden_states` when both having `output_hidden_states=True` and using Flash Attention 2.
Before fix: The output hidden states are linearized to shape similar to `(batch_size * sequence_length, hidden_size)` since the input ids are unpadded before passing to `ModernBertModel`.
Expected behavior: All output `hidden_states` should have shape `(batch_size, sequence_length, hidden_size)` according to the docs.
This PR applies unpadding to the output hidden_states to make the shapes correct.
## 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#create-a-pull-request),
Pull Request section?
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[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). (This PR makes the behavior following the current documentation.)
- [ ] Did you write any new necessary tests? (No need for extra tests.)
@ArthurZucker | null | {
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- `transformers` version: 4.51.3
- Platform: Linux-5.15.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.12.8
- Huggingface_hub version: 0.31.1
- Safetensors version: 0.5.2
- Accelerate version: 1.3.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.4.0+rocm6.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: N/A
- Using GPU in script?: NA
- GPU type: AMD Instinct MI210
### Who can help?
@ArthurZucker, error seems to be introduced with #35235
### 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
You can reproduce the error with:
```python
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=torch.bfloat16)
model.eval()
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
data = tokenizer("Once upon a time, ", return_tensors='pt')
inputs = {
'input_ids': data.input_ids,
'attention_mask': torch.ones_like(data.input_ids)}
model.config.use_cache = False
with torch.no_grad():
model(**inputs) # verify forward pass works
model, guards = torch._dynamo.export(model)(**inputs)
model(**inputs) # verify forward pass works
```
and the error received looks like
```shell
Traceback (most recent call last):
File "/home/repro.py", line 17, in <module>
model, guards = torch._dynamo.export(model)(**inputs)
# ... removed for brevity...
torch._dynamo.exc.Unsupported: call_method GetAttrVariable(UserDefinedObjectVariable(AttentionInterface), _global_mapping) __getitem__ (ConstantVariable(),) {}
from user code:
# ...removed for brevity...
File "/site-packages/transformers/models/llama/modeling_llama.py", line 274, in forward
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
File "/site-packages/transformers/modeling_utils.py", line 5841, in __getitem__
return self._global_mapping[key]
```
### Expected behavior
Expected behavior is no errors during the dynamo export.
A possible fix is
```python
def forward(self, ...):
# ...ignored for brevity...
if self.attention_interface is None:
self.attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
self.attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = self.attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
```
where `self.attention_interface = None` in the init | {
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https://api.github.com/repos/huggingface/transformers/issues/38270 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38270/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38270/comments | https://api.github.com/repos/huggingface/transformers/issues/38270/events | https://github.com/huggingface/transformers/issues/38270 | 3,081,302,954 | I_kwDOCUB6oc63qPOq | 38,270 | 'transformers.models.janus.convert_janus_weights_to_hf' is missing from release | {
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] | closed | false | null | [] | null | [] | 2025-05-21T20:10:37 | 2025-05-23T07:29:17 | 2025-05-23T07:29:17 | CONTRIBUTOR | null | null | null | null | ### System Info
In transformers v.4.52.0 - v4.52.2, the file: src/transformers/models/janus/convert_janus_weights_to_hf.py from #36053 is missing and results in an `ModuleNotFoundError` when running tests/models/janus/test_processor_janus.py:
```
Traceback:
tests/models/janus/test_processor_janus.py:23: in <module>
from transformers.models.janus.convert_janus_weights_to_hf import CHAT_TEMPLATE
E ModuleNotFoundError: No module named 'transformers.models.janus.convert_janus_weights_to_hf'
```
cc: @ArthurZucker @zucchini-nlp @yaswanth19
### 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
1. Run test `tests/models/janus/test_processor_janus.py`
2. Test fails with ModuleNotFoundError.
### Expected behavior
Test passes. | {
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] | closed | false | null | [] | null | [] | 2025-05-21T19:01:11 | 2025-05-22T14:43:30 | 2025-05-22T14:28:26 | NONE | null | null | null | null | ### System Info
21 May 2025 11:50:41 | polyaxon-main | from sentence_transformers import SentenceTransformer
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/sentence_transformers/__init__.py", line 14, in <module>
21 May 2025 11:50:41 | polyaxon-main | from sentence_transformers.cross_encoder import (
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/sentence_transformers/cross_encoder/__init__.py", line 3, in <module>
21 May 2025 11:50:41 | polyaxon-main | from .CrossEncoder import CrossEncoder
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py", line 19, in <module>
21 May 2025 11:50:41 | polyaxon-main | from transformers import (
21 May 2025 11:50:41 | polyaxon-main | File "<frozen importlib._bootstrap>", line 1055, in _handle_fromlist
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2045, in __getattr__
21 May 2025 11:50:41 | polyaxon-main | module = self._get_module(self._class_to_module[name])
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2075, in _get_module
21 May 2025 11:50:41 | polyaxon-main | raise e
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2073, in _get_module
21 May 2025 11:50:41 | polyaxon-main | return importlib.import_module("." + module_name, self.__name__)
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/importlib/__init__.py", line 127, in import_module
21 May 2025 11:50:41 | polyaxon-main | return _bootstrap._gcd_import(name[level:], package, level)
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 64, in <module>
21 May 2025 11:50:41 | polyaxon-main | from .integrations.tensor_parallel import (
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/integrations/tensor_parallel.py", line 723, in <module>
21 May 2025 11:50:41 | polyaxon-main | class ParallelInterface(MutableMapping):
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/integrations/tensor_parallel.py", line 733, in ParallelInterface
21 May 2025 11:50:41 | polyaxon-main | "colwise": ColwiseParallel(),
21 May 2025 11:50:41 | polyaxon-main | File "/opt/conda/lib/python3.9/site-packages/transformers/integrations/tensor_parallel.py", line 465, in __init__
21 May 2025 11:50:41 | polyaxon-main | self.input_layouts = (input_layouts or Replicate(),)
21 May 2025 11:50:41 | polyaxon-main | NameError: name 'Replicate' is not defined
The issue gets fixed when using the last stable release.
### 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
from sentence_transformers import SentenceTransformer
### Expected behavior
No import error. | {
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] | open | false | null | [] | null | [] | 2025-05-21T18:08:59 | 2025-06-06T18:11:13 | null | NONE | null | null | null | null | ### Feature request
In the current generation code, group beam search is necessarily greedy. From a theoretical point of view, it is not very clear why that should be the case, since the diversity penalty is applied on the logits anyway, yielding a full distribution from which sampling can still be performed.
### Motivation
I think there is a reasonable use case for such a feature: diversity beam search is very useful in particular for modalities like biological sequences which increasingly use the transformers library, but I could see it be useful as well for natural language or code, to generate diverse paths without falling to the drawbacks of greedy generation. From a more abstract point of view it is also seemingly unjustified to allow sampling for standard beam search and not for diversity beam search.
### Your contribution
I am aware of the work in #30810 so don't want to disrupt but would be happy to look into it. | null | {
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] | open | false | null | [] | null | [] | 2025-05-21T17:02:43 | 2025-10-15T16:05:46 | null | CONTRIBUTOR | null | null | null | null | ### Model description
### Model description
ByteDance recently released a multimodal understanding and generation model. While the code and weights have been made publicly available, the code requires significant formatting and cleaning to align with the standards of the Hugging Face Transformers library.
**BAGEL**
BAGEL, an open‑source multimodal foundation model with 7B active parameters (14B total) trained on large‑scale interleaved multimodal data. BAGEL outperforms the current top‑tier open‑source VLMs like Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding leaderboards, and delivers text‑to‑image quality that is competitive with strong specialist generators such as SD3. Moreover, BAGEL demonstrates superior qualitative results in classical image‑editing scenarios than the leading open-source models. More importantly, it extends to free-form visual manipulation, multiview synthesis, and world navigation, capabilities that constitute "world-modeling" tasks beyond the scope of previous image-editing models. The figure below showcases BAGEL's qualitative performance.
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
Research Paper: https://arxiv.org/pdf/2505.14683
Authors: Chaorui Deng, Deyao Zhu, Kunchang Li, et al.
Implementation:
- [ByteDance-Seed/BAGLE](https://github.com/bytedance-seed/BAGEL)
Model Weights:
- [ByteDance-Seed/BAGEL](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT) | null | {
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} | # What does this PR do?
Cleanup PR: moves a whisper processor test from the ASR pipeline test file to the whisper processor test file. In the process, it cleans up unused code from the library.
Some context: this was originally an ASR test because it used logic that was present in the ASR pipeline (`_find_timestamp_sequence`). However, in [this PR](https://github.com/huggingface/transformers/pull/21427), `_find_timestamp_sequence` stops being used by the pipeline. | {
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https://api.github.com/repos/huggingface/transformers/issues/38264 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38264/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38264/comments | https://api.github.com/repos/huggingface/transformers/issues/38264/events | https://github.com/huggingface/transformers/pull/38264 | 3,080,660,225 | PR_kwDOCUB6oc6XFchW | 38,264 | Fix convert_internvl_weights_to_hf.py to support local paths | {
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} | # What does this PR do?
This PR includes one improvements to the convert_internvl_weights_to_hf.py script for converting InternVL models:
- Allows loading model weights directly from a local path, enabling conversion without requiring remote downloads.
This change has been tested and verified on the InternVL2_5-1B, InternVL3-1B model.
Fixes #38200
## 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#create-a-pull-request),
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? | {
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] | closed | false | null | [] | null | [] | 2025-05-21T15:30:32 | 2025-05-23T17:39:27 | 2025-05-23T17:39:26 | NONE | null | null | null | null | ### System Info
- `transformers` version: 4.52.1 (the problem occurs on `4.52.2`, but when i ran `transformers env,` I get the same error!)
- Platform: Linux-6.8.0-1026-gcp-x86_64-with-glibc2.35
- Python version: 3.11.0rc1
- Huggingface_hub version: 0.31.4
- Safetensors version: 0.5.3
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.4.0+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: yes (lightning)
- Using GPU in script?: yes
- GPU type: NVIDIA L4
### Who can help?
@ArthurZucker
### 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
Using torch 2.4 with transformers 4.52.2, when loading a RoBerta model, the following exception occurs.
actually, just running `transformers env` in the shell, also produces the same error!
```
from transformers import BertModel, RobertaModel, BartModel
<frozen importlib._bootstrap>:1231: in _handle_fromlist
???
../venv/lib/python3.11/site-packages/transformers/utils/import_utils.py:2045: in __getattr__
module = self._get_module(self._class_to_module[name])
../venv/lib/python3.11/site-packages/transformers/utils/import_utils.py:2075: in _get_module
raise e
../venv/lib/python3.11/site-packages/transformers/utils/import_utils.py:2073: in _get_module
return importlib.import_module("." + module_name, self.__name__)
/usr/lib/python3.11/importlib/__init__.py:126: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
../venv/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py:44: in <module>
from ...modeling_utils import PreTrainedModel
../venv/lib/python3.11/site-packages/transformers/modeling_utils.py:64: in <module>
from .integrations.tensor_parallel import (
../venv/lib/python3.11/site-packages/transformers/integrations/tensor_parallel.py:723: in <module>
class ParallelInterface(MutableMapping):
../venv/lib/python3.11/site-packages/transformers/integrations/tensor_parallel.py:733: in ParallelInterface
"colwise": ColwiseParallel(),
../venv/lib/python3.11/site-packages/transformers/integrations/tensor_parallel.py:465: in __init__
self.input_layouts = (input_layouts or Replicate(),)
E NameError: name 'Replicate' is not defined
```
This worked until version 4.52.1
Interestingly, `Replicate` (which is only imported for `torch >= 2.5`) is already used in `__init__` in 4.52.1, so I assume `transformers.integrations.tensor_parallel.ColwiseParallel` shouldn't be used with `torch <2.5` but not sure where the root cause for that could be.
### Expected behavior
No exception should be thrown when loading the model. | {
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} | # What does this PR do?
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} | # What does this PR do?
This PR updates AMD CI image to use the latest PyTorch base image. This fix is required for the MI300 tests as PyTorch falls back to CPU on older versions.
## 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#create-a-pull-request),
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?
Transformers CI folks.
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} | # What does this PR do?
Remove unnecessary decorators and docstring in modular falcon h1, use can_return_tuple decorators | {
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} | # What does this PR do?
Fixes the shape issues reported in #36093, which have been around since [the code was added](https://github.com/huggingface/transformers/pull/33665) 👀 . It doesn't fix the quality of word timestamp outputs (see e.g. #36632), but rather how we gather the cross attentions from the right beams with beam search, which was broken.
`test_tiny_token_timestamp_batch_generation` is a test that has the same pattern, beam search + timestamps, and is failing on `main` with the same exception as reported in #36093. This PR does NOT fix that test, but allows the test to move past the shape exception until the output quality checks, which are broken 🙃 | {
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] | null | [] | 2025-05-21T10:09:31 | 2025-08-22T11:31:26 | 2025-08-22T11:31:26 | CONTRIBUTOR | null | null | null | null | ### System Info
- `transformers` version: 4.52.0.dev0
- Platform: Linux-5.10.134-008.12.kangaroo.al8.x86_64-x86_64-with-glibc2.35
- Python version: 3.10.17
- Huggingface_hub version: 0.31.1
- Safetensors version: 0.5.3
- Accelerate version: 1.6.0
- Accelerate config: not found
- DeepSpeed version: 0.16.7
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA A100-SXM4-80GB
### Who can help?
@zucchini-nlp
### 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
For model which has composite config like `qwen2_5_vl`, `llava_onevision`
https://github.com/huggingface/transformers/blob/101b3fa4ea1cfd66e72a73a0f505108454c24cce/src/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py#L293-L294
The following code would return `kwargs={'use_cache': True}`
```python
config, kwargs = AutoConfig.from_pretrained("Qwen/Qwen2.5-Omni-7B", return_unused_kwargs=True, use_cache=True)
```
So, if we use `AutoModel` or `AutoModelForCausalLM`, etc to load these models, we would get
```bash
TypeError: Qwen2_5OmniThinkerForConditionalGeneration.__init__() got an unexpected keyword argument 'use_cache'
```
This is due to the following code in
https://github.com/huggingface/transformers/blob/101b3fa4ea1cfd66e72a73a0f505108454c24cce/src/transformers/models/auto/auto_factory.py#L521-L538
https://github.com/huggingface/transformers/blob/101b3fa4ea1cfd66e72a73a0f505108454c24cce/src/transformers/models/auto/auto_factory.py#L568-L573
the kwargs are passed to `PretrainedModel.from_pretrained()`, and then passed to `__init__()`, which cause the error.
### Expected behavior
pass `kwargs` to `text_config` if the model has composite config | {
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} | The errors are often hidden deep in the stack trace otherwsie
cc @Cyrilvallez @pcuenca @ArthurZucker | {
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This PR was reverted because it was failing... 711d78d104366c16dd99ef11084c27848f7a4133 | {
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] | open | false | null | [] | null | [] | 2025-05-21T06:51:03 | 2025-06-16T16:58:17 | null | CONTRIBUTOR | null | null | null | null | ### Feature request
Will transformers support dynamic quantization config for bitsandbytes? Currently transformers support hqq dynamic quantization, via
```python
q4_config = {"nbits": 4, "group_size": 64}
q8_config = {"nbits": 8, "group_size": 64}
quant_config = HqqConfig(
dynamic_config={
"self_attn.q_proj": q4_config,
"self_attn.k_proj": q4_config,
"self_attn.v_proj": q4_config,
"self_attn.o_proj": q4_config,
"mlp.gate_proj": q8_config,
"mlp.up_proj": q4_config,
"mlp.down_proj": q4_config,
}
)
```
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} | @ydshieh , pls help review, thx.
And one more thing is, I can see there is a [print_env.py](https://github.com/huggingface/transformers/blob/711d78d104366c16dd99ef11084c27848f7a4133/utils/print_env.py#L35) in `utils`, I am curious in which situation I should use `transformers env` and in which i should use `print_env.py`? Pls let me know your insights, thx very much. | {
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`DTensor` import was changed during Pytorch 2.5,
before 2.5, it uses `from torch.distributed._tensor import DTensor`.
And this broke the code, please check
[src/pytorch_utils.py](https://github.com/huggingface/transformers/blame/711d78d104366c16dd99ef11084c27848f7a4133/src/transformers/pytorch_utils.py#L300C14-L300C19)
also check
PyTorch 2.4.x (and earlier relevant versions):
DTensor is referenced from torch.distributed._tensor.
See: [[GitHub - pytorch/pytorch (branch dcfa77... for v2.4)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)
PyTorch 2.5.x (and later relevant versions):
DTensor is referenced from torch.distributed.tensor.
See: [[GitHub - pytorch/pytorch (branch cfc227... for v2.5)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631)
### Who can help?
@S1ro1
### Information
- [ x] The official example scripts
- [ x] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
Install pytorch 2.4, then run
>>> from torch.distributed.tensor import DTensor
will get
cannot import name 'DTensor' from 'torch.distributed.tensor'
This effect case when use `transformers` to save weights.
### Expected behavior
Correct imported | {
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} | ## Resolve DTensor Import Path Inconsistency Across PyTorch Versions
**Summary:**
This pull request addresses an import issue for `DTensor` arising from its path change across different PyTorch versions. It implements a conditional import mechanism to ensure compatibility.
**Problem:**
The location of the `DTensor` module has been refactored within the PyTorch library:
* In PyTorch versions **prior to 2.5**, `DTensor` is accessible via `torch.distributed._tensor.DTensor`.
* In PyTorch versions **2.5 and newer**, `DTensor` has been moved to `torch.distributed.tensor.DTensor`.
This discrepancy can cause `ImportError` exceptions when code expecting one path is run with a different PyTorch version.
**Solution:**
To maintain compatibility across PyTorch versions, this PR introduces a version-aware import for `DTensor`:
* If the installed PyTorch version is less than 2.5, `DTensor` will be imported from `torch.distributed._tensor`.
* If the installed PyTorch version is 2.5 or greater, `DTensor` will be imported from `torch.distributed.tensor`.
This approach ensures that the correct import path is used dynamically, based on the runtime environment.
**Evidence of PyTorch Internal Change:**
The change in `DTensor`'s import path is evident in PyTorch's own codebase. The following links point to the relevant lines in `torch/_dynamo/variables/torch.py` for different versions, illustrating the path modification:
* **PyTorch 2.4.x (and earlier relevant versions):**
`DTensor` is referenced from `torch.distributed._tensor`.
See: [[GitHub - pytorch/pytorch (branch dcfa77... for v2.4)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)](https://github.com/pytorch/pytorch/blob/dcfa7702c3ecd8754e8a66bc49142de00c8474ee/torch/_dynamo/variables/torch.py#L563)
* **PyTorch 2.5.x (and later relevant versions):**
`DTensor` is referenced from `torch.distributed.tensor`.
See: [[GitHub - pytorch/pytorch (branch cfc227... for v2.5)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631)](https://github.com/pytorch/pytorch/blob/cfc227ad43c8a39e52463ef06029dddc00919eae/torch/_dynamo/variables/torch.py#L631) | {
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} | # What does this PR do?
This PR aims at integrating X-Codec model to `transformers`.
The X-Codec model is a neural audio codec that integrates semantic information from self-supervised models (e.g., HuBERT) alongside traditional acoustic information. This enables :
- **Music continuation** : Better modeling of musical semantics yields more coherent continuations.
- **Text-to-Sound Synthesis** : X-Codec captures semantic alignment between text prompts and generated audio.
- **Semantic aware audio tokenization**: X-Codec is used as an audio tokenizer in the YuE lyrics to song generation model.
X-codec first encodes the audio using an acoustic model (DAC model) and then extracts semantic information using a pretrained Hubert model, this semantic information is further refined with a semantic encoder. The combined features are then fed into a Residual Vector Quantizer (RVQ) that converts the features into discrete codes.
Each individual component reproduce the original X-Codec outputs exactly. Note that I removed the extra padding at the end of the decoded `audio_values` so that the output length matches that of the input, the produced audio closely resembles the original audio with a 1e-5 tolerance; otherwise the forward pass is identical with the output from the original model.
X-Codec can be now used as a drop in audio tokenizer in YuE as suggested in the design discussion in https://github.com/huggingface/transformers/issues/36784.
## Who can review?
@eustlb @zucchini-nlp
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} | @ydshieh , as we discussed in https://github.com/huggingface/transformers/pull/38192, i propose to change all device callings to device agnostic `backend_xx` calling for test cases, and i go through the `tests` folder and change the rest device-binding callings to that way. Pls help review, and thx for your patience in advance.
@ydshieh @IlyasMoutawwakil , pls help review, thx. | {
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} | This PR addresses Issue #38187.
- Deleted `docs/source/ja/tasks/sequence_classification.md`
- Removed the entry from `_toctree.yml`
- Updated links in docs to point to the English sequence_classification page (with "(英語版)" in the link text)
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} | # What does this PR do?
The if condition check can be simplified, since DTensor is also a Tensor:
if isinstance(tensor, torch.Tensor) or isinstance(tensor, DTensor): =>
if isinstance(tensor, torch.Tensor):
The code for DTensor export later within this function will handle it.
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Fixes # (issue)
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} | # What does this PR do?
Fixes KeyError in 4D attention mask creation for encoder-decoder models when generating with attention mask and static cache.
This occurs because `inputs_embeds` does not get added to `model_inputs` for encoder-decoder models, like Whisper. Subsequently it tries to check if `model_inputs["inputs_embeds"]` is none and throws a KeyError.
cc @gante
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More details here:[ ](https://gist.github.com/DandBman/c76a548b1972da50ffe6bbdd93fdd613) | {
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} | # What does this PR do?
As the title says. However not sure if worth it, as there are a lot of different descriptions currently for the same args. The descriptions that are different from the one in ModelOutputArgs are left in the ModelOutput docstring, which is why the there's not that much more deletions than additions in this PR.
Wdyt @Cyrilvallez @ArthurZucker ? | {
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} | Light refactor of the tensor parallelism docs!
I think it's more intuitive and impactful to start by showing `tp_plan` so they understand how it works at a higher-level, and then they can explore the different strategies and how to set those. | {
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request the support for training support for QuantizationMethod.BITNET
### Open source status
- [ ] The model implementation is available
- [ ] The model weights are available
### Provide useful links for the implementation
_No response_ | null | {
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} | Fixes #37333
This PR corrects the initialization of the bbox_embed layers in GroundingDinoForObjectDetection when config.decoder_bbox_embed_share is set to False.
What does this PR do?
Previously, the bbox_embed list was not properly initialized with independent layers when decoder_bbox_embed_share=False. The current implementation mistakenly overwrites the list in each loop iteration, resulting in incorrect behavior.
This PR:
Ensures that a new GroundingDinoMLPPredictionHead is created per decoder layer when decoder_bbox_embed_share=False
Uses nn.ModuleList properly to store separate instances
Preserves shared head behavior when decoder_bbox_embed_share=True
Motivation
Fixing this ensures that each decoder layer has its own bounding box prediction head when specified, which is essential for the expected decoding behavior and model performance. | {
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} | # What does this PR do?
Fixing some failing gptq tests.
Fixing some bnb tests
in gptq, It seems there's a regression in the new release — the layer type used to be `tritonv2` in CI, but it's now `torch`. An issue has been opened [here](https://github.com/ModelCloud/GPTQModel/issues/1617). Will update once the issue addressed
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} | # What does this PR do?
whisper has a very slow set of tests.
This PR applies low-hanging fruit changes to get faster tests (slow tests: 7m47s -> 6m59s on my machine)
- the main dataset is now loaded once per test suite (as opposed to once per test in relevant tests)
- generation tests happen on GPU when possible | {
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} | Whisper attention refactor according to the same strategies applied in #38108
Also, several fixes on Whisper along the way, reducing the number of failed tests to 3 (disregarding skipped tests):
- `test_small_longform_timestamps_generation`
- `test_tiny_token_timestamp_batch_generation`
- `test_whisper_longform_multi_batch_hard_prev_cond`
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} | # What does this PR do?
As stated in the title | {
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} | # What does this PR do?
Fix InternVL conversion script.
Fixes #38203 | {
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} | # What does this PR do?
Fixes #37172
`forced_decoder_ids`, an old `whisper` flag, was deprecated in favor of the more explicit `task` and `language` flags (#28687). However, we need to handle it for BC: the original checkpoints have them.
This PR does a few deprecation-related changes, in an attempt to move us further away from using this flag:
- Removes as many uses of `generation_config.forced_decoder_ids` as possible, except to handle BC in whisper's `generate`.
- If the newer flags (`task`/`language`) are set, don't throw any warning, i.e. the deprecated flag is ignored if the newer flags are set. Users who loaded base whisper and applied these new flags were seeing warnings, and they shouldn't.
- Don't mutate `generation_config.forced_decoder_ids`, and ignore it in `GenerationMixin.generate`
✅ no regressions in SLOW whisper tests | {
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} | # What does this PR do?
Should fix https://github.com/huggingface/transformers/issues/38228 | {
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} | # What does this PR do?
- allow non-Nvidia daily scheduled CI runs being able to compare to Nvidia runs
- allow manual trigger by `push` or `workflow_dispatch`
- this is useful when we need to trigger a manual runs, like a run for new torch version (RC) to see if there is any regression
- (If AMD workflow want to have this, only need to change AMD caller)
- avoid the reports on the hub being overridden by new runs
- see the `runs` directory [here](https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/tree/main/2025-05-20)
Plan:
- compare reports also for non-model jobs (pipeline, quantization)
- check bad commits for non-model jobs
- incorporate the changes in #37976
Question:
- If AMD workflow runs also want to run `check_bad_commit`?
- Probably not very useful (as we have this info from Nvidia runs)
- (if AMD want to run this, we need to create a new file in `hf-workflows`)
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} | # What does this PR do?
This pr simply fixes an issue related to using `_replace_with_bitnet_linear` outside of the quantizer in tests. It checks if `quantization_config` is not None before accessing `use_rms_norm`
Action : https://github.com/huggingface/transformers/actions/runs/15127726788/job/42523132728 | {
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] | closed | false | null | [] | null | [] | 2025-05-20T12:07:49 | 2025-06-28T08:02:32 | 2025-06-28T08:02:32 | NONE | null | null | null | null | ### System Info
The new entry for `sam_hq` overwrites the existing entry for regular `sam`.
```
MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = OrderedDict(
[
("sam", "SamModel"),
]
)
MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = OrderedDict(
[
("sam_hq", "SamHQModel"),
]
)
```
See
https://github.com/huggingface/transformers/blob/d34e21e7dd392c76e7852d836b6f30ba1a2c5d62/src/transformers/models/auto/modeling_auto.py#L1521
https://github.com/huggingface/transformers/blob/d34e21e7dd392c76e7852d836b6f30ba1a2c5d62/src/transformers/models/auto/modeling_auto.py#L1527
Both entries should be merged into one
### 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
generator = pipeline("mask-generation", model="Zigeng/SlimSAM-uniform-77", device=device, points_per_batch=128)
### Expected behavior
sam should work again, not only sam_hq | {
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} | # What does this PR do?
This PR fixes a breaking change that was caused by https://github.com/huggingface/transformers/pull/38090. We removed in that PR `train_dataset` arg but TRL lib uses it. We make it optional instead now | {
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