Bump transformers from 4.41.2 to 4.44.2

[dependabot]

Bumps transformers from 4.41.2 to 4.44.2.

Release notes

Sourced from transformers's releases.

Release v4.44.2

Patch release v4.44.2, mostly 2 regressions that were not caught for Jamba and for processors!

• Fix: Jamba cache fails to use torch.nn.module (#32894) Authored by @​xgal
• Fix: No need to dtype A in Jamba (#32924) @​xgal
• Fix: Regression on Processor.save_pretrained caused by #31691 (#32921) Authored by @​leloykun

Patch release v4.44.1

Here are the different fixes, mostly Gemma2 context length, nits here and there, and generation issues

• is_torchdynamo_compiling -- cast a wide exception net (#32476) by @​gante
• Revert "fixes to properly shard FSDP across cpu and meta for cpu_effcient_loading for prequantized 4bit (#32276)" (#32477) by @​gante and @​matthewdouglas
• Gemma2: fix FA2 generation (#32553) by @​zucchini-nlp
• Fix: FA2 with packed training (#32487) by @​zucchini-nlp
• Fix sliding window attention used in Gemma2FlashAttention2 (#32522) by @​brcps12
• Automatically add transformers tag to the modelcard (#32623) by @​LysandreJik
• add back the position ids (#32554) by @​ArthurZucker
• Use head_dim if in config for RoPE (#32495) @​suiyoubi @​ArthurZucker
• Revert PR 32299, flag users when Zero-3 was missed (#32851) by @​muellerzr
• fix multi-gpu with static cache (#32543) by @​SunMarc
• Reduce the error log when using core models that need their weights r… (#32656) by @​muellerzr
• Fix VLM generation issues (#32836) by @​zucchini-nlp
• Fix generate with inputs_embeds as input (#32493) (this PR has some cherry-pick)

Full Changelog: huggingface/transformers@v4.44.0...v4.44.1

Release v4.44.0: End to end compile generation!!! Gemma2 (with assisted decoding), Codestral (Mistral for code), Nemotron, Efficient SFT training, CPU Offloaded KVCache, torch export for static cache

This release comes a bit early in our cycle because we wanted to ship important and requested models along with improved performances for everyone!

All of these are included with examples in the awesome https://github.com/huggingface/local-gemma repository! 🎈 We tried to share examples of what is now possible with all the shipped features! Kudos to @​gante, @​sanchit-gandhi and @​xenova

💥 End-to-end generation compile

Generate: end-to-end compilation #30788 by @​gante: model.generate now supports compiling! There are a few limitations, but here is a small snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import copy
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B", torch_dtype=torch.bfloat16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
compile generate
compiled_generate = torch.compile(model.generate, fullgraph=True, mode="reduce-overhead")
compiled generate does NOT accept parameterization except a) model inputs b) a generation config
generation_config = copy.deepcopy(model.generation_config)
</tr></table>

... (truncated)

Commits

• 1748902 v4.44.2
• 6845144 Fix regression on Processor.save_pretrained caused by #31691 (#32921)
• 3d8cba8 fix: no need to dtype A in jamba (#32924)
• c1df7f8 fix: jamba cache fails to use torch.nn.module (#32894)
• ca56cd7 v4.44.1
• 6e931e1 Gemma2: fix FA2 generation (#32553)
• 74f57df Fix generate with inputs_embeds as input (#32493)
• 084fe2e Merge branch 'v4.44-release' of github.com:huggingface/transformers into v4.4...
• fff9be1 Reduce the error log when using core models that need their weights renamed, ...
• 4fd0f48 Fix VLM generation issues (#32836)
• Additional commits viewable in compare view

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