Bump transformers from 4.39.3 to 4.44.0

[dependabot]

Bumps transformers from 4.39.3 to 4.44.0.

Release notes

Sourced from transformers's releases.

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)
generation_config.pad_token_id = model.config.eos_token_id
model_inputs = tokenizer(["Write a poem about the market crashing in summer"], return_tensors="pt")
model_inputs = model_inputs.to(model.device)
output_compiled = compiled_generate(**model_inputs, generation_config=generation_config)
print(output_compiled)

⚡ 3 to 5x compile speedup (compilation time 👀 not runtime)

• 3-5x faster torch.compile forward compilation for autoregressive decoder models #32227* by @​fxmarty . As documented on the PR, this makes the whole generation a lot faster when you re-use the cache! You can see this when you run model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

🪶 Offloaded KV cache: offload the cache to CPU when you are GPU poooooor 🚀

• Offloaded KV Cache #31325* by @​n17s : you just have to set cache_implementation="offloaded" when calling from_pretrained or using this:

from transformers import GenerationConfig
gen_config = GenerationConfig(cache_implementation="offloaded", # other generation options such as num_beams=4,num_beam_groups=2,num_return_sequences=4,diversity_penalty=1.0,max_new_tokens=50,early_stopping=True)
outputs = model.generate(inputs["input_ids"],generation_config=gen_config)

📦 Torch export for static cache

pytorch team gave us a great gift: you can now use torch.export directly compatible with Executorch! Find examples here.

• Make static cache compatible with torch.export #32168 by @​guangy10

... (truncated)

Commits

• 984bc11 Revert "fixes to properly shard FSDP across cpu and meta for cpu_effcient_loa...
• af61272 is_torchdynamo_compiling -- cast a wide exception net (#32476)
• 3e93524 release v4.44.0
• 80b90e7 Add codestral mamba2 (#32080)
• 3d8bd11 Generate: fix end to end compilation (#32465)
• 6a03942 Add Nemotron HF Support (#31699)
• 36fd35e Dependencies: fix typo (#32389)
• 438d06c Fix get large model config for Switch Transformer encoder only tester (#32438)
• fb66ef8 Update kwargs validation for preprocess with decorator (#32024)
• e85d863 add the missing flash attention test marker (#32419)
• Additional commits viewable in compare view

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