Bump the pip group across 1 directory with 2 updates

[dependabot]

Bumps the pip group with 2 updates in the / directory: torch and transformers.

Updates torch from 2.1.0 to 2.2.0

Release notes

Sourced from torch's releases.

PyTorch 2.2: FlashAttention-v2, AOTInductor

PyTorch 2.2 Release Notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation

Highlights

We are excited to announce the release of PyTorch® 2.2! PyTorch 2.2 offers ~2x performance improvements to scaled_dot_product_attention via FlashAttention-v2 integration, as well as AOTInductor, a new ahead-of-time compilation and deployment tool built for non-python server-side deployments.

This release also includes improved torch.compile support for Optimizers, a number of new inductor optimizations, and a new logging mechanism called TORCH_LOGS.

Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.

Along with 2.2, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog.

This release is composed of 3,628 commits and 521 contributors since PyTorch 2.1. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.2. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

Summary:

• scaled_dot_product_attention (SDPA) now supports FlashAttention-2, yielding around 2x speedups compared to previous versions.
• PyTorch 2.2 introduces a new ahead-of-time extension of TorchInductor called AOTInductor, designed to compile and deploy PyTorch programs for non-python server-side.
• torch.distributed supports a new abstraction for initializing and representing ProcessGroups called device_mesh.
• PyTorch 2.2 ships a standardized, configurable logging mechanism called TORCH_LOGS.
• A number of torch.compile improvements are included in PyTorch 2.2, including improved support for compiling Optimizers and improved TorchInductor fusion and layout optimizations.
• Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.
• torch.ao.quantization now offers a prototype torch.export based flow

... (truncated)

Commits

• 8ac9b20 Run docker release build on final tag (#117131) (#117182)
• 2490352 Fix cuInit test on Windows (#117095)
• 3a44bb7 [CI] Test that cuInit is not called during import (#117043)
• 1c8ba38 [CI] Use jemalloc for CUDA builds (#116900) (#116988)
• 96d2ddb Store user model to simplify ONNXProgram.{adapt_torch_*,call} APIs (#1152...
• 738b4a5 Update ONNX's IO Adapter to support FakeTensor with ExportedProgram (#114407)...
• 4cf10bf [Cherry-pick] [Quant] [PT2] Enable batchnorm in _move_exported_model_to_eval ...
• 7e97e4b [AARCH64] Fall back to GEMM if mkldnn_matmul fails (#115936) (#116666)
• 1a3e3c7 [CUDA] baddmm should fall back to addmm for batch=1 (#114992) (#116518)
• ab7505f Fix broken PyYAML 6.0 on MacOS x86 (#115956) (#116551)
• Additional commits viewable in compare view

Updates transformers from 4.26.0 to 4.38.0

Release notes

Sourced from transformers's releases.

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

• Flash Attention 2

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2"
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

• bitsandbytes-4bit

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", load_in_4bit=True
)
</tr></table>

... (truncated)

Commits

• 08ab54a [ gemma] Adds support for Gemma 💎 (#29167)
• 2de9314 [Maskformer] safely get backbone config (#29166)
• 476957b 🚨 Llama: update rope scaling to match static cache changes (#29143)
• 7a4bec6 Release: 4.38.0
• ee3af60 Add support for fine-tuning CLIP-like models using contrastive-image-text exa...
• 0996a10 Revert low cpu mem tie weights (#29135)
• 15cfe38 [Core tokenization] add_dummy_prefix_space option to help with latest is...
• efdd436 FIX [PEFT / Trainer ] Handle better peft + quantized compiled models (#29...
• 5e95dca [cuda kernels] only compile them when initializing (#29133)
• a7755d2 Generate: unset GenerationConfig parameters do not raise warning (#29119)
• Additional commits viewable in compare view

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