Bump the genai-workflow group across 1 directory with 11 updates #367

dependabot · 2024-09-09T13:18:26Z

Bumps the genai-workflow group with 11 updates in the /workflows/charts/huggingface-llm directory:

Package	From	To
accelerate	`0.30.1`	`0.34.2`
datasets	`2.19.0`	`2.21.0`
einops	`0.7.0`	`0.8.0`
mkl-include	`2023.2.0`	`2024.2.1`
mkl	`2023.2.0`	`2024.2.1`
onnxruntime-extensions	`0.10.1`	`0.12.0`
onnxruntime	`1.17.3`	`1.19.2`
peft	`0.11.1`	`0.12.0`
protobuf	`4.24.4`	`5.28.0`
psutil	`5.9.5`	`6.0.0`
tokenizers	`0.19.1`	`0.20.0`

Updates accelerate from 0.30.1 to 0.34.2

Release notes

Sourced from accelerate's releases.

v0.34.1 Patchfix

Bug fixes

Fixes an issue where processed DataLoaders could no longer be pickled in #3074 thanks to @byi8220

Fixes an issue when using FSDP where default_transformers_cls_names_to_wrap would separate _no_split_modules by characters instead of keeping it as a list of layer names in #3075

Full Changelog: huggingface/accelerate@v0.34.0...v0.34.1

v0.34.0: StatefulDataLoader Support, FP8 Improvements, and PyTorch Updates!

Dependency Changes

Updated Safetensors Requirement: The library now requires safetensors version 0.4.3.

Added support for Numpy 2.0: The library now fully supports numpy 2.0.0

Core

New Script Behavior Changes

Process Group Management: PyTorch now requires users to destroy process groups after training. The accelerate library will handle this automatically with accelerator.end_training(), or you can do it manually using PartialState().destroy_process_group().

MLU Device Support: Added support for saving and loading RNG states on MLU devices by @huismiling

NPU Support: Corrected backend and distributed settings when using transfer_to_npu, ensuring better performance and compatibility.

DataLoader Enhancements

Stateful DataDataLoader: We are excited to announce that early support has been added for the StatefulDataLoader from torchdata, allowing better handling of data loading states. Enable by passing use_stateful_dataloader=True to the DataLoaderConfiguration, and when calling load_state() the DataLoader will automatically be resumed from its last step, no more having to iterate through passed batches.

Decoupled Data Loader Preparation: The prepare_data_loader() function is now independent of the Accelerator, giving you more flexibility towards which API levels you would like to use.

XLA Compatibility: Added support for skipping initial batches when using XLA.

Improved State Management: Bug fixes and enhancements for saving/loading DataLoader states, ensuring smoother training sessions.

Epoch Setting: Introduced the set_epoch function for MpDeviceLoaderWrapper.

FP8 Training Improvements

Enhanced FP8 Training: Fully Sharded Data Parallelism (FSDP) and DeepSpeed support now work seamlessly with TransformerEngine FP8 training, including better defaults for the quantized FP8 weights.

Integration baseline: We've added a new suite of examples and benchmarks to ensure that our TransformerEngine integration works exactly as intended. These scripts run one half using 🤗 Accelerate's integration, the other with raw TransformersEngine, providing users with a nice example of what we do under the hood with accelerate, and a good sanity check to make sure nothing breaks down over time. Find them here

Import Fixes: Resolved issues with import checks for the Transformers Engine that has downstream issues.

FP8 Docker Images: We've added new docker images for TransformerEngine and accelerate as well. Use docker pull huggingface/accelerate@gpu-fp8-transformerengine to quickly get an environment going.

torchpippy no more, long live torch.distributed.pipelining

With the latest PyTorch release, torchpippy is now fully integrated into torch core, and as a result we are exclusively supporting the PyTorch implementation from now on

There are breaking examples and changes that comes from this shift. Namely:

Tracing of inputs is done with a shape each GPU will see, rather than the size of the total batch. So for 2 GPUs, one should pass in an input of [1, n, n] rather than [2, n, n] as before.

We no longer support Encoder/Decoder models. PyTorch tracing for pipelining no longer supports encoder/decoder models, so the t5 example has been removed.

Computer vision model support currently does not work: There are some tracing issues regarding resnet's we are actively looking into.

If either of these changes are too breaking, we recommend pinning your accelerate version. If the encoder/decoder model support is actively blocking your inference using pippy, please open an issue and let us know. We can look towards adding in the old support for torchpippy potentially if needed.

Fully Sharded Data Parallelism (FSDP)

Environment Flexibility: Environment variables are now fully optional for FSDP, simplifying configuration. You can now fully create a FullyShardedDataParallelPlugin yourself manually with no need for environment patching:
from accelerate import FullyShardedDataParallelPlugin
fsdp_plugin = FullyShardedDataParallelPlugin(...)
FSDP RAM efficient loading: Added a utility to enable RAM-efficient model loading (by setting the proper environmental variable). This is generally needed if not using accelerate launch and need to ensure the env variables are setup properly for model loading:
from accelerate.utils import enable_fsdp_ram_efficient_loading, disable_fsdp_ram_efficient_loading
</tr></table> 

... (truncated)

Commits

c61f41c Release: v0.34.2
beb4378 Release: v0.34.1
e13bef2 Allow DataLoaderAdapter subclasses to be pickled by implementing __reduce__...
73a1531 Fix FSDP auto_wrap using characters instead of full str for layers (#3075)
159c0dd Release: v0.34.0
8931e5e Remove skip_first_batches support for StatefulDataloader and fix all the te...
a848592 Speed up tests by shaving off subprocess when not needed (#3042)
758d624 add set_epoch for MpDeviceLoaderWrapper (#3053)
b07ad2a Fix typo in comment (#3045)
1d09a20 use duck-typing to ensure underlying optimizer supports schedulefree hooks (#...
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Updates datasets from 2.19.0 to 2.21.0

Release notes

Sourced from datasets's releases.

2.21.0

Features
Support pyarrow large_list by @albertvillanova in huggingface/datasets#7019
Support Polars round trip:
import polars as pl
from datasets import Dataset
df1 = pl.from_dict({"col_1": [[1, 2], [3, 4]]}
df2 = Dataset.from_polars(df).to_polars()
assert df1.equals(df2)
What's Changed

Use HF_HUB_OFFLINE instead of HF_DATASETS_OFFLINE by @Wauplin in huggingface/datasets#6968

packaging: Remove useless dependencies by @daskol in huggingface/datasets#6971

Fix resuming arrow format by @lhoestq in huggingface/datasets#6964

Fix webdataset pickling by @lhoestq in huggingface/datasets#6972

Set temporary numpy upper version < 2.0.0 to fix CI by @albertvillanova in huggingface/datasets#6975

Fix regression for pandas < 2.0.0 in JSON loader by @albertvillanova in huggingface/datasets#6978

Ensure compatibility with numpy 2.0.0 by @KennethEnevoldsen in huggingface/datasets#6976

Remove underlines between badges by @novialriptide in huggingface/datasets#6966

Update docs on trust_remote_code defaults to False by @albertvillanova in huggingface/datasets#6981

Improve skip take shuffling and distributed by @lhoestq in huggingface/datasets#6965

Fix tests using hf-internal-testing/librispeech_asr_dummy by @albertvillanova in huggingface/datasets#6998

Fix dump of bfloat16 torch tensor by @lhoestq in huggingface/datasets#7002

minor fix for bfloat16 by @lhoestq in huggingface/datasets#7003

Fix incorrect rank value in data splitting by @yzhangcs in huggingface/datasets#6994

less script docs by @lhoestq in huggingface/datasets#6993

Fix CI by temporarily pinning ruff < 0.5.0 by @albertvillanova in huggingface/datasets#7007

Support ruff 0.5.0 in CI by @albertvillanova in huggingface/datasets#7009

Fix WebDatasets KeyError for user-defined Features when a field is missing in an example by @ProGamerGov in huggingface/datasets#7004

[Streaming] retry on requests errors by @lhoestq in huggingface/datasets#6963

Re-enable raising error from huggingface-hub FutureWarning in CI by @albertvillanova in huggingface/datasets#7011

Skip faiss tests on Windows to avoid running CI for 360 minutes by @albertvillanova in huggingface/datasets#7014

Support fsspec 2024.6.1 by @albertvillanova in huggingface/datasets#7017

Persist IterableDataset epoch in workers by @lhoestq in huggingface/datasets#6710

Fix casting list array to fixed size list by @albertvillanova in huggingface/datasets#7021

Remove dead code for pyarrow < 15.0.0 by @albertvillanova in huggingface/datasets#7023

Fix check_library_imports by @lhoestq in huggingface/datasets#7026

Missing line from previous pr by @lhoestq in huggingface/datasets#7027

Fix ci by @lhoestq in huggingface/datasets#7028

Add decorator as explicit test dependency by @albertvillanova in huggingface/datasets#7043

Mark tests that require librosa by @albertvillanova in huggingface/datasets#7044

Unblock NumPy 2.0 by @NeilGirdhar in huggingface/datasets#6991

Fix tensorflow min version depending on Python version by @albertvillanova in huggingface/datasets#7045

Support librosa and numpy 2.0 for Python 3.10 by @albertvillanova in huggingface/datasets#7046

add checkpoint and resume title in docs by @lhoestq in huggingface/datasets#7050

Update load_hub.mdx by @severo in huggingface/datasets#7057

Add batching to IterableDataset by @lappemic in huggingface/datasets#7054

... (truncated)

Commits

a1b5a32 Release: 2.21.0 (#7098)
83e5c05 Fix CI for metrics
af818af chore: fix typos in docs (#7034)
be43e09 Support pyarrow large_list (#7019)
e7094be Support HTTP authentication in non-streaming mode (#7082)
5735e6b feat: support non streamable arrow file binary format (#7025)
4d5e237 Fix push_to_hub by not calling create_branch if branch exists (#7069)
87e8ab3 Set load_from_disk path type as PathLike (#7081)
360d8c1 Fix prepare_single_hop_path_and_storage_options (#7068)
779f6d7 Fix CI test_convert_to_parquet (#7078)
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Updates einops from 0.7.0 to 0.8.0

Release notes

Sourced from einops's releases.

v0.8.0: tinygrad, small fixes and updates

TLDR

tinygrad backend added

resolve warning in py3.11 related to docstring

remove graph break for unpack

breaking TF layers were updated to follow new instructions, new layers compatible with TF 2.16, and not compatible with old TF (certainly does not work with TF2.13)

What's Changed

Fix invalid escape sequence in einsum docstring by @atwam in arogozhnikov/einops#298

Tinygrad support by @blueridanus in arogozhnikov/einops#297

Coerce bool to int in unpack by @drubinstein in arogozhnikov/einops#287

Remove oneflow from testing by @arogozhnikov in arogozhnikov/einops#289

tests: fix torch installation to force CPU by @arogozhnikov in arogozhnikov/einops#288

Allow anonymous axes in parse_shape, fix #302 by @arogozhnikov in arogozhnikov/einops#303

Codebase standards + update TF layers by @arogozhnikov in arogozhnikov/einops#318

update github actions by @arogozhnikov in arogozhnikov/einops#319

New Contributors

@drubinstein made their first contribution in arogozhnikov/einops#287

@atwam made their first contribution in arogozhnikov/einops#298

@blueridanus made their first contribution in arogozhnikov/einops#297

Full Changelog: arogozhnikov/einops@v0.7.0...v0.8.0

Commits

fe9d81d bump version to 0.8.0
10e19b4 prepare readme for a new release
b67cfea Rename KerasBackend -> TFKerasBackend to avoid confusion with keras 3.
f33113e update github actions
cff63f2 Codebase standards + update TF layers (#318)
2ab5c2a switch to stable paddlepaddle in testing, because of https://github.com/Paddl...
60b87de remove announcement
5655ce1 Merge pull request #303 from arogozhnikov/dev
5b41d90 allow anonymous axes in parse_shape, fix #302
9a9b304 delete experimental data-api_packing as production version is available now
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Updates mkl-include from 2023.2.0 to 2024.2.1

Updates mkl from 2023.2.0 to 2024.2.1

Commits

See full diff in compare view

Updates onnxruntime-extensions from 0.10.1 to 0.12.0

Release notes

Sourced from onnxruntime-extensions's releases.

v0.12.0

What's Changed

Added C APIs for language, vision and audio processors including new FeatureExtractor for Whisper model

Support for Phi-3 Small Tokenizer and new OpenAI tiktoken format for fast loading of BPE tokenizers

Added new CUDA custom operators such as MulSigmoid, Transpose2DCast, ReplaceZero, AddSharedInput and MulSharedInput

Enhanced Custom Op Lite API on GPU and fused kernels for DORT

Bug fixes, including null bos_token for Qwen2 tokenizer and SentencePiece converted FastTokenizer issue on non-ASCII characters, as well as necessary updates for MSVC 19.40 and numpy 2.0 release

New Contributors

@yihonglyu made their first contribution in microsoft/onnxruntime-extensions#702

@skyline75489 made their first contribution in microsoft/onnxruntime-extensions#748

Full Changelog: microsoft/onnxruntime-extensions@v.0.11.0...v0.12.0

v0.11.0

What's changed

Created Java packaging pipeline and published to Maven repository.

Added support for conversion of Huggingface FastTokenizer into ONNX custom operator.

Unified the SentencePiece tokenizer with other Byte Pair Encoding (BPE) based tokenizers.

Fixed Whisper large model pre-processing bug.

Enabled eager execution for custom operator and refactored the header file structure.

Contributions

Contributors to ONNX Runtime Extensions include members across teams at Microsoft, along with our community members: @sayanshaw24 @wenbingl @skottmckay @natke @hariharans29 @jslhcl @snnn @kazssym @YUNQIUGUO @souptc @yihonglyu

Commits

cb47d2c Update nuget extraction path for iOS xcframework (#792)
b27fbbe Update macosx framework packaging to follow apple guidelines (#776) (#789)
c7a2d45 Update build-package-for-windows.yml (#784)
3ce1e9f Upgrade ESRP signing task from v2 to v5 (#780)
e113ed3 removed OpenAIAudioToText from config (#777)
c9c11b4 Fix the windows API missing issue and Linux shared library size issue for Jav...
c3145b8 add the decoder_prompt_id for whisper tokenizer (#775)
620050f reimplement resize cpu kernel for image processing (#768)
d79299e increase timeout (#773)
735041e increase timeout (#772)
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Updates onnxruntime from 1.17.3 to 1.19.2

Release notes

Sourced from onnxruntime's releases.

ONNX Runtime v1.19.2

Announcements

ORT 1.19.2 is a small patch release, fixing some broken workflows and introducing bug fixes.

Build System & Packages

Fixed the signing of native DLLs.

Disabled absl symbolize in Windows Release build to avoid dependency on dbghelp.dll.

Training

Restored support for CUDA compute capability 7.0 and 7.5 with CUDA 12, and 6.0 and 6.1 with CUDA 11.

Several fixes for training CI pipelines.

Mobile

Fixed ArgMaxOpBuilder::AddToModelBuilderImpl() nullptr Node access for CoreML EP.

Generative AI

Added CUDA kernel for Phi3 MoE.

Added smooth softmax support in CUDA and CPU kernels for the GroupQueryAttention operator.

Fixed number of splits calculations in GroupQueryAttention CUDA operator.

Enabled causal support in the MultiHeadAttention CUDA operator.

Contributors

@prathikr, @mszhanyi, @edgchen1, @tianleiwu, @wangyems, @aciddelgado, @mindest, @snnn, @baijumeswani, @MaanavD

Thanks to everyone who helped ship this release smoothly!

Full Changelog: microsoft/onnxruntime@v1.19.0...v1.19.2

ONNX Runtime v1.19.0

Announcements

Note that the wrong commit was initially tagged with v1.19.0. The final commit has since been correctly tagged: microsoft/onnxruntime@26250ae. This shouldn't effect much, but sorry for the inconvenience!

Build System & Packages

Numpy support for 2.x has been added

Qualcomm SDK has been upgraded to 2.25

ONNX has been upgraded from 1.16 → 1.16.1

Default GPU packages use CUDA 12.x and Cudnn 9.x (previously CUDA 11.x/CuDNN 8.x) CUDA 11.x/CuDNN 8.x packages are moved to the aiinfra VS feed.

TensorRT 10.2 support added

Introduced Java CUDA 12 packages on Maven.

Discontinued support for Xamarin. (Xamarin reached EOL on May 1, 2024)

Discontinued support for macOS 11 and increasing the minimum supported macOS version to 12. (macOS 11 reached EOL in September 2023)

Discontinued support for iOS 12 and increasing the minimum supported iOS version to 13.

Core

Implemented DeformConv

Fixed big-endian and support build on AIX

Performance

Added QDQ support for INT4 quantization in CPU and CUDA Execution Providers

Implemented FlashAttention on CPU to improve performance for GenAI prompt cases

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Commits

ffceed9 ORT 1.19.2 Release: Cherry Pick Round 1 (#21861)
d651463 ORT 1.19.1 Release: Cherry Pick Round 1 (#21796)
26250ae ORT 1.19.0 Release: Cherry-Pick Round 2 (#21726)
ccf6a28 ORT 1.19.0 Release: Cherry-Pick Round 1 (#21619)
ee2fe87 ORT 1.19.0 Release: Cherry-Pick Round 0 (#21609)
530a2d7 Enable FP16 Clip and Handle Bias in FP16 Depthwise Conv (#21493)
82036b0 Remove references to the outdated CUDA EP factory method (#21549)
07d3be5 CoreML: Add ML Program Split Op (#21456)
5d78b9a [TensorRT EP] Update TRT OSS Parser to 10.2 (#21552)
8417c32 Keep QDQ nodes w/ nonpositive scale around MaxPool (#21182)
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Updates peft from 0.11.1 to 0.12.0

Release notes

Sourced from peft's releases.

v0.12.0: New methods OLoRA, X-LoRA, FourierFT, HRA, and much more

Highlights

New methods

OLoRA

@tokenizer-decode added support for a new LoRA initialization strategy called OLoRA (#1828). With this initialization option, the LoRA weights are initialized to be orthonormal, which promises to improve training convergence. Similar to PiSSA, this can also be applied to models quantized with bitsandbytes. Check out the accompanying OLoRA examples.

X-LoRA

@EricLBuehler added the X-LoRA method to PEFT (#1491). This is a mixture of experts approach that combines the strength of multiple pre-trained LoRA adapters. Documentation has yet to be added but check out the X-LoRA tests for how to use it.

FourierFT

@Phoveran, @zqgao22, @Chaos96, and @DSAILatHKUST added discrete Fourier transform fine-tuning to PEFT (#1838). This method promises to match LoRA in terms of performance while reducing the number of parameters even further. Check out the included FourierFT notebook.

HRA

@DaShenZi721 added support for Householder Reflection Adaptation (#1864). This method bridges the gap between low rank adapters like LoRA on the one hand and orthogonal fine-tuning techniques such as OFT and BOFT on the other. As such, it is interesting for both LLMs and image generation models. Check out the HRA example on how to perform DreamBooth fine-tuning.

Enhancements

IA³ now supports merging of multiple adapters via the add_weighted_adapter method thanks to @alexrs (#1701).

Call peft_model.get_layer_status() and peft_model.get_model_status() to get an overview of the layer/model status of the PEFT model. This can be especially helpful when dealing with multiple adapters or for debugging purposes. More information can be found in the docs (#1743).

DoRA now supports FSDP training, including with bitsandbytes quantization, aka QDoRA ()#1806).

VeRA has been extended by @dkopi to support targeting layers with different weight shapes (#1817).

@kallewoof added the possibility for ephemeral GPU offloading. For now, this is only implemented for loading DoRA models, which can be sped up considerably for big models at the cost of a bit of extra VRAM (#1857).

Experimental: It is now possible to tell PEFT to use your custom LoRA layers through dynamic dispatching. Use this, for instance, to add LoRA layers for thus far unsupported layer types without the need to first create a PR on PEFT (but contributions are still welcome!) (#1875).

Examples

@shirinyamani added a script and a notebook to demonstrate DoRA fine-tuning.

@rahulbshrestha contributed a notebook that shows how to fine-tune a DNA language model with LoRA.

Changes

Casting of the adapter dtype

Important: If the base model is loaded in float16 (fp16) or bfloat16 (bf16), PEFT now autocasts adapter weights to float32 (fp32) instead of using the dtype of the base model (#1706). This requires more memory than previously but stabilizes training, so it's the more sensible default. To prevent this, pass autocast_adapter_dtype=False when calling get_peft_model, PeftModel.from_pretrained, or PeftModel.load_adapter.

Adapter device placement

The logic of device placement when loading multiple adapters on the same model has been changed (#1742). Previously, PEFT would move all adapters to the device of the base model. Now, only the newly loaded/created adapter is moved to the base model's device. This allows users to have more fine-grained control over the adapter devices, e.g. allowing them to offload unused adapters to CPU more easily.

PiSSA

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Commits

e6cd24c Release v0.12.0 (#1946)
05f57e9 PiSSA, OLoRA: Delete initial adapter after conversion instead of the active a...
2ce83e0 FIX Decrease memory overhead of merging (#1944)
ebcd079 [WIP] ENH Add support for Qwen2 (#1906)
ba75bb1 FIX: More VeRA tests, fix tests, more checks (#1900)
6472061 FIX Prefix tuning Grouped-Query Attention (#1901)
e02b938 FIX PiSSA & OLoRA with rank/alpha pattern, rslora (#1930)
5268495 FEAT Add HRA: Householder Reflection Adaptation (#1864)
2aaf9ce ENH Sync LoRA tp_layer methods with vanilla LoRA (#1919)
a019f86 FIX sft script print_trainable_parameters attr lookup (#1928)
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Updates protobuf from 4.24.4 to 5.28.0

Commits

439c42c Updating version.json and repo version numbers to: 28.0
c9454f4 Remove --copt="-Werror" from .bazelrc (#18005)
f5a1b17 Move -Werror to our test/dev bazelrc files. (#17938)
0c9e14a Merge pull request #17917 from thomasvl/patch_objc_to_28
6a6ebe4 Merge pull request #17919 from protocolbuffers/28.x-202408221734
09ba2bb Updating version.json and repo version numbers to: 28.0-dev
e340f52 Updating version.json and repo version numbers to: 28.0-rc3
b276420 [ObjC] Issue stderr warnings for deprecated generation options.
13f850d Merge pull request #17913 from protocolbuffers/cp-compat-upgrade
6bf01c5 Binary compatibility shims for GeneratedMessageV3, SingleFieldBuilderV3, Repe...
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Updates psutil from 5.9.5 to 6.0.0

Changelog

Sourced from psutil's changelog.

6.0.0

2024-06-18

Enhancements

2109_: maxfile and maxpath fields were removed from the namedtuple returned by disk_partitions()_. Reason: on network filesystems (NFS) this can potentially take a very long time to complete.

2366_, [Windows]: log debug message when using slower process APIs.

2375_, [macOS]: provide arm64 wheels. (patch by Matthieu Darbois)

2396_: process_iter()_ no longer pre-emptively checks whether PIDs have been reused. This makes process_iter()_ around 20x times faster.

2396_: a new psutil.process_iter.cache_clear() API can be used the clear process_iter()_ internal cache.

2401_, Support building with free-threaded CPython 3.13. (patch by Sam Gross)

2407_: Process.connections()_ was renamed to Process.net_connections()_. The old name is still available, but it's deprecated (triggers a DeprecationWarning) and will be removed in the future.

2425_: [Linux]: provide aarch64 wheels. (patch by Matthieu Darbois / Ben Raz)

Bug fixes

2250_, [NetBSD]: Process.cmdline()_ sometimes fail with EBUSY. It usually happens for long cmdlines with lots of arguments. In this case retry getting the cmdline for up to 50 times, and return an empty list as last resort.

2254_, [Linux]: offline cpus raise NotImplementedError in cpu_freq() (patch by Shade Gladden)

2272_: Add pickle support to psutil Exceptions.

2359_, [Windows], [CRITICAL]: pid_exists()_ disagrees with Process_ on whether a pid exists when ERROR_ACCESS_DENIED.

2360_, [macOS]: can't compile on macOS < 10.13. (patch by Ryan Schmidt)

2362_, [macOS]: can't compile on macOS 10.11. (patch by Ryan Schmidt)

2365_, [macOS]: can't compile on macOS < 10.9. (patch by Ryan Schmidt)

2395_, [OpenBSD]: pid_exists()_ erroneously return True if the argument is a thread ID (TID) instead of a PID (process ID).

2412_, [macOS]: can't compile on macOS 10.4 PowerPC due to missing MNT_ constants.

Porting notes

Version 6.0.0 introduces some changes which affect backward compatibility:

2109_: the namedtuple returned by disk_partitions()_' no longer has maxfile and maxpath fields.

2396_: process_iter()_ no longer pre-emptively checks whether PIDs have been reused. If you want to check for PID reusage you are supposed to use Process.is_running()_ against the yielded Process_ instances. That will also automatically remove reused PIDs from process_iter()_ internal cache.

... (truncated)

Commits

3d5522a release
5b30ef4 Add aarch64 manylinux wheels (#2425)
1d092e7 test subprocesses: sleep() with an interval of 0.1 to make the test process m...
5f80c12 Fix #2412, [macOS]: can't compile on macOS 10.4 PowerPC due to missing MNT_...
89b6096 process_iter(): use another global var to keep track of reused PIDs
9421bf8 openbsd: skip test if cmdline() returns [] due to EBUSY
4b1a054 Fix #2250 / NetBSD / cmdline: retry on EBUSY. (#2421)
20be5ae ruff: enable and fix 'unused variable' rule
5530985 chore(ci): update actions (#2417)
1c7cb0a Don't build with limited API for 3.13 free-threaded build (#2402)
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Updates tokenizers from 0.19.1 to 0.20.0

Release notes

Sourced from tokenizers's releases.

Release v0.20.0: faster encode, better python support

Release v0.20.0

This release is focused on performances and user experience.

Performances:

First off, we did a bit of benchmarking, and found some place for improvement for us! With a few minor changes (mostly #1587) here is what we get on Llama3 running on a g6 instances on AWS https://github.com/huggingface/tokenizers/blob/main/bindings/python/benches/test_tiktoken.py :

Python API

We shipped better deserialization errors in general, and support for __str__ and __repr__ for all the object. This allows for a lot easier debugging see this:

>>> from tokenizers import Tokenizer;
>>> tokenizer = Tokenizer.from_pretrained("bert-base-uncased");
>>> print(tokenizer)
Tokenizer(version="1.0", truncation=None, padding=None, added_tokens=[{"id":0, "content":"[PAD]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":100, "content":"[UNK]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":101, "content":"[CLS]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":102, "content":"[SEP]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":103, "content":"[MASK]", "single_word":False, "lstrip":False, "rstrip":False, ...}], normalizer=BertNormalizer(clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True), pre_tokenizer=BertPreTokenizer(), post_processor=TemplateProcessing(single=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0)], pair=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0), Sequence(id=B, type_id=1), SpecialToken(id="[SEP]", type_id=1)], special_tokens={"[CLS]":SpecialToken(id="[CLS]", ids=[101], tokens=["[CLS]"]), "[SEP]":SpecialToken(id="[SEP]", ids=[102], tokens=["[SEP]"])}), decoder=WordPiece(prefix="##", cleanup=True), model=WordPiece(unk_token="[UNK]", continuing_subword_prefix="##", max_input_chars_per_word=100, vocab={"[PAD]":0, "[unused0]":1, "[unused1]":2, "[unused2]":3, "[unused3]":4, ...}))
>>> tokenizer
Tokenizer(version="1.0", truncation=None, padding=None, added_tokens=[{"id":0, "content":"[PAD]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":100, "content":"[UNK]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":101, "content":"[CLS]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":102, "content":"[SEP]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":103, "content":"[MASK]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}], normalizer=BertNormalizer(clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True), pre_tokenizer=BertPreTokenizer(), post_processor=TemplateProcessing(single=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0)], pair=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0), Sequence(id=B, type_id=1), SpecialToken(id="[SEP]", type_id=1)], special_tokens={"[CLS]":SpecialToken(id="[CLS]", ids=[101], tokens=["[CLS]"]), "[SEP]":SpecialToken(id="[SEP]", ids=[102], tokens=["[SEP]"])}), decoder=WordPiece(prefix="##", cleanup=True), model=WordPiece(unk_token="[UNK]", continuing_subword_prefix="##", max_input_chars_per_word=100, vocab={"[PAD]":0, "[unused0]":1, "[unused1]":2, ...}))

The pre_tokenizer.Sequence and normalizer.Sequence are also more accessible now:

from tokenizers import normalizers
norm = normalizers.Sequence([normalizers.Strip(), normalizers.BertNormalizer()])
norm[0]
norm[1].lowercase=False

What's Changed

remove enforcement of non special when adding tokens by @ArthurZucker in huggingface/tokenizers#1521
[BREAKING CHANGE] Ignore added_tokens (both special and not) in the decoder by @Narsil in huggingface/tokenizers#1513
Make USED_PARALLELISM atomic by @nathaniel-daniel in huggingface/tokenizers#1532
Fixing for clippy 1.78 by @Narsil in huggingface/tokenizers#1548
feat(ci): add trufflehog secrets detection by @McPatate in huggingface/tokenizers#1551
Switch from cached_download to hf_hub_download in tests by @Wauplin in huggingface/tokenizers#1547
Fix "dictionnary" typo by @nprisbrey in

github-actions · 2024-09-09T13:21:56Z

Dependency Review

The following issues were found:

✅ 0 vulnerable package(s)
✅ 0 package(s) with incompatible licenses
✅ 0 package(s) with invalid SPDX license definitions
⚠️ 3 package(s) with unknown licenses.

See the Details below.

License Issues

workflows/charts/huggingface-llm/requirements.txt

Package	Version	License	Issue Type
mkl-include	2024.2.1	Null	Unknown License
mkl	2024.2.1	Null	Unknown License
protobuf	5.28.0	Null	Unknown License

OpenSSF Scorecard

Scorecard details

Package

Version

Score

Details

pip/accelerate

0.34.2

🟢 6.4

Details

Check	Score	Reason
Code-Review	🟢 9	Found 27/30 approved changesets -- score normalized to 9
Maintained	🟢 10	30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Binary-Artifacts	🟢 10	no binaries found in the repo
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Security-Policy	⚠️ 0	security policy file not detected
Signed-Releases	⚠️ -1	no releases found
Fuzzing	⚠️ 0	project is not fuzzed
Vulnerabilities	🟢 10	0 existing vulnerabilities detected
Packaging	🟢 10	packaging workflow detected
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
SAST	🟢 6	SAST tool is not run on all commits -- score normalized to 6

pip/datasets

2.21.0

🟢 5.9

Details

Check	Score	Reason
Code-Review	🟢 4	Found 14/30 approved changesets -- score normalized to 4
Maintained	🟢 10	30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Signed-Releases	⚠️ -1	no releases found
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
Security-Policy	🟢 10	security policy file detected
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Packaging	⚠️ -1	packaging workflow not detected
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Binary-Artifacts	🟢 10	no binaries found in the repo
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
Vulnerabilities	🟢 10	0 existing vulnerabilities detected
Fuzzing	⚠️ 0	project is not fuzzed
SAST	⚠️ 0	SAST tool is not run on all commits -- score normalized to 0

pip/einops

0.8.0

🟢 4.5

Details

Check	Score	Reason
Code-Review	⚠️ 1	Found 4/24 approved changesets -- score normalized to 1
Maintained	🟢 6	5 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 6
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Signed-Releases	⚠️ -1	no releases found
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
Packaging	⚠️ -1	packaging workflow not detected
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Binary-Artifacts	🟢 10	no binaries found in the repo
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
Security-Policy	⚠️ 0	security policy file not detected
Fuzzing	⚠️ 0	project is not fuzzed
Vulnerabilities	🟢 10	0 existing vulnerabilities detected
SAST	⚠️ 0	SAST tool is not run on all commits -- score normalized to 0

pip/mkl

2024.2.1

Unknown

pip/mkl-include

2024.2.1

Unknown

pip/onnxruntime

1.19.2

🟢 6.8

Details

Check	Score	Reason
Code-Review	🟢 10	all last 30 commits are reviewed through GitHub
Maintained	🟢 10	30 commit(s) out of 30 and 8 issue activity out of 30 found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no badge detected
Vulnerabilities	🟢 10	no vulnerabilities detected
Signed-Releases	⚠️ 0	0 out of 5 artifacts are signed or have provenance
Branch-Protection	🟢 8	branch protection is not maximal on development and all release branches
Security-Policy	🟢 10	security policy file detected
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Packaging	⚠️ -1	no published package detected
License	🟢 10	license file detected
Token-Permissions	⚠️ 0	non read-only tokens detected in GitHub workflows
Dependency-Update-Tool	🟢 10	update tool detected
Binary-Artifacts	🟢 10	no binaries found in the repo
Fuzzing	⚠️ 0	project is not fuzzed
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0

pip/onnxruntime-extensions

0.12.0

🟢 6.1

Details

Check	Score	Reason
Code-Review	🟢 9	Found 27/30 approved changesets -- score normalized to 9
Maintained	🟢 10	30 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Signed-Releases	⚠️ -1	no releases found
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
Packaging	⚠️ -1	packaging workflow not detected
Security-Policy	🟢 10	security policy file detected
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
SAST	⚠️ 0	SAST tool is not run on all commits -- score normalized to 0
Fuzzing	⚠️ 0	project is not fuzzed
Vulnerabilities	🟢 10	0 existing vulnerabilities detected
Binary-Artifacts	🟢 7	binaries present in source code
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0

pip/peft

0.12.0

Unknown

pip/protobuf

5.28.0

🟢 6.8

Details

Check	Score	Reason
Binary-Artifacts	🟢 10	no binaries found in the repo
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
CI-Tests	🟢 10	24 out of 24 merged PRs checked by a CI test -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
Code-Review	⚠️ 0	found 29 unreviewed changesets out of 30 -- score normalized to 0
Contributors	🟢 10	13 different organizations found -- score normalized to 10
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Dependency-Update-Tool	🟢 10	update tool detected
Fuzzing	🟢 10	project is fuzzed
License	🟢 9	license file detected
Maintained	🟢 10	30 commit(s) out of 30 and 14 issue activity out of 30 found in the last 90 days -- score normalized to 10
Packaging	⚠️ -1	no published package detected
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
SAST	⚠️ 1	SAST tool is not run on all commits -- score normalized to 1
Security-Policy	🟢 10	security policy file detected
Signed-Releases	⚠️ 0	0 out of 5 artifacts are signed or have provenance
Token-Permissions	🟢 10	GitHub workflow tokens follow principle of least privilege
Vulnerabilities	🟢 7	3 existing vulnerabilities detected

pip/psutil

6.0.0

🟢 5.8

Details

Check	Score	Reason
Code-Review	⚠️ 2	Found 8/30 approved changesets -- score normalized to 2
Maintained	🟢 10	9 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Signed-Releases	⚠️ -1	no releases found
Packaging	⚠️ -1	packaging workflow not detected
Security-Policy	🟢 10	security policy file detected
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Binary-Artifacts	🟢 10	no binaries found in the repo
Branch-Protection	⚠️ 0	branch protection not enabled on development/release branches
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
Fuzzing	🟢 10	project is fuzzed
Vulnerabilities	🟢 10	0 existing vulnerabilities detected
SAST	⚠️ 0	SAST tool is not run on all commits -- score normalized to 0

pip/tokenizers

0.20.0

🟢 5.3

Details

Check	Score	Reason
Code-Review	🟢 8	Found 24/27 approved changesets -- score normalized to 8
Maintained	🟢 10	30 commit(s) and 20 issue activity found in the last 90 days -- score normalized to 10
CII-Best-Practices	⚠️ 0	no effort to earn an OpenSSF best practices badge detected
License	🟢 10	license file detected
Signed-Releases	⚠️ -1	no releases found
Branch-Protection	⚠️ -1	internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration
Binary-Artifacts	🟢 10	no binaries found in the repo
Dangerous-Workflow	🟢 10	no dangerous workflow patterns detected
Token-Permissions	⚠️ 0	detected GitHub workflow tokens with excessive permissions
Security-Policy	⚠️ 0	security policy file not detected
Pinned-Dependencies	⚠️ 0	dependency not pinned by hash detected -- score normalized to 0
Fuzzing	⚠️ 0	project is not fuzzed
Packaging	🟢 10	packaging workflow detected
SAST	⚠️ 0	SAST tool is not run on all commits -- score normalized to 0
Vulnerabilities	🟢 3	7 existing vulnerabilities detected

Scanned Manifest Files

workflows/charts/huggingface-llm/requirements.txt

accelerate@0.34.2
datasets@2.21.0
einops@0.8.0
mkl@2024.2.1
mkl-include@2024.2.1
onnxruntime@1.19.2
onnxruntime-extensions@0.12.0
peft@0.12.0
protobuf@5.28.0
psutil@6.0.0
tokenizers@0.20.0
accelerate@0.30.1
datasets@2.19.0
einops@0.7.0
mkl@2023.2.0
mkl-include@2023.2.0
onnxruntime@1.17.3
onnxruntime-extensions@0.10.1
peft@0.11.1
protobuf@4.24.4
psutil@5.9.5
tokenizers@0.19.1

Bumps the genai-workflow group with 11 updates in the /workflows/charts/huggingface-llm directory: | Package | From | To | | --- | --- | --- | | [accelerate](https://github.com/huggingface/accelerate) | `0.30.1` | `0.34.2` | | [datasets](https://github.com/huggingface/datasets) | `2.19.0` | `2.21.0` | | [einops](https://github.com/arogozhnikov/einops) | `0.7.0` | `0.8.0` | | [mkl-include](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html) | `2023.2.0` | `2024.2.1` | | [mkl](https://github.com/oneapi-src/oneMKL) | `2023.2.0` | `2024.2.1` | | [onnxruntime-extensions](https://github.com/microsoft/onnxruntime-extensions) | `0.10.1` | `0.12.0` | | [onnxruntime](https://github.com/microsoft/onnxruntime) | `1.17.3` | `1.19.2` | | [peft](https://github.com/huggingface/peft) | `0.11.1` | `0.12.0` | | [protobuf](https://github.com/protocolbuffers/protobuf) | `4.24.4` | `5.28.0` | | [psutil](https://github.com/giampaolo/psutil) | `5.9.5` | `6.0.0` | | [tokenizers](https://github.com/huggingface/tokenizers) | `0.19.1` | `0.20.0` | Updates `accelerate` from 0.30.1 to 0.34.2 - [Release notes](https://github.com/huggingface/accelerate/releases) - [Commits](huggingface/accelerate@v0.30.1...v0.34.2) Updates `datasets` from 2.19.0 to 2.21.0 - [Release notes](https://github.com/huggingface/datasets/releases) - [Commits](huggingface/datasets@2.19.0...2.21.0) Updates `einops` from 0.7.0 to 0.8.0 - [Release notes](https://github.com/arogozhnikov/einops/releases) - [Commits](arogozhnikov/einops@v0.7.0...v0.8.0) Updates `mkl-include` from 2023.2.0 to 2024.2.1 Updates `mkl` from 2023.2.0 to 2024.2.1 - [Release notes](https://github.com/oneapi-src/oneMKL/releases) - [Commits](https://github.com/oneapi-src/oneMKL/commits) Updates `onnxruntime-extensions` from 0.10.1 to 0.12.0 - [Release notes](https://github.com/microsoft/onnxruntime-extensions/releases) - [Commits](microsoft/onnxruntime-extensions@v0.10.1...v0.12.0) Updates `onnxruntime` from 1.17.3 to 1.19.2 - [Release notes](https://github.com/microsoft/onnxruntime/releases) - [Changelog](https://github.com/microsoft/onnxruntime/blob/main/docs/ReleaseManagement.md) - [Commits](microsoft/onnxruntime@v1.17.3...v1.19.2) Updates `peft` from 0.11.1 to 0.12.0 - [Release notes](https://github.com/huggingface/peft/releases) - [Commits](huggingface/peft@v0.11.1...v0.12.0) Updates `protobuf` from 4.24.4 to 5.28.0 - [Release notes](https://github.com/protocolbuffers/protobuf/releases) - [Changelog](https://github.com/protocolbuffers/protobuf/blob/main/protobuf_release.bzl) - [Commits](protocolbuffers/protobuf@v4.24.4...v5.28.0) Updates `psutil` from 5.9.5 to 6.0.0 - [Changelog](https://github.com/giampaolo/psutil/blob/master/HISTORY.rst) - [Commits](giampaolo/psutil@release-5.9.5...release-6.0.0) Updates `tokenizers` from 0.19.1 to 0.20.0 - [Release notes](https://github.com/huggingface/tokenizers/releases) - [Changelog](https://github.com/huggingface/tokenizers/blob/main/RELEASE.md) - [Commits](huggingface/tokenizers@v0.19.1...v0.20.0) --- updated-dependencies: - dependency-name: accelerate dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: datasets dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: einops dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: mkl-include dependency-type: direct:production update-type: version-update:semver-major dependency-group: genai-workflow - dependency-name: mkl dependency-type: direct:production update-type: version-update:semver-major dependency-group: genai-workflow - dependency-name: onnxruntime-extensions dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: onnxruntime dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: peft dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow - dependency-name: protobuf dependency-type: direct:production update-type: version-update:semver-major dependency-group: genai-workflow - dependency-name: psutil dependency-type: direct:production update-type: version-update:semver-major dependency-group: genai-workflow - dependency-name: tokenizers dependency-type: direct:production update-type: version-update:semver-minor dependency-group: genai-workflow ... Signed-off-by: dependabot[bot] <support@github.com>

dependabot · 2024-09-12T18:41:45Z

Looks like these dependencies are updatable in another way, so this is no longer needed.

dependabot bot requested review from tylertitsworth and dmsuehir as code owners September 9, 2024 13:18

dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Sep 9, 2024

dependabot bot mentioned this pull request Sep 9, 2024

Bump the genai-workflow group across 1 directory with 11 updates #349

Closed

dependabot bot force-pushed the dependabot/pip/workflows/charts/huggingface-llm/genai-workflow-302004c940 branch from dea4af3 to 466a963 Compare September 10, 2024 17:05

dependabot bot closed this Sep 12, 2024

dependabot bot deleted the dependabot/pip/workflows/charts/huggingface-llm/genai-workflow-302004c940 branch September 12, 2024 18:41

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Bump the genai-workflow group across 1 directory with 11 updates #367

Bump the genai-workflow group across 1 directory with 11 updates #367

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Bump the genai-workflow group across 1 directory with 11 updates #367

Bump the genai-workflow group across 1 directory with 11 updates #367

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dependabot bot commented on behalf of github Sep 9, 2024 • edited Loading

v0.34.1 Patchfix

Bug fixes

v0.34.0: StatefulDataLoader Support, FP8 Improvements, and PyTorch Updates!

Dependency Changes

Core

New Script Behavior Changes

DataLoader Enhancements

FP8 Training Improvements

torchpippy no more, long live torch.distributed.pipelining

Fully Sharded Data Parallelism (FSDP)

2.21.0

Features

What's Changed

v0.8.0: tinygrad, small fixes and updates

TLDR

What's Changed

New Contributors

v0.12.0

What's Changed

New Contributors

v0.11.0

What's changed

Contributions

ONNX Runtime v1.19.2

Announcements

Build System & Packages

Training

Mobile

Generative AI

Contributors

ONNX Runtime v1.19.0

Announcements

Build System & Packages

Core

Performance

v0.12.0: New methods OLoRA, X-LoRA, FourierFT, HRA, and much more

Highlights

New methods

OLoRA

X-LoRA

FourierFT

HRA

Enhancements

Examples

Changes

Casting of the adapter dtype

Adapter device placement

PiSSA

6.0.0

Release v0.20.0: faster encode, better python support

Release v0.20.0

Performances:

Python API

What's Changed

github-actions bot commented Sep 9, 2024 • edited Loading

Dependency Review

License Issues

workflows/charts/huggingface-llm/requirements.txt

OpenSSF Scorecard

Scanned Manifest Files

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`torchpippy` no more, long live `torch.distributed.pipelining`

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