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Bump the pip group across 1 directory with 5 updates #3

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Bumps the pip group with 5 updates in the / directory:

Package From To
langchain 0.0.101 0.2.2
pytorch-lightning 1.5.0 1.6.0
streamlit 1.12.1 1.30.0
torch 1.12.1 1.13.1
transformers 4.26.1 4.38.0

Updates langchain from 0.0.101 to 0.2.2

Release notes

Sourced from langchain's releases.

langchain-community==0.2.2

Release langchain-community==0.2.2

Changes since langchain-community==0.2.1

community[patch]: Release 0.2.2 (#22496) community, milvus, pinecone, qdrant, mongo: Broadcast operation failure while using simsimd beyond v3.7.7 (#22271) community[minor]: Add tools calls to ChatEdenAI (#22320) docs : Update docstrings for OpenAI base.py (#22221) communty[patch]: Native RAG Support in Prem AI langchain (#22238) community[minor]: Enable retrieval api calls in PebbloRetrievalQA (#21958) community[patch]: deprecate all HF classes (#22444) community[minor]: Improve Cassandra VectorStore as_retriever (#22465) community[patch]: Upstash Vector Store Namespace Support (#22251) community[minor]: Updating payload for pebblo discover API (#22309) community: add standard chat model params to Ollama (#22446) community[patch]: Update OpenVINO embedding and reranker to support static input shape (#22171) community[minor]: Implement MiniMaxChat interface (#22391) community[patch]: Airtable to allow for addtl params (#22092) community[patch]: update embeddings/oracleai.py (#22240) community[patch]: Update the default api_url and reqeust_body of sparkllm embedding (#22136) community[minor]: Add IPEX-LLM BGE embedding support on both Intel CPU and GPU (#22226) community: fix AzureSearch delete documents (#22315) community: fix missing apify_api_token field in ApifyWrapper (#22421) add embed_image API to JinaEmbedding (#22416) doc: fix wrong documentation on FAISS load_local function (#22310) community[patch]: Standardize qianfan model init args name (#22322) community: adding tool_call_id for every ToolCall (#22323) community, docs: update token usage tracking callback + how-to guides (#22145) milvus: New langchain_milvus package and new milvus features (#21077) community[patch]: Update firecrawl api key name (#22183) community[minor]: Add Zep Cloud components + docs + examples (#21671) community[patch]: DuckDB VS - expose similarity, improve performance of from_texts (#20971) community[minor]: clickhouse -- ability to use secure connection (#22108) community[patch]: Put authorized identities behind a feature flag in SharepointLoader (#22125) community: add metadata to chain logging; (#22122) community: Update costs of openai finetuned models (#22124) community[major]: lint for usage of xml library (#22132) doc: Add doc for CassandraByteStore (#22126) docs: fix some spelling mistakes caught by newest version of code spell (#22090) community[minor]: ManticoreSearch engine added to vectorstore (#19117)

langchain==0.2.2

Release langchain==0.2.2

Changes since langchain==0.2.1

langchain[patch]: Release 0.2.2 (#22495) langchain[patch]: Fix MongoDBAtlasVectorSearch reference in self query retriever (#22401) Update parent_document_retriever.py about **kwargs (#22219) infra: update langchainhub and add integration test (#22154) docs: fix some spelling mistakes caught by newest version of code spell (#22090)

... (truncated)

Commits
  • 85aa218 langchain[patch]: Release 0.2.2 (#22495)
  • 8e86080 mistralai[patch]: Release 0.1.8 (#22494)
  • e850de2 huggingface[patch]: release 0.0.2 (#22493)
  • 593de8a docs[patch]: Add robots.txt and root sitemap (#22492)
  • 99a3cad text-splitters[patch]: Release 0.2.1 (#22490)
  • 161b02a core[patch]: Release 0.2.4 (#22489)
  • 50258a7 docs: Update chroma docs link for collection reference (#22472)
  • 9b45374 docs: Added Semantic Cache Example with BedrockChat using Bedrock Embedding… ...
  • 3796672 community, milvus, pinecone, qdrant, mongo: Broadcast operation failure while...
  • 03178ee community[minor]: Add tools calls to ChatEdenAI (#22320)
  • Additional commits viewable in compare view

Updates pytorch-lightning from 1.5.0 to 1.6.0

Release notes

Sourced from pytorch-lightning's releases.

PyTorch Lightning 1.6: Support Intel's Habana Accelerator, New efficient DDP strategy (Bagua), Manual Fault-tolerance, Stability and Reliability.

The core team is excited to announce the PyTorch Lightning 1.6 release ⚡

Highlights

PyTorch Lightning 1.6 is the work of 99 contributors who have worked on features, bug-fixes, and documentation for a total of over 750 commits since 1.5. This is our most active release yet. Here are some highlights:

Introducing Intel's Habana Accelerator

Lightning 1.6 now supports the Habana® framework, which includes Gaudi® AI training processors. Their heterogeneous architecture includes a cluster of fully programmable Tensor Processing Cores (TPC) along with its associated development tools and libraries and a configurable Matrix Math engine.

You can leverage the Habana hardware to accelerate your Deep Learning training workloads simply by passing:

trainer = pl.Trainer(accelerator="hpu")
single Gaudi training
trainer = pl.Trainer(accelerator="hpu", devices=1)
distributed training with 8 Gaudi
trainer = pl.Trainer(accelerator="hpu", devices=8)

The Bagua Strategy

The Bagua Strategy is a deep learning acceleration framework that supports multiple, advanced distributed training algorithms with state-of-the-art system relaxation techniques. Enabling Bagua, which can be considerably faster than vanilla PyTorch DDP, is as simple as:

trainer = pl.Trainer(strategy="bagua")
or to choose a custom algorithm
trainer = pl.Trainer(strategy=BaguaStrategy(algorithm="gradient_allreduce")  # default

Towards stable Accelerator, Strategy, and Plugin APIs

The Accelerator, Strategy, and Plugin APIs are a core part of PyTorch Lightning. They're where all the distributed boilerplate lives, and we're constantly working to improve both them and the overall PyTorch Lightning platform experience.

In this release, we've made some large changes to achieve that goal. Not to worry, though! The only users affected by these changes are those who use custom implementations of Accelerator and Strategy (TrainingTypePlugin) as well as certain Plugins. In particular, we want to highlight the following changes:

  • All TrainingTypePlugins have been renamed to Strategy (#11120). Strategy is a more appropriate name because it encompasses more than simply training communcation. This change is now aligned with the changes we implemented in 1.5, which introduced the new strategy and devices flags to the Trainer.

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.6.0] - 2022-03-29

Added

  • Allow logging to an existing run ID in MLflow with MLFlowLogger (#12290)
  • Enable gradient accumulation using Horovod's backward_passes_per_step (#11911)
  • Add new DETAIL log level to provide useful logs for improving monitoring and debugging of batch jobs (#11008)
  • Added a flag SLURMEnvironment(auto_requeue=True|False) to control whether Lightning handles the requeuing (#10601)
  • Fault Tolerant Manual
    • Add _Stateful protocol to detect if classes are stateful (#10646)
    • Add _FaultTolerantMode enum used to track different supported fault tolerant modes (#10645)
    • Add a _rotate_worker_indices utility to reload the state according the latest worker (#10647)
    • Add stateful workers (#10674)
    • Add an utility to collect the states across processes (#10639)
    • Add logic to reload the states across data loading components (#10699)
    • Cleanup some fault tolerant utilities (#10703)
    • Enable Fault Tolerant Manual Training (#10707)
    • Broadcast the _terminate_gracefully to all processes and add support for DDP (#10638)
  • Added support for re-instantiation of custom (subclasses of) DataLoaders returned in the *_dataloader() methods, i.e., automatic replacement of samplers now works with custom types of DataLoader (#10680)
  • Added a function to validate if fault tolerant training is supported. (#10465)
  • Added a private callback to manage the creation and deletion of fault-tolerance checkpoints (#11862)
  • Show a better error message when a custom DataLoader implementation is not well implemented and we need to reconstruct it (#10719)
  • Show a better error message when frozen dataclass is used as a batch (#10927)
  • Save the Loop's state by default in the checkpoint (#10784)
  • Added Loop.replace to easily switch one loop for another (#10324)
  • Added support for --lr_scheduler=ReduceLROnPlateau to the LightningCLI (#10860)
  • Added LightningCLI.configure_optimizers to override the configure_optimizers return value (#10860)
  • Added LightningCLI(auto_registry) flag to register all subclasses of the registerable components automatically (#12108)
  • Added a warning that shows when max_epochs in the Trainer is not set (#10700)
  • Added support for returning a single Callback from LightningModule.configure_callbacks without wrapping it into a list (#11060)
  • Added console_kwargs for RichProgressBar to initialize inner Console (#10875)
  • Added support for shorthand notation to instantiate loggers with the LightningCLI (#11533)
  • Added a LOGGER_REGISTRY instance to register custom loggers to the LightningCLI (#11533)
  • Added info message when the Trainer arguments limit_*_batches, overfit_batches, or val_check_interval are set to 1 or 1.0 (#11950)
  • Added a PrecisionPlugin.teardown method (#10990)
  • Added LightningModule.lr_scheduler_step (#10249)
  • Added support for no pre-fetching to DataFetcher (#11606)
  • Added support for optimizer step progress tracking with manual optimization (#11848)
  • Return the output of the optimizer.step. This can be useful for LightningLite users, manual optimization users, or users overriding LightningModule.optimizer_step (#11711)
  • Teardown the active loop and strategy on exception (#11620)
  • Added a MisconfigurationException if user provided opt_idx in scheduler config doesn't match with actual optimizer index of its respective optimizer (#11247)
  • Added a loggers property to Trainer which returns a list of loggers provided by the user (#11683)
  • Added a loggers property to LightningModule which retrieves the loggers property from Trainer (#11683)
  • Added support for DDP when using a CombinedLoader for the training data (#11648)
  • Added a warning when using DistributedSampler during validation/testing (#11479)
  • Added support for Bagua training strategy (#11146)
  • Added support for manually returning a poptorch.DataLoader in a *_dataloader hook (#12116)
  • Added rank_zero module to centralize utilities (#11747)
  • Added a _Stateful support for LightningDataModule (#11637)
  • Added _Stateful support for PrecisionPlugin (#11638)

... (truncated)

Commits

Updates streamlit from 1.12.1 to 1.30.0

Release notes

Sourced from streamlit's releases.

1.30.0

What's Changed

New Features 🎉

Bug Fixes 🐛

Other Changes

New Contributors

Full Changelog: streamlit/streamlit@1.29.0...1.30.0

1.29.0

What's Changed

Breaking Changes 🛠

New Features 🎉

... (truncated)

Commits

Updates torch from 1.12.1 to 1.13.1

Release notes

Sourced from torch's releases.

PyTorch 1.13.1 Release, small bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

  • RuntimeError by torch.nn.modules.activation.MultiheadAttention with bias=False and batch_first=True #88669
  • Installation via pip on Amazon Linux 2, regression #88869
  • Installation using poetry on Mac M1, failure #88049
  • Missing masked tensor documentation #89734
  • torch.jit.annotations.parse_type_line is not safe (command injection) #88868
  • Use the Python frame safely in _pythonCallstack #88993
  • Double-backward with full_backward_hook causes RuntimeError #88312
  • Fix logical error in get_default_qat_qconfig #88876
  • Fix cuda/cpu check on NoneType and unit test #88854 and #88970
  • Onnx ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504
  • Onnx operator_export_type on the new registry #87735
  • torchrun AttributeError caused by file_based_local_timer on Windows #85427

The release tracker should contain all relevant pull requests related to this release as well as links to related issues

PyTorch 1.13: beta versions of functorch and improved support for Apple’s new M1 chips are now available

Pytorch 1.13 Release Notes

  • Highlights
  • Backwards Incompatible Changes
  • New Features
  • Improvements
  • Performance
  • Documentation
  • Developers

Highlights

We are excited to announce the release of PyTorch 1.13! This includes stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.

Summary:

  • The BetterTransformer feature set supports fastpath execution for common Transformer models during Inference out-of-the-box, without the need to modify the model. Additional improvements include accelerated add+matmul linear algebra kernels for sizes commonly used in Transformer models and Nested Tensors is now enabled by default.

  • Timely deprecating older CUDA versions allows us to proceed with introducing the latest CUDA version as they are introduced by Nvidia®, and hence allows support for C++17 in PyTorch and new NVIDIA Open GPU Kernel Modules.

  • Previously, functorch was released out-of-tree in a separate package. After installing PyTorch, a user will be able to import functorch and use functorch without needing to install another package.

  • PyTorch is offering native builds for Apple® silicon machines that use Apple's new M1 chip as a beta feature, providing improved support across PyTorch's APIs.

Stable Beta Prototype
Better TransformerCUDA 10.2 and 11.3 CI/CD Deprecation Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIsExtend NNC to support channels last and bf16Functorch now in PyTorch Core LibraryBeta Support for M1 devices Arm® Compute Library backend support for AWS Graviton CUDA Sanitizer

You can check the blogpost that shows the new features here.

Backwards Incompatible changes

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

Updates transformers from 4.26.1 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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Bump the pip group across 1 directory with 5 updates
5bd7191 
Bumps the pip group with 5 updates in the / directory:

| Package | From | To |
| --- | --- | --- |
| [langchain](https://github.com/langchain-ai/langchain) | `0.0.101` | `0.2.2` |
| [pytorch-lightning](https://github.com/Lightning-AI/lightning) | `1.5.0` | `1.6.0` |
| [streamlit](https://github.com/streamlit/streamlit) | `1.12.1` | `1.30.0` |
| [torch](https://github.com/pytorch/pytorch) | `1.12.1` | `1.13.1` |
| [transformers](https://github.com/huggingface/transformers) | `4.26.1` | `4.38.0` |



Updates `langchain` from 0.0.101 to 0.2.2
- [Release notes](https://github.com/langchain-ai/langchain/releases)
- [Commits](langchain-ai/langchain@v0.0.101...langchain==0.2.2)

Updates `pytorch-lightning` from 1.5.0 to 1.6.0
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Changelog](https://github.com/Lightning-AI/pytorch-lightning/blob/1.6.0/CHANGELOG.md)
- [Commits](Lightning-AI/pytorch-lightning@1.5.0...1.6.0)

Updates `streamlit` from 1.12.1 to 1.30.0
- [Release notes](https://github.com/streamlit/streamlit/releases)
- [Commits](streamlit/streamlit@1.12.1...1.30.0)

Updates `torch` from 1.12.1 to 1.13.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.12.1...v1.13.1)

Updates `transformers` from 4.26.1 to 4.38.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.26.1...v4.38.0)

---
updated-dependencies:
- dependency-name: langchain
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: streamlit
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-type: direct:production
  dependency-group: pip
...

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@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Jun 6, 2024
@dependabot dependabot bot mentioned this pull request Jun 6, 2024
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