[Frontend] [Core] Integrate Tensorizer in to S3 loading machinery, allow passing arbitrary arguments during save/load

[sangstar]

Tensorizer and S3Model loading integrated, updated tensorizer==2.10.1, support passing all TensorSerializer and TensorDeserializer params

Supersedes the closed #19616 with fixed signed-off commits.

This PR does the following:

• Integrates Tensorizer loading in to the S3Model machinery. It now seamlessly can be used with it to load all non-tensor model artifacts.
• Consequently, Tensorizer now, when serializing, will not only serialize model tensors, but all model artifacts needed to run a model on vLLM, relying on huggingface_hub's snapshot_download.
• This means that when loading with Tensorizer, it is no longer necessary to provide Tensorizer args with --model-loader-extra-config. Providing an S3 directory in the model tag will allow Tensorizer to resolve everything as long as all model artifacts for served_model_name are in the aforementioned directory, and Tensorizer can authenticate to S3 (which is does so with the usual boto3-style AWS environment variables, the s3cmd-style environment variables, an ~/.s3cfg file, or the ~/.aws/ config and credential files on one's home path. For example, after serializing a model with Tensorizer, this now works:

vllm serve s3://my-bucket/vllm/facebook/opt-125m/v1 --load-format=tensorizer

• Original functionality with specifying --model-loader-extra is still supported, and can accept additional nested serialization_kwargs and deserialization_kwargs JSONs, which allow configuring TensorDeserializer and TensorSerializer with arbitrary parameters (as long as they do not conflict with vLLM)
• Updated vLLM's tensorizer version to ==2.10.0. This version comes with the boto3-style credential support.
• Additionally fixes the regex when parsing --model-loader-extra-config to respect newlines.

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[gemini-code-assist]

Summary of Changes

Hello @sangstar, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the integration of Tensorizer within vLLM, particularly for S3-based workflows. It simplifies the loading process by allowing users to specify an S3 directory directly as the model path, while also making the serialization process more complete by including non-tensor model artifacts. Additionally, it provides greater flexibility by enabling the passing of arbitrary Tensorizer arguments during both serialization and deserialization.

Highlights

• Seamless S3 Loading: Tensorizer loading is now integrated with the S3Model machinery. Users can simply provide an S3 directory containing the tensorized model and other artifacts (like config, tokenizer files) as the model tag, and vLLM will automatically load it using Tensorizer, without needing the --model-loader-extra-config flag for basic loading.
• Comprehensive Serialization: When serializing a vLLM model with Tensorizer, the process now automatically includes all necessary model artifacts (config, tokenizer files, etc.) from the original Hugging Face model using huggingface_hub.snapshot_download, in addition to the model tensors. This ensures the serialized output is a complete package ready for deployment.
• Arbitrary Tensorizer Args: The --model-loader-extra-config flag now supports passing arbitrary keyword arguments to Tensorizer's TensorSerializer and TensorDeserializer via nested serialization_kwargs and deserialization_kwargs JSON objects. This allows fine-grained control over Tensorizer's behavior during save and load.
• Tensorizer Version Update: The required Tensorizer version has been updated to 2.10.0, which includes improvements like boto3-style credential support.
• Improved Config Parsing: The regex used to parse the --model-loader-extra-config JSON string has been fixed to correctly handle newlines and leading/trailing whitespace.

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[gemini-code-assist]

Code Review

This pull request significantly enhances Tensorizer integration within vLLM. Key improvements include:

• Simplified S3 Loading: Models can now be loaded from S3 by specifying the directory containing all artifacts (including model.tensors, config.json, tokenizer files, etc.). Tensorizer will automatically download necessary non-tensor files using huggingface_hub.snapshot_download during serialization, making the loading process seamless.
• Arbitrary Argument Passing: Users can now pass arbitrary keyword arguments to TensorSerializer and TensorDeserializer during save and load operations via serialization_kwargs and deserialization_kwargs in TensorizerConfig or through the example script.
• Tensorizer Version Update: The tensorizer dependency is updated to 2.10.0, which includes boto3-style S3 credential support.
• Improved CLI Argument Parsing: The regex for parsing JSON in CLI arguments now correctly handles newlines.
• Refactored Configuration: TensorizerConfig and TensorizerArgs have been refactored for better clarity and flexibility.

The changes are well-tested, including new end-to-end tests for serialization and serving. The example script tensorize_vllm_model.py has been updated to reflect these new capabilities.

One area for minor improvement could be updating the docstring of the example script to more prominently feature the new S3 directory loading mechanism.

[aarnphm]

LGTM. Can we deprecate the previous model-load-extra field?

I think we ought to have a model-loader-config fwiw and maybe follow similar spec as speculative-config

cc @hmellor the config change

[sangstar]

Made some small extra tweaks; fast check tests passing!

[sangstar]

Gentle pokes for visibility! Anything else I can address for y'all? @jeejeelee @aarnphm @ywang96

[sangstar]

Gentle prod! Is there anything else I can assist with in getting this merged? @aarnphm @ywang96 @jeejeelee @DarkLight1337

[aarnphm]

Ok, have a stamp from me. Tiny comment.

[sangstar]

Rebased. Looks like there was a few test failures unrelated to my changes. Anything else needs doing or can this be merged? @aarnphm @DarkLight1337 @jeejeelee @simon-mo @ywang96 @mgoin