fix pydantic validation errors

[brian-dellabetta]

SUMMARY:
Recent changes to compressed-tensors are causing some llm-compressor unit tests to fail. Fixing them on this branch

TEST PLAN:
tests pass

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[gemini-code-assist]

Summary of Changes

Hello @brian-dellabetta, 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 addresses failing unit tests by updating the quantization scheme configuration in test_sparse.py. The failures were introduced by a recent change in the compressed-tensors library, necessitating an update to how quantization arguments are defined and applied.

Highlights

• Update Quantization Imports: Added QuantizationStrategy and QuantizationType imports to align with the updated compressed-tensors API.
• Refactor Quantization Scheme: Modified the QuantizationScheme for the fc1 layer, changing the target from a specific module name ("model.fc1") to a module type ("Linear").
• Adjust Quantization Arguments: Updated the QuantizationArgs for weights to use the new parameters (num_bits, type, group_size, strategy, symmetric, dynamic) instead of the deprecated ones (precision, granularity, algorithm, blocksize).

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[gemini-code-assist]

Code Review

This pull request updates a unit test to align with recent API changes in the compressed-tensors library. The modifications to QuantizationScheme correctly reflect the new API. I've identified a minor inconsistency in the test data where the configured quantization bit-width doesn't match the mocked data type. My suggestion aims to align these to improve the test's clarity and correctness.