[Quantization] Support mixed-precision compression

[dsikka]

SUMMARY:

• Requires [Quantization] Support more than one quant-compressor neuralmagic/compressed-tensors#415
• Updates infer_quantization_format to be infer_per_module_quantization_format such that instead of returning a global format, a per module format is assigned to each module to be used during compression time. All unique compression formats are returned

Testing:

• Will run nightly / e2e once the ct PR lands

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @dsikka, 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 PR introduces support for mixed-precision compression by enabling per-module quantization format inference. It adds new logic to dynamically determine the optimal compression format for individual quantized submodules within a model, based on their specific quantization schemes and sparsity structures. This allows for more granular and efficient compression strategies.

Highlights

• Dynamic Format Inference: I've implemented a new helper function, _get_quant_method, which intelligently determines the appropriate CompressionFormat (e.g., nvfp4_pack_quantized, marlin_24, pack_quantized) for a given module based on its input/weight quantization arguments and sparsity structure.
• Per-Module Quantization Application: I've added infer_per_module_quantization, a core function that iterates through all quantized submodules in a model, applies the dynamic format inference from _get_quant_method, and updates each submodule's quantization_scheme with its specific compression format.
• Integration into Model Compression Workflow: The get_model_compressor utility now leverages infer_per_module_quantization to assign specific compression formats to individual modules, moving towards a more flexible and mixed-precision compression approach.

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[gemini-code-assist]

Code Review

This pull request introduces support for mixed-precision quantization by allowing different compression formats for different modules. The overall approach is sound, but there are a few critical issues in the implementation that need to be addressed. Specifically, there's a logical error in how the quantization formats are inferred and used to configure the model compressor, which could lead to incorrect behavior or runtime errors. Additionally, there are some minor issues like a leftover debug print statement, a missing docstring, and a potential crash when handling sparsity structures. Addressing these points will improve the robustness and maintainability of the new functionality.

[kylesayrs]

Type hint does not match return type of function

[kylesayrs]

This function is called _get_quant_method, but returns a format

[kylesayrs]

Why are we not inferring from scheme.format? Doesn't this override anything the user might have passed?

[kylesayrs]

Rather than waiting until the end to do inference, why not infer a format at the source?

class QuantizationScheme:
    format: Optional[CompressionFormat] = None

    @validate_field("format")
    def validate_format(self, value):
        if self.weights and self.input_activations ...
        
        inferred_format = ...
        if value is not None and value != inferred_format:
            logger.warn_once("Consider using inferred scheme")
            
        return value or inferred_format