Added GLM Modeling

[phaelon74]

SUMMARY:
Without a model file for GLM, only experts activated from a calibaration sample, are used. This means accuracy will drop quite a bit if the dataset is not robust and even with a robust dataset, I highly doubt all experts are being hit and pulled from as needed.

With this model file, it should cycle through all experts like Qwen does with it's model files, etc.

TEST PLAN:
I used LLM_Compressor to quant GLM4.7 and it succeeded without issue. Equally I quanted a llama3 model as well, to make sure it didn't break any other flows.

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[gemini-code-assist]

Summary of Changes

Hello @phaelon74, 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 introduces a specialized calibration module for GLM4 Mixture-of-Experts (MoE) models within the llmcompressor framework. The primary goal is to enhance the accuracy and robustness of quantized GLM4 models by ensuring that all experts within the MoE architecture are thoroughly calibrated, rather than just those activated by a limited calibration sample. This is achieved by temporarily routing all tokens to all experts during the calibration phase, while still respecting routing weights for the final output.

Highlights

• GLM4 MoE Calibration Module: A new CalibrationGlm4MoeMoE module has been introduced to enable proper calibration for GLM4 Mixture-of-Experts (MoE) models. This module ensures that during calibration, all tokens are sent to all experts, allowing for comprehensive quantization statistics collection, even for experts not activated by standard routing.
• Enhanced Calibration Accuracy: This change addresses a limitation where, without a dedicated model file, only experts activated by a calibration sample were used, potentially leading to reduced accuracy. The new module ensures all experts are properly calibrated, improving the overall robustness and accuracy of quantized GLM4 models.
• Integration and Testing: The new GLM4 MoE calibration module is integrated into the llmcompressor.modeling package. Testing confirmed successful quantization of GLM4.7 and verified that existing quantization flows for other models like Llama3 remain unaffected.

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[gemini-code-assist]

Code Review

This pull request adds support for GLM MoE model calibration by introducing CalibrationGlm4MoeMoE. The implementation correctly handles passing all tokens to all experts during calibration, which is crucial for accurate quantization. My feedback includes a suggestion to refactor the forward method to improve maintainability and a strong recommendation to add unit tests for this new functionality to ensure its correctness and prevent future regressions.

[mratsim]

Nice, you frontrunned me!

I was tired of waiting for Transformers v5 before doing proper all experts calibration of GLM-4.7 / GLM-4.5-Air / GLM-4.6V (see #2036).

Not doing so can have a dramatic impact and many models out there just use raw LLM compressors (mine included): https://avtc.github.io/aquarium-side-by-side/

[mratsim]

By the way, does this PR work on the vision models as-is like GLM-4.6V?

[phaelon74]

By the way, does this PR work on the vision models as-is like GLM-4.6V?

It should handle it yes, schematically, but I can run it through the test script later tonight and tell you for certain.

[phaelon74]

I believe this is now ready for formal review, after having resolved the issues and added a testing script with results. This is my first PR, so if I missed anything, apologies.