Enable ZP Support for Machete

[czhu-cohere]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

The Machete kernel already support ZP so enable it in the vLLM frontend. For pre-applying the scales we follow https://github.com/vllm-project/vllm/blob/main/csrc/quantization/machete/Readme.md

Test Plan

There are already correctness test with zp in test_machete_mm.py (under AWQ style configs). We test the latency diff with/without zp (using benchmark_machete.py) and the e2e correctness (lm-eval, sanity check queries).

We tested on an internal w4a16 asym gs=128 checkpoint (using compressed-tensors) based on CohereLabs/c4ai-command-a-03-2025. The quant config is

  "quantization_config": {
    "config_groups": {
      "group_0": {
        "input_activations": null,
        "output_activations": null,
        "targets": [
          "Linear"
        ],
        "weights": {
          "actorder": "weight",
          "block_structure": null,
          "dynamic": false,
          "group_size": 128,
          "num_bits": 4,
          "observer": "minmax",
          "observer_kwargs": {},
          "strategy": "group",
          "symmetric": false,
          "type": "int"
        }
      }
    },
    "format": "pack-quantized",
    "global_compression_ratio": null,
    "ignore": [
      "lm_head"
    ],
    "kv_cache_scheme": null,
    "quant_method": "compressed-tensors",
    "quantization_status": "compressed"
  },

Test Result

Sanity check query

python3       -m vllm.entrypoints.openai.api_server       --model /root/engines/CA-w4a16-gs128-asymm/poseidon --tensor-parallel-size 1 --max-model-len 2048

curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d '{
        "model": "/root/engines/CA-w4a16-gs128-asymm/poseidon",
        "prompt": "San Francisco is a",
        "max_tokens": 25,
        "temperature": 0
    }'

completion w/machete

"top holiday destination featuring scenic beauty and great ethnic and cultural diversity.\n\nSan Francisco is known for nature, parks, and"

completion w/marlin

"top holiday destination featuring scenic beauty and great ethnic and cultural diversity.\n\nSan Francisco is known for nature, parks, and"

lm-eval (mmlu_pro)

machete
mmlu_pro: {'exact_match,custom-extract': np.float64(0.698969414893617), 'exact_match_stderr,custom-extract': np.float64(0.004097131976561064), 'alias': 'mmlu_pro'}

marlin
mmlu_pro: {'exact_match,custom-extract': np.float64(0.6967253989361702), 'exact_match_stderr,custom-extract': np.float64(0.004102789695366856), 'alias': 'mmlu_pro'}

latency

python3 benchmarks/kernels/benchmark_machete.py \ --act-type bfloat16 \ --group-scale-type bfloat16 \ --out-type bfloat16 \ --group-zero-type bfloat16 \model_bench

the summary is that across the different shapes, with zp is ~1.5% higher latency on average.

(Optional) Documentation Update

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[gemini-code-assist]

Summary of Changes

Hello @czhu-cohere, 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 enables Zero Point support for the Machete kernel within the vLLM frontend, as the Machete kernel already supports it. This change removes the previous restriction that prevented the use of zero points with Machete.

Highlights

• Machete Kernel: Enabled Zero Point (ZP) support for the Machete kernel in the vLLM frontend.

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[gemini-code-assist]

Code Review

This pull request enables zero-point quantization for the Machete kernel by removing a feature guard. Ensure adequate test coverage at the MacheteQuantLinear layer level to prevent regressions.

[robertgshaw2-redhat]

cc @LucasWilkinson is this okay for machete?

[LucasWilkinson]

Makes sense to me! Thanks for the contribution! Happy to see that diff is relatively small 😄. Side-note: I can't access the google doc; id love to see the numbers.

[czhu-cohere]

Thanks @mgoin @LucasWilkinson , I uploaded a screenshot for the latency numbers