[Kernels] Support blocked fp8 quantization for compressed tensors MoE

[bnellnm]

Purpose

Add fp8 blocked quantization support to compressed tensors MoE.

Test Plan

Run lm-eval on the following models:

• RedHatAI/Qwen3-30B-A3B-FP8-BLOCK
• nm-testing/Llama-4-Maverick-17B-128E-Instruct-FP8-BLOCK
• nm-testing/Llama-4-Scout-17B-16E-Instruct-FP8-BLOCK

Test Result

• RedHatAI/Qwen3-30B-A3B-FP8-BLOCK

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.86|±  |0.0349|
|     |       |strict-match    |     5|exact_match|↑  | 0.95|±  |0.0219|

• nm-testing/Llama-4-Maverick-17B-128E-Instruct-FP8-BLOCK

OOM'd on 8 gpu machine.

• nm-testing/Llama-4-Scout-17B-16E-Instruct-FP8-BLOCK

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.94|±  |0.0239|
|     |       |strict-match    |     5|exact_match|↑  | 0.92|±  |0.0273|

[gemini-code-assist]

Code Review

This pull request adds support for blocked FP8 quantization for Mixture of Experts (MoE) layers, primarily to enable DeepGEMM kernels. The changes are mostly in vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py. My review identifies a critical issue with parameter registration that could lead to incorrect behavior and makes the code confusing. I've also noted a minor issue in vllm/model_executor/warmup/deep_gemm_warmup.py.

[bnellnm]

@mgoin , @tlrmchlsmth I think this is ready to go as a first cut.

[yewentao256]

Looks good and thanks for the work!
Could you also add performance metric using vllm bench throughput...?

[bnellnm]

Benchmark with DP=4 + deepep_high_throughput

============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  60.33
Total input tokens:                      1021533
Total generated tokens:                  110757
Request throughput (req/s):              16.58
Output token throughput (tok/s):         1835.83
Peak output token throughput (tok/s):    4559.00
Peak concurrent requests:                1000.00
Total Token throughput (tok/s):          18768.05
---------------Time to First Token----------------
Mean TTFT (ms):                          20646.11
Median TTFT (ms):                        23193.82
P99 TTFT (ms):                           50440.13
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          176.28
Median TPOT (ms):                        183.99
P99 TPOT (ms):                           310.81
---------------Inter-token Latency----------------
Mean ITL (ms):                           173.92
Median ITL (ms):                         114.73
P99 ITL (ms):                            2261.17
==================================================