Fix Llama4 FlashInfer FP4 MoE issues

[nvpohanh]

FlashInfer cutlass FP4 MoE already support Llama4, so remove unncessary asserts and set group to 0 when not used.

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

Test Plan

Llama4 Scout FP4 TP2 concurrency 128 gsm8k accuracy test

Test Result

local-completions (base_url=http://0.0.0.0:8080/v1/completions,model=nvidia/Llama-4-Scout-17B-16E-Instruct-FP4,tokenized_requests=False,tokenizer_backend=None,num_concurrent=128,timeout=120,max_retries=5), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9060|±  |0.0080|
|     |       |strict-match    |     5|exact_match|↑  |0.8901|±  |0.0086|

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request introduces necessary fixes to enable Llama4 models with FlashInfer FP4 MoE. The changes correctly remove assertions that previously blocked the use of apply_router_weight_on_input, which is required for Llama4. Additionally, the pull request robustly handles cases where expert grouping is not used by providing a default value of 0 for group-related parameters passed to the FlashInfer kernel, preventing potential runtime errors. The changes are well-targeted and appear correct.