[BugFix][Performance] Restore flashinfer autotuning for all scenarios

[varun-sundar-rabindranath]

Purpose

Bug:
on main + B200 : vllm serve openai/gpt-oss-20b --enforce-eager fails.
on main + H100 : VLLM_USE_FLASHINFER_MOE_MXFP4_BF16=1 vllm serve openai/gpt-oss-20b --enforce-eager fails.

Both failures are asserts in the flashinfer code base,

(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm/model_executor/layers/quantization/mxfp4.py", line 1109, in apply
(EngineCore_DP0 pid=3490083)     _ = flashinfer_cutlass_fused_moe(
(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm/utils/flashinfer.py", line 84, in wrapper
(EngineCore_DP0 pid=3490083)     return impl(*args, **kwargs)
(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm-test/lib/python3.10/site-packages/flashinfer/fused_moe/core.py", line 790, in cutlass_fused_moe
(EngineCore_DP0 pid=3490083)     return get_cutlass_fused_moe_module(device_arch).cutlass_fused_moe(
(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm-test/lib/python3.10/site-packages/flashinfer/fused_moe/core.py", line 460, in cutlass_fused_moe
(EngineCore_DP0 pid=3490083)     _, gemm_tactic_1 = tuner.choose_one(
(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm-test/lib/python3.10/site-packages/flashinfer/autotuner.py", line 457, in choose_one
(EngineCore_DP0 pid=3490083)     profiles = self._generate_optimization_profiles(tuning_config, inputs)
(EngineCore_DP0 pid=3490083)   File "/home/varun/code/vllm/vllm-test/lib/python3.10/site-packages/flashinfer/autotuner.py", line 643, in _generate_optimization_profiles
(EngineCore_DP0 pid=3490083)     assert len(opt_shapes) > 0, "Empty tuning buckets are not allowed"
(EngineCore_DP0 pid=3490083) AssertionError: Empty tuning buckets are not allowed

Note that this is the same error reported in #27751

Fix:
Our calls to the flashinfer MoE kernels, set tune_max_num_tokens to the CUDAGraph capture size. When CUDAGraph was disabled, max_capture_size is set 0 and the autotuner asserts. This PR sets tune_max_num_tokens to 1 when CUDAGraphs are disabled (i.e. eager-mode)

Note:
Initially, this issue was thought to manifest in specific scenarios and we resorted to skipping autotuning for those cases in PRs, #27762 and #26729 . This PR reverts the skip logic introduced in those PRs.

Fixes #27751

Test Plan

manually run vllm serve openai/gpt-oss-20b --enforce-eager on B200.
CI

Test Result

Tests Pass

[gemini-code-assist]

Code Review

This pull request effectively resolves a crash that occurred when running MoE models in eager mode by ensuring tune_max_num_tokens is at least 1. The fix is correctly applied across multiple flashinfer kernel invocation sites in trtllm_moe.py and mxfp4.py. Additionally, the removal of the now-redundant flashinfer_autotune_supported function and its associated logic simplifies the codebase and re-enables autotuning for all scenarios, which is a great improvement. The test suite has been updated appropriately to validate the fix. The changes are well-targeted and correct.

[varun-sundar-rabindranath]

cc @zyongye @nvpohanh @mgoin PTAL. Sorry about the confusion with intermediate fixes.

[varun-sundar-rabindranath]

Why were we setting this to self.max_capture_size ? Shouldn't we set this to max_num_batched_tokens atleast ?
Just curious cc @pavanimajety @nvpohanh

[mgoin]

Ohh I see, very interesting. Yes I have the same question of why not use the max batch size, since we will want to autotune not only for cudagraphs for the prefill as well

[nvpohanh]

@nvjullin Could you review this PR and comment on this? Thanks!

[nvjullin]

It comes from PR23608. After a quick look in flashinfer, I believe this parameter is needed because autotuning on a dummy input won't result in the maximum number of tokens at each EP rank. I agree max_num_batched_tokens makes more sense.

[varun-sundar-rabindranath]

cc @nvpohanh
cc @mgoin changes since you last reviewed.

[yewentao256]

LGTM, thanks for the work!

[yewentao256]

Suggested change

	# Enable autotune when,
	# TODO: Enable autotune when,