vllm.model_executor.warmup.kernel_warmup

def flashinfer_autotune(runner: "GPUModelRunner") -> None:
    """
    Autotune FlashInfer operations.
    FlashInfer have many implementations for the same operation,
    autotuning runs benchmarks for each implementation and stores
    the results. The results are cached transparently and
    future calls to FlashInfer will use the best implementation.
    Without autotuning, FlashInfer will rely on heuristics, which may
    be significantly slower.
    """
    from vllm.utils.flashinfer import autotune

    with torch.inference_mode(), autotune():
        # We skip EPLB here since we don't want to record dummy metrics
        # When autotuning with number of tokens m, flashinfer will autotune
        # operations for all number of tokens up to m.
        # So we only need to run with the max number of tokens.
        runner._dummy_run(runner.scheduler_config.max_num_batched_tokens,
                          skip_eplb=True,
                          is_profile=True)

def kernel_warmup(worker: "Worker"):
    # Deep GEMM warmup
    do_deep_gemm_warmup = (envs.VLLM_USE_DEEP_GEMM
                           and is_deep_gemm_supported()
                           and not envs.VLLM_SKIP_DEEP_GEMM_WARMUP)
    if do_deep_gemm_warmup:
        model = worker.get_model()
        max_tokens = worker.scheduler_config.max_num_batched_tokens
        deep_gemm_warmup(model, max_tokens)

    # FlashInfer autotune for Blackwell (SM 10.0) GPUs
    if has_flashinfer() and current_platform.is_device_capability(100):
        flashinfer_autotune(worker.model_runner)