vllm.v1.worker.gpu.warmup ¶

@torch.inference_mode()
def warmup_kernels(model_runner: GPUModelRunner) -> None:
    """Run two execute_model + sample_tokens iterations to JIT compile
    triton kernels.

    The first iteration simulates a prefill with requests of 2 prompt
    tokens each. The second iteration simulates a decode step with all
    requests generating 1 token each.
    """
    prompt_token_ids = [0, 1]
    prompt_len = len(prompt_token_ids)
    num_reqs = min(
        model_runner.scheduler_config.max_num_seqs,
        model_runner.scheduler_config.max_num_batched_tokens // prompt_len,
    )

    num_kv_cache_groups = len(model_runner.kv_cache_config.kv_cache_groups)
    req_ids = [f"_warmup_{i}_" for i in range(num_reqs)]

    # SamplingParams exercising all sampling features.
    if model_runner.is_pooling_model:
        sampling_params = None
        pooling_params = PoolingParams()
    else:
        sampling_params = SamplingParams.for_sampler_warmup()
        pooling_params = None

    # Step 1: Prefill all requests with 2 prompt tokens each.
    new_reqs = [
        NewRequestData.from_request(
            Request(req_ids[i], prompt_token_ids, sampling_params, pooling_params),
            # Each request uses a distinct block per KV cache group.
            block_ids=tuple([i] for _ in range(num_kv_cache_groups)),
            prefill_token_ids=prompt_token_ids,
        )
        for i in range(num_reqs)
    ]

    prefill_output = SchedulerOutput.make_empty()
    prefill_output.scheduled_new_reqs = new_reqs
    prefill_output.num_scheduled_tokens = {rid: prompt_len for rid in req_ids}
    prefill_output.total_num_scheduled_tokens = prompt_len * num_reqs
    prefill_output.num_common_prefix_blocks = [0] * num_kv_cache_groups

    # Disable KV connector for warmup run.
    model_runner.kv_connector.set_disabled(True)
    model_runner.execute_model(prefill_output)

    if not model_runner.is_pooling_model:
        # Warm up sampler and perform a decode step for non-pooling models.

        grammar_output = None
        if model_runner.is_last_pp_rank:
            # Build a GrammarOutput to exercise the structured output bitmask
            # kernel during the prefill step.
            vocab_size = model_runner.model_config.get_vocab_size()
            bitmask_width = (vocab_size + 31) // 32
            grammar_bitmask = np.full(
                (len(req_ids), bitmask_width), fill_value=-1, dtype=np.int32
            )
            grammar_output = GrammarOutput(
                structured_output_request_ids=req_ids, grammar_bitmask=grammar_bitmask
            )

        model_runner.sample_tokens(grammar_output)

        # Step 2: Decode all requests with 1 token each.
        cached_req_data = CachedRequestData.make_empty()
        cached_req_data.req_ids = list(req_ids)
        cached_req_data.new_block_ids = [None] * num_reqs
        cached_req_data.num_computed_tokens = [prompt_len] * num_reqs
        cached_req_data.num_output_tokens = [1] * num_reqs

        decode_output = SchedulerOutput.make_empty()
        decode_output.scheduled_cached_reqs = cached_req_data
        decode_output.num_scheduled_tokens = {rid: 1 for rid in req_ids}
        decode_output.total_num_scheduled_tokens = num_reqs
        decode_output.num_common_prefix_blocks = [0] * num_kv_cache_groups

        model_runner.execute_model(decode_output)
        model_runner.sample_tokens(None)

    # Clean up - process finish_req_ids.
    cleanup_output = SchedulerOutput.make_empty()
    cleanup_output.finished_req_ids = set(req_ids)
    model_runner.execute_model(cleanup_output)
    model_runner.kv_connector.set_disabled(False)
    torch.cuda.synchronize()