xc-llm-ascend/tests/e2e/singlecard/test_multi_instance.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Two VllmRunner instances are nested so that the first instance's worker
process is still holding NPU memory when the second instance's worker process
starts.  Both instances must:

  1. Initialize without raising any exception (no OOM during
     determine_available_memory / KV-cache allocation).
  2. Successfully complete a short generation request.

The model is Qwen/Qwen3-0.6B (~0.5 GiB weights) and gpu_memory_utilization
is set to 0.4 per instance so that two instances comfortably fit on a single
64 GiB Ascend 910B card while leaving enough headroom to avoid the
pre-fix negative-KV-cache condition.
"""

import os

from tests.e2e.conftest import VllmRunner

os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

MODEL = "Qwen/Qwen3-0.6B"
_PROMPTS = ["Hello, my name is"]
_MAX_TOKENS = 5
# Use a low utilization so two instances fit side-by-side on one card:
#   2 × 0.4 × card_total ≤ card_total  (holds for any card ≥ 1 GiB)
_GPU_MEM_UTIL = 0.4
_MAX_MODEL_LEN = 512


def test_two_instances_on_single_card() -> None:
    """
    Regression test for PR #7427 (multi-instance OOM on single card).

    Start a first vllm-ascend instance; while it is still running and holding
    NPU memory, start a second instance with identical settings.  Both must
    initialize correctly and produce non-empty outputs.

    Failure signature (pre-fix):
        RuntimeError / ValueError during the second instance's init, or
        "Available KV cache memory: -X.XX GiB" in the logs followed by
        zero KV blocks being allocated.
    """
    # ── First instance ──────────────────────────────────────────────────
    with VllmRunner(
        MODEL,
        max_model_len=_MAX_MODEL_LEN,
        gpu_memory_utilization=_GPU_MEM_UTIL,
        enforce_eager=True,
    ) as runner1:
        # ── Second instance starts while first is still alive ────────────
        # This is the exact scenario from PR #7427: the second worker process
        # sees a reduced init_snapshot.free_memory because the first instance's
        # worker is still holding NPU memory.
        with VllmRunner(
            MODEL,
            max_model_len=_MAX_MODEL_LEN,
            gpu_memory_utilization=_GPU_MEM_UTIL,
            enforce_eager=True,
        ) as runner2:
            outputs2 = runner2.generate_greedy(_PROMPTS, max_tokens=_MAX_TOKENS)

        outputs1 = runner1.generate_greedy(_PROMPTS, max_tokens=_MAX_TOKENS)

    # ── Assertions ───────────────────────────────────────────────────────
    assert outputs1, "First instance produced no outputs"
    assert outputs2, "Second instance produced no outputs"

    _, text1 = outputs1[0]
    _, text2 = outputs2[0]

    assert text1, "First instance output text is empty — model may have failed to run"
    assert text2, (
        "Second instance output text is empty — "
        "KV cache may have been allocated with zero blocks (pre-fix OOM regression)"
    )