From cbb27feaf2adf20ff315b4cf38b6cccf68248bcd Mon Sep 17 00:00:00 2001 From: Yizhou <136800916+yiz-liu@users.noreply.github.com> Date: Fri, 21 Nov 2025 08:50:46 +0800 Subject: [PATCH] [Test] Add ACL graph capture/replay DP test (#4259) ### What this PR does / why we need it? Add ACL graph capture/replay DP test, this is a imprved version of #3886 Restructures the multi-card ACL graph test for improved clarity, robustness, and accuracy. Key improvements include: - Replaces fragile `sys.settrace` and manual patching with a clean, reusable spy installer using `unittest.mock.patch`. - Introduces more precise metrics by tracking `NPUModelRunner.execute_model` and `_dummy_run` calls directly. - Rewrites assertions to be more accurate and provides clear explanations for the expected counts of graph captures, replays, model executions, and dummy runs. - Simplifies the overall test structure by separating the worker logic into a dedicated function. - Removes a long, unnecessary sleep at the end of the test. - Expands test coverage by adding a larger `max_tokens` parameter. ### Does this PR introduce _any_ user-facing change? None. ### How was this patch tested? None. - vLLM version: v0.11.0 - vLLM main: https://github.com/vllm-project/vllm/commit/2918c1b49c88c29783c86f78d2c4221cb9622379 --------- Signed-off-by: lilinsiman Signed-off-by: Yizhou Liu Co-authored-by: lilinsiman --- .github/workflows/_e2e_test.yaml | 1 + .../multicard/test_aclgraph_capture_replay.py | 237 ++++++++++++++++++ 2 files changed, 238 insertions(+) create mode 100644 tests/e2e/multicard/test_aclgraph_capture_replay.py diff --git a/.github/workflows/_e2e_test.yaml b/.github/workflows/_e2e_test.yaml index 0dd57b70..bafc4766 100644 --- a/.github/workflows/_e2e_test.yaml +++ b/.github/workflows/_e2e_test.yaml @@ -182,6 +182,7 @@ jobs: VLLM_USE_MODELSCOPE: True if: ${{ inputs.type == 'full' }} run: | + pytest -sv tests/e2e/multicard/test_aclgraph_capture_replay.py pytest -sv tests/e2e/multicard/test_torchair_graph_mode.py pytest -sv tests/e2e/multicard/test_full_graph_mode.py pytest -sv tests/e2e/multicard/test_data_parallel.py diff --git a/tests/e2e/multicard/test_aclgraph_capture_replay.py b/tests/e2e/multicard/test_aclgraph_capture_replay.py new file mode 100644 index 00000000..f4dd4965 --- /dev/null +++ b/tests/e2e/multicard/test_aclgraph_capture_replay.py @@ -0,0 +1,237 @@ +# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. +# Copyright 2023 The vLLM team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import gc +import math +import multiprocessing +import os +from typing import Any +from unittest.mock import patch + +import pytest +import torch + +from vllm_ascend.utils import vllm_version_is + +if vllm_version_is("0.11.0"): + from vllm.utils import get_open_port +else: + from vllm.utils.network_utils import get_open_port + +MODELS = [ + "Qwen/Qwen3-0.6B", + "vllm-ascend/DeepSeek-V2-Lite-W8A8", +] + + +def _install_spies(counters: dict[str, Any]) -> contextlib.ExitStack: + """Installs thread-safe spies on NPU methods to track invocation counts.""" + from vllm_ascend.worker.model_runner_v1 import NPUModelRunner + + def make_spy(cls, method_name, counter): + original = getattr(cls, method_name) + + def spy(self, *args, **kwargs): + with counter.get_lock(): + counter.value += 1 + return original(self, *args, **kwargs) + + return spy + + stack = contextlib.ExitStack() + hooks = [ + (torch.npu.NPUGraph, "replay", counters["replay"]), + (torch.npu.NPUGraph, "__init__", counters["capture"]), + (NPUModelRunner, "execute_model", counters["exec_model"]), + (NPUModelRunner, "_dummy_run", counters["dummy_run"]), + ] + + for cls, method, counter in hooks: + stack.enter_context( + patch.object(cls, method, make_spy(cls, method, counter))) + + return stack + + +def _run_worker_process( + rank: int, + local_rank: int, + world_size: int, + master_ip: str, + master_port: int, + counters: dict[str, Any], + model_path: str, + max_tokens: int, +): + """Main entry point for the worker process.""" + os.environ.update({ + "VLLM_DP_RANK": str(rank), + "VLLM_DP_RANK_LOCAL": str(local_rank), + "VLLM_DP_SIZE": str(world_size), + "VLLM_DP_MASTER_IP": master_ip, + "VLLM_DP_MASTER_PORT": str(master_port), + }) + + # Import vLLM only after environment setup + from vllm import LLM, SamplingParams + from vllm.distributed.parallel_state import ( + destroy_distributed_environment, destroy_model_parallel) + + # Apply hooks and run inference + with _install_spies(counters): + prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", + ] + + # Simple data sharding + chunk_size = len(prompts) // world_size + start_idx = rank * chunk_size + end_idx = start_idx + chunk_size if rank < world_size - 1 else len( + prompts) + local_prompts = prompts[start_idx:end_idx] + + llm = LLM( + model=model_path, + quantization="ascend" if "W8A8" in model_path else None, + # enable_expert_parallel=True if "DeepSeek" in model_path else False, + trust_remote_code=True, + ) + + # Expose model config to the main test process + counters["hidden_layers"].value = ( + llm.llm_engine.model_config.hf_config.num_hidden_layers) + + llm.generate(local_prompts, + SamplingParams(max_tokens=max_tokens, temperature=0.0)) + + # Explicit cleanup is mandatory in multi-process vLLM tests + del llm + + destroy_model_parallel() + destroy_distributed_environment() + + with contextlib.suppress(AssertionError): + torch.distributed.destroy_process_group() + + gc.collect() + torch.npu.empty_cache() + torch.npu.reset_peak_memory_stats() + + +# @patch.dict(os.environ, clear=["HCCL_OP_EXPANSION_MODE","VLLM_WORKER_MULTIPROC_METHOD"]) +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("max_tokens", [4, 36]) +@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"}) +def test_aclgraph_capture_replay_dp2( + model: str, + max_tokens: int, + monkeypatch: pytest.MonkeyPatch, +) -> None: + # Counter doesn't work in default "spawn" mode + monkeypatch.delenv("VLLM_WORKER_MULTIPROC_METHOD", raising=False) + + # Shared counters for cross-process assertion + counters = { + "replay": multiprocessing.Value("i", 0), + "capture": multiprocessing.Value("i", 0), + "exec_model": multiprocessing.Value("i", 0), + "dummy_run": multiprocessing.Value("i", 0), + "hidden_layers": multiprocessing.Value("i", -1), + } + + dp_size = 2 + port = get_open_port() + + # Launch workers + workers = [] + for rank in range(dp_size): + p = multiprocessing.Process( + target=_run_worker_process, + args=(rank, rank, dp_size, "127.0.0.1", port, counters, model, + max_tokens), + ) + p.start() + workers.append(p) + + # Supervision loop + for p in workers: + p.join(timeout=900) + if p.exitcode != 0: + for k in workers: + if k.is_alive(): + k.kill() + raise RuntimeError( + f"Worker {p.pid} failed with exit code {p.exitcode}") + + actual_capture = counters["capture"].value + actual_replay = counters["replay"].value + num_execute_model = counters["exec_model"].value + num_dummy_run = counters["dummy_run"].value + num_layers = counters["hidden_layers"].value + + num_acl_graphs = num_layers + 1 + num_comm_groups = sum(1 for s in [dp_size, 1] + if s > 1) # dp_size=2, tp_size=1 + + # Metric 1: Graph Capture (ACL Graph Construction) + # Ref: vllm_ascend.utils.update_aclgraph_sizes + max_batch_sizes = math.floor((1800 - num_comm_groups * 40) / + num_acl_graphs / (1 + num_comm_groups * 2)) + + expected_capture = max_batch_sizes * num_acl_graphs * dp_size + assert ( + actual_capture == expected_capture + ), f"Capture count mismatch. Expected: {expected_capture}, Got: {actual_capture}" + + # Metric 2: Model Execution (NPUModelRunner.execute_model) + # vLLM Step Breakdown: + # 1. First step (prefill, 1 prompt) + # 2. Generation steps (max_tokens) + # 3. Final step (likely EOS/idle step), no replay here + total_steps = max_tokens + 1 # this includes the 1 and 2 above + expected_exec_model = (total_steps + 1) * dp_size + + assert ( + num_execute_model == expected_exec_model + ), f"Model execution count mismatch. Expected: {expected_exec_model}, Got: {num_execute_model}" + + # Metric 3: Dummy Runs (Warmup & Alignment) + # vLLM synchronizes globally every 32 steps. + # Ref: vllm.v1.engine.core.DPEngineCoreProc._has_global_unfinished_reqs + aligned_steps = (total_steps + 31) // 32 * 32 + + # Part A: Warmup runs (Profile run + 2 runs per captured graph) + warmup_runs = 1 + (2 * max_batch_sizes) + + # Part B: Alignment padding (Empty runs to hit the 32-step boundary) + padding_runs = aligned_steps - total_steps + + expected_dummy_run = (warmup_runs + padding_runs) * dp_size + + assert ( + num_dummy_run == expected_dummy_run + ), f"Dummy run count mismatch. Expected: {expected_dummy_run}, Got: {num_dummy_run}" + + # Metric 4: Graph Replay (Inference Execution) + # Replays happen for every aligned step across all graphs. + expected_replay = num_acl_graphs * aligned_steps * dp_size + + assert ( + actual_replay == expected_replay + ), f"Replay count mismatch. Expected: {expected_replay}, Got: {actual_replay}"