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[CI][Doc] Optimize multi-node CI (#3565)

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### What this PR does / why we need it?
This pull request mainly do the following things:
1. Add a doc for multi-node CI, The main content is the mechanism
principle and how to contribute
2. Simplify the config yaml for more developer-friendly
3. Optimized the mooncake installation script to prevent accidental
failures during installation
4. Fix the workflow to ensure the kubernetes can be apply correctly
5. Add Qwen3-235B-W8A8 disaggregated_prefill test
6. Add GLM-4.5 multi dp test
7. Add 2p1d 4nodes disaggregated_prefill test
8. Refactor nightly tests
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2025-10-25 09:23:47 +08:00
committed by GitHub
parent 292cf339c3
commit 7f73c28a24
21 changed files with 1165 additions and 378 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
tests/e2e/conftest.py
View File

@@ -49,6 +49,7 @@ from vllm.utils import get_open_port
from tests.e2e.model_utils import (TokensTextLogprobs,
TokensTextLogprobsPromptLogprobs)
from tests.e2e.nightly.multi_node.config.multi_node_config import NodeInfo
from vllm_ascend.ascend_config import clear_ascend_config
# TODO: remove this part after the patch merged into vllm, if
# we not explicitly patch here, some of them might be effectiveless
@@ -115,6 +116,9 @@ class RemoteOpenAIServer:
env_dict: Optional[dict[str, str]] = None,
seed: Optional[int] = None,
auto_port: bool = True,
nodes_info: Optional[list[NodeInfo]] = None,
disaggregated_prefill: Optional[dict] = None,
proxy_port: Optional[int] = None,
max_wait_seconds: Optional[float] = None,
override_hf_configs: Optional[dict[str, Any]] = None) -> None:
if isinstance(vllm_serve_args, str):
@@ -144,13 +148,23 @@ class RemoteOpenAIServer:
"--hf-overrides",
json.dumps(override_hf_configs)
]
self.host = str(server_host)
self.port = int(server_port)
# for multi-nodes test
self.nodes_info = nodes_info
self.disaggregated_prefill = disaggregated_prefill
self.cur_index = os.getenv("LWS_WORKER_INDEX", 0)
self.proxy_port = proxy_port
self._start_server(model, vllm_serve_args, env_dict)
max_wait_seconds = max_wait_seconds or 7200
self._wait_for_server(url=self.url_for("health"),
timeout=max_wait_seconds)
if self.disaggregated_prefill:
assert proxy_port is not None, "for disaggregated_prefill, proxy port must be provided"
self._wait_for_server_pd(proxy_port=proxy_port)
else:
self._wait_for_server(url=self.url_for("health"),
timeout=max_wait_seconds)
def __enter__(self):
return self
@@ -187,6 +201,21 @@ class RemoteOpenAIServer:
if isinstance(client, httpx.Client):
client.close()
def _wait_for_server_pd(self, proxy_port: int):
# Wait for all api_server nodes ready
assert self.nodes_info is not None, "cluster info must be provided"
for node_info in self.nodes_info:
if node_info.headless:
continue
url_health = f"http://{node_info.ip}:{node_info.server_port}/health"
self._wait_for_server(url=url_health, timeout=7200)
# Wait for proxy ready
master_node = self.nodes_info[0]
url_proxy = f"http://{master_node.ip}:{proxy_port}/healthcheck"
self._wait_for_server(url=url_proxy, timeout=7200)
def _wait_for_server(self, *, url: str, timeout: float):
# run health check
start = time.time()

163
tests/e2e/nightly/multi_node/config/models/DeepSeek-R1-W8A8.yaml Normal file
View File

@@ -0,0 +1,163 @@
test_name: "test DeepSeek-R1-W8A8 disaggregated_prefill"
model: "vllm-ascend/DeepSeek-R1-0528-W8A8"
num_nodes: 4
npu_per_node: 16
env_common:
VLLM_USE_MODELSCOPE: true
HCCL_BUFFSIZE: 1024
SERVER_PORT: 8080
OMP_PROC_BIND: false
OMP_NUM_THREADS: 10
PYTORCH_NPU_ALLOC_CONF: expandable_segments:True
HCCL_DETERMINISTIC: True
TASK_QUEUE_ENABLE: 1
HCCL_OP_RETRY_ENABLE: "L0:0, L1:0, L2:0"
disaggregated_prefill:
enabled: true
prefiller_host_index: [0, 1]
decoder_host_index: [2]
ranktable_gen_path: "examples/disaggregated_prefill_v1/gen_ranktable.py"
ranktable_path: "/tmp/ranktable.json"
deployment:
-
server_cmd: >
vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 2
--data-parallel-size-local 2
--tensor-parallel-size 8
--enforce-eager
--enable-expert-parallel
--seed 1024
--quantization ascend
--max-num-seqs 4
--max-model-len 36864
--max-num-batched-tokens 16384
--trust-remote-code
--gpu-memory-utilization 0.9
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
--kv-transfer-config
'{"kv_connector": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_producer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
--additional-config
'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":false,"enable_multistream_shared_expert":false},"enable_prefill_optimizations":true,"enable_weight_nz_layout":true}'
-
server_cmd: >
vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 2
--data-parallel-size-local 2
--tensor-parallel-size 8
--enforce-eager
--enable-expert-parallel
--seed 1024
--quantization ascend
--max-num-seqs 4
--max-model-len 36864
--max-num-batched-tokens 16384
--trust-remote-code
--gpu-memory-utilization 0.9
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
--kv-transfer-config
'{"kv_connector": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_producer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
--additional-config
'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":false,"enable_multistream_shared_expert":false},"enable_prefill_optimizations":true,"enable_weight_nz_layout":true}'
-
server_cmd: >
vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 32
--data-parallel-size-local 16
--data-parallel-start-rank 0
--data-parallel-address $LOCAL_IP
--data-parallel-rpc-port 13389
--tensor-parallel-size 1
--enable-expert-parallel
--seed 1024
--quantization ascend
--max-num-seqs 28
--max-model-len 36864
--max-num-batched-tokens 256
--trust-remote-code
--gpu-memory-utilization 0.9
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
--kv-transfer-config
'{"kv_connector": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_consumer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
--additional-config
'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":true,"enable_multistream_mla":true,"graph_batch_sizes":[28],"use_cached_graph":true,"enable_super_kernel":false},"multistream_overlap_shared_expert":true}'
-
server_cmd: >
vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
--headless
--data-parallel-size 32
--data-parallel-size-local 16
--data-parallel-start-rank 16
--data-parallel-address $MASTER_IP
--data-parallel-rpc-port 13389
--tensor-parallel-size 1
--enable-expert-parallel
--seed 1024
--quantization ascend
--max-num-seqs 28
--max-model-len 36864
--max-num-batched-tokens 256
--trust-remote-code
--gpu-memory-utilization 0.9
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
--kv-transfer-config
'{"kv_connector": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_consumer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
--additional-config
'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":true,"enable_multistream_mla":true,"graph_batch_sizes":[28],"use_cached_graph":true,"enable_super_kernel":false},"multistream_overlap_shared_expert":true}'
benchmarks:
perf:
case_type: performance
dataset_path: vllm-ascend/GSM8K-in3500-bs400
request_conf: vllm_api_stream_chat
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
num_prompts: 1
max_out_len: 2
batch_size: 1
baseline: 5
threshold: 0.97
acc:
case_type: accuracy
dataset_path: vllm-ascend/AIME2024
request_conf: vllm_api_general_chat
dataset_conf: aime2024/aime2024_gen_0_shot_chat_prompt
max_out_len: 10
batch_size: 32
baseline: 1
threshold: 1

8
tests/e2e/nightly/multi_node/config/models/DeepSeek-V3.yaml
View File

@@ -26,10 +26,6 @@ disaggregated_prefill:
deployment:
-
local_index: 0
master_index: 0
headless: false
env_extend:
server_cmd: >
vllm serve "vllm-ascend/DeepSeek-V3-W8A8"
--host 0.0.0.0
@@ -66,10 +62,6 @@ deployment:
}'
-
local_index: 1
master_index: 0
headless: true
env_extend:
server_cmd: >
vllm serve "vllm-ascend/DeepSeek-V3-W8A8"
--host 0.0.0.0

68
tests/e2e/nightly/multi_node/config/models/GLM-4_5.yaml Normal file
View File

@@ -0,0 +1,68 @@
test_name: "test GLM-4.5 multi-dp"
model: "ZhipuAI/GLM-4.5"
num_nodes: 2
npu_per_node: 16
env_common:
VLLM_USE_MODELSCOPE: true
OMP_PROC_BIND: false
OMP_NUM_THREADS: 100
HCCL_BUFFSIZE: 1024
SERVER_PORT: 8080
deployment:
-
server_cmd: >
vllm serve "ZhipuAI/GLM-4.5"
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 4
--data-parallel-size-local 2
--data-parallel-address $LOCAL_IP
--data-parallel-rpc-port 13389
--tensor-parallel-size 8
--seed 1024
--enable-expert-parallel
--max-num-seqs 16
--max-model-len 8192
--max-num-batched-tokens 8192
--trust-remote-code
--no-enable-prefix-caching
--gpu-memory-utilization 0.9
-
server_cmd: >
vllm serve "ZhipuAI/GLM-4.5"
--headless
--data-parallel-size 4
--data-parallel-size-local 2
--data-parallel-start-rank 2
--data-parallel-address $MASTER_IP
--data-parallel-rpc-port 13389
--tensor-parallel-size 8
--seed 1024
--max-num-seqs 16
--max-model-len 8192
--max-num-batched-tokens 8192
--enable-expert-parallel
--trust-remote-code
--no-enable-prefix-caching
--gpu-memory-utilization 0.9
benchmarks:
perf:
case_type: performance
dataset_path: vllm-ascend/GSM8K-in3500-bs400
request_conf: vllm_api_stream_chat
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
num_prompts: 1
max_out_len: 2
batch_size: 1
baseline: 5
threshold: 0.97
acc:
case_type: accuracy
dataset_path: vllm-ascend/AIME2024
request_conf: vllm_api_general_chat
dataset_conf: aime2024/aime2024_gen_0_shot_chat_prompt
max_out_len: 10
batch_size: 32
baseline: 1
threshold: 1

8
tests/e2e/nightly/multi_node/config/models/Qwen3-235B-A3B.yaml
View File

@@ -11,10 +11,6 @@ env_common:
deployment:
-
local_index: 0
master_index: 0
headless: false
env_extend:
server_cmd: >
vllm serve "Qwen/Qwen3-235B-A22B"
--host 0.0.0.0
@@ -33,10 +29,6 @@ deployment:
--no-enable-prefix-caching
--gpu-memory-utilization 0.9
-
local_index: 1
master_index: 0
headless: true
env_extend:
server_cmd: >
vllm serve "Qwen/Qwen3-235B-A22B"
--headless

105
tests/e2e/nightly/multi_node/config/models/Qwen3-235B-W8A8.yaml Normal file
View File

@@ -0,0 +1,105 @@
test_name: "test Qwen3-235B-A22B-W8A8 disaggregated_prefill"
model: "vllm-ascend/Qwen3-235B-A22B-W8A8"
num_nodes: 2
npu_per_node: 16
env_common:
VLLM_USE_MODELSCOPE: true
OMP_PROC_BIND: false
OMP_NUM_THREADS: 100
HCCL_BUFFSIZE: 1024
SERVER_PORT: 8080
disaggregated_prefill:
enabled: true
prefiller_host_index: [0]
decoder_host_index: [1]
deployment:
-
server_cmd: >
vllm serve "vllm-ascend/Qwen3-235B-A22B-W8A8"
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 2
--data-parallel-size-local 2
--tensor-parallel-size 8
--seed 1024
--enable-expert-parallel
--max-num-seqs 16
--max-model-len 8192
--max-num-batched-tokens 8192
--quantization ascend
--trust-remote-code
--no-enable-prefix-caching
--gpu-memory-utilization 0.9
--kv-transfer-config
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 2,
"tp_size": 8
}
}
}'
-
server_cmd: >
vllm serve "vllm-ascend/Qwen3-235B-A22B-W8A8"
--host 0.0.0.0
--port $SERVER_PORT
--data-parallel-size 2
--data-parallel-size-local 2
--tensor-parallel-size 8
--seed 1024
--quantization ascend
--max-num-seqs 16
--max-model-len 8192
--max-num-batched-tokens 8192
--enable-expert-parallel
--trust-remote-code
--no-enable-prefix-caching
--gpu-memory-utilization 0.9
--kv-transfer-config
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_consumer",
"kv_port": "30200",
"engine_id": "1",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 2,
"tp_size": 8
}
}
}'
benchmarks:
perf:
case_type: performance
dataset_path: vllm-ascend/GSM8K-in3500-bs400
request_conf: vllm_api_stream_chat
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
num_prompts: 1
max_out_len: 2
batch_size: 1
baseline: 5
threshold: 0.97
acc:
case_type: accuracy
dataset_path: vllm-ascend/AIME2024
request_conf: vllm_api_general_chat
dataset_conf: aime2024/aime2024_gen_0_shot_chat_prompt
max_out_len: 10
batch_size: 32
baseline: 1
threshold: 1

139
tests/e2e/nightly/multi_node/config/multi_node_config.py
View File

@@ -1,6 +1,7 @@
import logging
import os
import subprocess
from dataclasses import dataclass
from typing import Optional
import regex as re
@@ -15,6 +16,16 @@ from tests.e2e.nightly.multi_node.config.utils import (get_avaliable_port,
setup_logger()
logger = logging.getLogger(__name__)
DISAGGREGATED_PREFILL_PROXY_SCRIPT = "examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py"
DISAGGEGATED_PREFILL_PORT = 5333
@dataclass
class NodeInfo:
index: int
ip: str
server_cmd: str
headless: bool
server_port: int
class MultiNodeConfig:
@@ -22,38 +33,50 @@ class MultiNodeConfig:
def __init__(self,
model: str,
test_name: str,
num_nodes: int = 2,
npu_per_node: int = 16,
server_port: int = 8080,
headless: bool = False,
disaggregated_prefill: Optional[dict] = None,
envs: Optional[dict] = None,
server_cmd: str = "",
nodes_info: Optional[list[NodeInfo]] = None,
perf_cmd: Optional[str] = None,
acc_cmd: Optional[str] = None):
self.test_name = test_name
self.model = model
self.num_nodes = num_nodes
self.nodes_info = nodes_info or []
self.num_nodes = len(self.nodes_info)
self.npu_per_node = npu_per_node
self.envs = envs if envs is not None else {}
self.server_port = server_port
if disaggregated_prefill:
self.proxy_port = get_avaliable_port()
self.headless = headless
self.server_cmd = server_cmd
self.envs = envs if envs is not None else {}
self.proxy_port = get_avaliable_port()
self.perf_cmd = perf_cmd
self.acc_cmd = acc_cmd
assert perf_cmd is not None, "perf_cmd must be provided"
assert acc_cmd is not None, "acc_cmd must be provided"
assert server_cmd is not None, "server_cmd must be provided"
self.cur_index = os.getenv("LWS_WORKER_INDEX", 0)
self.cur_index = int(os.getenv("LWS_WORKER_INDEX", 0))
self.cur_ip = get_cur_ip()
self.nic_name = get_net_interface(self.cur_ip)
self.cluster_ips = get_cluster_ips(num_nodes)
self.cluster_ips = get_cluster_ips(self.num_nodes)
self.cur_node_info: NodeInfo = self.nodes_info[self.cur_index]
self.disaggregated_prefill = disaggregated_prefill
self._init_disaggregated_prefill()
self._init_dist_env()
self.server_cmd = self._expand_env_vars(self.server_cmd, self.envs)
self.server_cmd = self._expand_env_vars(self.cur_node_info.server_cmd,
self.envs)
def _init_disaggregated_prefill(self):
if self.disaggregated_prefill:
decode_host_index = self.disaggregated_prefill.get(
"decoder_host_index")
if not decode_host_index:
raise RuntimeError("got empty decode_host_index")
self.decode_start_index: int = decode_host_index[0]
self.num_prefillers = self.decode_start_index
self.num_decoders = self.num_nodes - self.num_prefillers
if self.disaggregated_prefill.get(
"ranktable_gen_path") is not None:
self._gen_ranktable()
def _init_dist_env(self):
self.envs["HCCL_IF_IP"] = self.cur_ip
@@ -62,7 +85,17 @@ class MultiNodeConfig:
self.envs["HCCL_SOCKET_IFNAME"] = self.nic_name
self.envs["LOCAL_IP"] = self.cur_ip
self.envs["NIC_NAME"] = self.nic_name
self.envs["MASTER_IP"] = self.cluster_ips[0]
if self.disaggregated_prefill:
self.envs[
"DISAGGREGATED_PREFILL_RANK_TABLE_PATH"] = self.disaggregated_prefill.get(
"ranktable_path")
if self.cur_index < self.decode_start_index:
self.envs["MASTER_IP"] = self.cluster_ips[0]
else:
self.envs["MASTER_IP"] = self.cluster_ips[
self.decode_start_index]
ascend_path = "/usr/local/Ascend/ascend-toolkit/latest/python/site-packages"
self.envs[
"LD_LIBRARY_PATH"] = f"{ascend_path}:{self.envs.get('LD_LIBRARY_PATH', os.environ.get('LD_LIBRARY_PATH', ''))}"
@@ -172,15 +205,21 @@ class MultiNodeConfig:
deployments = config_data.get("deployment", [])
assert len(deployments) == num_nodes, \
f"Number of deployments ({len(deployments)}) must match num_nodes ({num_nodes})"
for deployment in deployments:
if deployment.get("local_index") == int(
os.getenv("LWS_WORKER_INDEX", 0)):
envs_extend = deployment.get("env_extend", {})
if envs_extend:
envs.update(envs_extend)
server_cmd = deployment.get("server_cmd")
headless = deployment.get("headless", False)
break
cluster_ips = get_cluster_ips(num_nodes)
nodes_info = []
for index, deployment in enumerate(deployments):
# after assert len(deployments) == num_nodes, we can assume that this will must have a match
server_cmd = deployment.get("server_cmd", "")
headless = "--headless" in server_cmd
nodes_info.append(
NodeInfo(ip=cluster_ips[index],
index=index,
headless=headless,
server_port=server_port,
server_cmd=server_cmd))
benchmarks = config_data.get("benchmarks", {})
assert benchmarks is not None, "benchmarks must be provided"
perf_cmd = benchmarks["perf"]
@@ -188,13 +227,11 @@ class MultiNodeConfig:
return cls(model=model,
test_name=test_name,
num_nodes=num_nodes,
npu_per_node=npu_per_node,
envs=envs,
server_port=server_port,
headless=headless,
disaggregated_prefill=disaggregated_prefill,
server_cmd=server_cmd,
nodes_info=nodes_info,
perf_cmd=perf_cmd,
acc_cmd=acc_cmd)
@@ -204,4 +241,52 @@ class MultiNodeConfig:
@property
def is_master(self):
return int(self.cur_index) == 0
return self.cur_index == 0
def _gen_ranktable(self):
cluster_ip = self.cluster_ips
assert len(cluster_ip) > 0
nnodes = self.num_nodes
node_rank = self.cur_index
master_addr = cluster_ip[0]
master_port = DISAGGEGATED_PREFILL_PORT
assert self.disaggregated_prefill is not None
ranktable_gen_path = self.disaggregated_prefill.get(
"ranktable_gen_path")
ranktable_path = self.disaggregated_prefill.get("ranktable_path")
assert ranktable_gen_path is not None and ranktable_path is not None
if os.path.exists(str(ranktable_path)):
return
local_host = self.cur_ip
cmd = [
"torchrun",
"--nproc_per_node",
"1",
"--nnodes",
str(nnodes),
"--node_rank",
str(node_rank),
"--master_addr",
master_addr,
"--master_port",
str(master_port),
ranktable_gen_path,
"--ranktable-path",
str(ranktable_path),
"--local-host",
local_host,
"--prefill-device-cnt",
str(self.npu_per_node * self.num_prefillers),
"--decode-device-cnt",
str(self.npu_per_node * self.num_decoders),
]
env = os.environ.copy()
assert self.nic_name is not None
env["GLOO_SOCKET_IFNAME"] = self.nic_name
subprocess.run(cmd, env=env, check=True)
assert os.path.exists(
str(ranktable_path)), "failed generate ranktable.json"

113
tests/e2e/nightly/multi_node/scripts/build_mooncake.sh Normal file
View File

@@ -0,0 +1,113 @@
#!/bin/bash
set -e
set -o pipefail
GREEN="\033[0;32m"
BLUE="\033[0;34m"
YELLOW="\033[0;33m"
RED="\033[0;31m"
NC="\033[0m" # No Color
branch=${1:-pooling_async_memecpy_v1}
point=${2:-9d96b2e1dd76cc601d76b1b4c5f6e04605cd81d3}
repo_url="https://github.com/AscendTransport/Mooncake"
repo_name="Mooncake"
state_file=".build_state"
echo "[INFO] Branch: $branch"
echo "[INFO] Commit: $point"
echo "-------------------------------------------"
mark_done() { echo "$1" >> "$state_file"; }
is_done() { grep -Fxq "$1" "$state_file" 2>/dev/null; }
if ! is_done "clone"; then
echo "[STEP] Clone repository..."
if [ -d "$repo_name" ]; then
echo "[WARN] Directory $repo_name already exists, skipping clone."
else
git clone -b "$branch" "$repo_url" "$repo_name"
fi
cd "$repo_name"
git fetch --all
git checkout "$point" || { echo "[ERROR] Checkout failed."; exit 1; }
cd ..
mark_done "clone"
else
echo "[SKIP] Clone step already done."
fi
if ! is_done "deps"; then
cd "$repo_name"
echo "[STEP]Installing dependencies (ignore Go failure)..."
yes | bash dependencies.sh || echo "⚠️ dependencies.sh failed (Go install likely failed), continuing..."
cd ..
mark_done "deps"
else
echo "[SKIP] Dependencies already installed."
fi
if ! is_done "mpi"; then
echo "[STEP] Install MPI..."
apt purge -y mpich libmpich-dev openmpi-bin libopenmpi-dev || true
apt install -y mpich libmpich-dev
export CPATH=/usr/lib/aarch64-linux-gnu/mpich/include/:${CPATH:-}
export CPATH=/usr/lib/aarch64-linux-gnu/openmpi/lib:${CPATH:-}
mark_done "mpi"
else
echo "[SKIP] MPI installation already done."
fi
if ! is_done "build"; then
echo "[STEP] Compile and install..."
cd "$repo_name"
if [ -d "build" ]; then
echo "[INFO] Removing existing build directory..."
rm -rf build
fi
mkdir build && cd build
cmake .. || { echo "[ERROR] cmake failed."; exit 1; }
make -j || { echo "[ERROR] make failed."; exit 1; }
make install || { echo "[ERROR] make install failed."; exit 1; }
mark_done "build"
else
echo "[SKIP] Build already done."
fi
if ! is_done "copy_lib"; then
echo "[STEP] Copy library files..."
cp mooncake-transfer-engine/src/transport/ascend_transport/hccl_transport/ascend_transport_c/libascend_transport_mem.so \
/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/
cp mooncake-transfer-engine/src/libtransfer_engine.so \
/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/
cd ..
mark_done "copy_lib"
else
echo "[SKIP] Library copy already done."
fi
if ! grep -q "export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH" ~/.bashrc; then
echo -e "${YELLOW}Adding LD_LIBRARY_PATH to your PATH in ~/.bashrc${NC}"
echo 'export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH' >> ~/.bashrc
echo -e "${YELLOW}Please run 'source ~/.bashrc' or start a new terminal${NC}"
fi
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH
echo "=========================================="
echo -e "${GREEN}[SUCCESS] Mooncake build completed!"
echo "You can rerun this script anytime — it will resume from the last step."
echo "=========================================="
echo "Example startup command:"
echo "mooncake_master --eviction_high_watermark_ratio 0.8 --eviction_ratio 0.05 --port 50088"

18
tests/e2e/nightly/multi_node/scripts/lws.yaml.jinja2
View File

@@ -17,19 +17,24 @@ spec:
- name: vllm-leader
image: {{ image | default("m.daocloud.io/quay.io/ascend/cann:8.2.rc1-a3-ubuntu22.04-py3.11") }}
env:
- name: CONFIG_YAML_PATH
value: {{ config_file_path | default("tests/e2e/nightly/multi_node/config/models/DeepSeek-V3.yaml") }}
- name: WORKSPACE
value: "/root/workspace"
# Set vLLM version and vLLM-Ascend version here, once there is a new release, update here.
- name: VLLM_VERSION
value: "v0.11.0"
- name: VLLM_ASCEND_VERSION
value: "main"
value: {{ vllm_ascend_ref | default("main") }}
- name: VLLM_ASCEND_REMOTE_URL
value: {{ vllm_ascend_remote_url | default("https://github.com/vllm-project/vllm-ascend.git") }}
- name: RESULT_FILE_PATH
value: {{ result_file_path | default("/root/.cache/tests/ret/test_result.txt") }}
command:
- sh
- -c
- |
bash /root/.cache/tests/run.sh
tail -f /dev/null
resources:
limits:
huawei.com/ascend-1980: "16"
@@ -70,19 +75,24 @@ spec:
- name: vllm-worker
image: {{ image | default("m.daocloud.io/quay.io/ascend/cann:8.2.rc1-a3-ubuntu22.04-py3.11") }}
env:
- name: CONFIG_YAML_PATH
value: {{ config_file_path | default("tests/e2e/nightly/multi_node/config/models/DeepSeek-V3.yaml") }}
- name: WORKSPACE
value: "/root/workspace"
# Set vLLM version and vLLM-Ascend version here, once there is a new release, update here.
- name: VLLM_VERSION
value: "v0.11.0"
- name: VLLM_ASCEND_VERSION
value: "main"
value: {{ vllm_ascend_ref | default("main") }}
- name: VLLM_ASCEND_REMOTE_URL
value: {{ vllm_ascend_remote_url | default("https://github.com/vllm-project/vllm-ascend.git") }}
- name: RESULT_FILE_PATH
value: {{ result_file_path | default("/root/.cache/tests/ret/test_result.txt") }}
command:
- sh
- -c
- |
bash /root/.cache/tests/run.sh
tail -f /dev/null
resources:
limits:
huawei.com/ascend-1980: "16"

174
tests/e2e/nightly/multi_node/scripts/run.sh
View File

@@ -1,7 +1,47 @@
#!/bin/bash
set -euo pipefail
export SRC_DIR="$WORKSPACE/source_code"
# Color definitions
GREEN="\033[0;32m"
BLUE="\033[0;34m"
YELLOW="\033[0;33m"
RED="\033[0;31m"
NC="\033[0m" # No Color
# Configuration
GOVER=1.23.8
LOG_DIR="/root/.cache/tests/logs"
OVERWRITE_LOGS=true
SRC_DIR="$WORKSPACE/source_code"
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH
# Function to print section headers
print_section() {
echo -e "\n${BLUE}=== $1 ===${NC}"
}
# Function to print success messages
print_success() {
echo -e "${GREEN}✓ $1${NC}"
}
# Function to print error messages and exit
print_error() {
echo -e "${RED}✗ ERROR: $1${NC}"
exit 1
}
# Function to check command success
check_success() {
if [ $? -ne 0 ]; then
print_error "$1"
fi
}
if [ $(id -u) -ne 0 ]; then
print_error "Require root permission, try sudo ./dependencies.sh"
fi
check_npu_info() {
echo "====> Check NPU info"
@@ -22,18 +62,13 @@ checkout_src() {
# vllm-ascend
if [ ! -d "$SRC_DIR/vllm-ascend" ]; then
git clone --depth 1 -b $VLLM_ASCEND_VERSION https://github.com/vllm-project/vllm-ascend.git "$SRC_DIR/vllm-ascend"
git clone --depth 1 -b $VLLM_ASCEND_VERSION $VLLM_ASCEND_REMOTE_URL "$SRC_DIR/vllm-ascend"
fi
# vllm
if [ ! -d "$SRC_DIR/vllm" ]; then
git clone -b $VLLM_VERSION https://github.com/vllm-project/vllm.git "$SRC_DIR/vllm"
fi
#mooncake
if [ ! -d "$SRC_DIR/Mooncake" ]; then
git clone -b pooling_async_memecpy_v1 https://github.com/AscendTransport/Mooncake "$SRC_DIR/Mooncake"
fi
}
install_sys_dependencies() {
@@ -57,28 +92,55 @@ install_vllm() {
pip install -r "$SRC_DIR/vllm-ascend/requirements-dev.txt"
}
install_mooncake() {
echo "====> Install mooncake"
apt-get update -y
apt-get install -y --no-install-recommends mpich libmpich-dev
cd $SRC_DIR/Mooncake
bash dependencies.sh --yes
apt purge mpich libmpich-dev -y
apt purge openmpi-bin -y
apt purge openmpi-bin libopenmpi-dev -y
apt install mpich libmpich-dev -y
export CPATH=/usr/lib/aarch64-linux-gnu/mpich/include/:$CPATH
export CPATH=/usr/lib/aarch64-linux-gnu/openmpi/lib:$CPATH
download_go() {
ARCH=$(uname -m)
GOVER=1.23.8
if [ "$ARCH" = "aarch64" ]; then
ARCH="arm64"
elif [ "$ARCH" = "x86_64" ]; then
ARCH="amd64"
else
echo "Unsupported architecture: $ARCH"
exit 1
fi
# Download Go
echo "Downloading Go $GOVER..."
wget -q --show-progress https://golang.google.cn/dl/go$GOVER.linux-$ARCH.tar.gz
check_success "Failed to download Go $GOVER"
mkdir build
cd -
cd $SRC_DIR/Mooncake/build
cmake ..
make -j
make install
cp mooncake-transfer-engine/src/transport/ascend_transport/hccl_transport/ascend_transport_c/libascend_transport_mem.so /usr/local/Ascend/ascend-toolkit/latest/python/site-packages/
cp mooncake-transfer-engine/src/libtransfer_engine.so /usr/local/Ascend/ascend-toolkit/latest/python/site-packages/
cd -
# Install Go
echo "Installing Go $GOVER..."
tar -C /usr/local -xzf go$GOVER.linux-$ARCH.tar.gz
check_success "Failed to install Go $GOVER"
# Clean up downloaded file
rm -f go$GOVER.linux-$ARCH.tar.gz
check_success "Failed to clean up Go installation file"
print_success "Go $GOVER installed successfully"
}
install_go() {
# Check if Go is already installed
if command -v go &> /dev/null; then
GO_VERSION=$(go version | awk '{print $3}')
if [[ "$GO_VERSION" == "go$GOVER" ]]; then
echo -e "${YELLOW}Go $GOVER is already installed. Skipping...${NC}"
else
echo -e "${YELLOW}Found Go $GO_VERSION. Will install Go $GOVER...${NC}"
download_go
fi
else
download_go
fi
# Add Go to PATH if not already there
if ! grep -q "export PATH=\$PATH:/usr/local/go/bin" ~/.bashrc; then
echo -e "${YELLOW}Adding Go to your PATH in ~/.bashrc${NC}"
echo 'export PATH=$PATH:/usr/local/go/bin' >> ~/.bashrc
echo -e "${YELLOW}Please run 'source ~/.bashrc' or start a new terminal to use Go${NC}"
fi
export PATH=$PATH:/usr/local/go/bin
}
kill_npu_processes() {
@@ -89,47 +151,14 @@ kill_npu_processes() {
}
run_tests() {
echo "====> Run tests"
shopt -s nullglob
declare -A results
local total=0
local passed=0
local failed=0
local REPORT_FILE="/root/.cache/test_summary.md"
echo "#Nightly Multi-node Test Summary" > "$REPORT_FILE"
echo "" >> "$REPORT_FILE"
echo "| Config File | Result |" >> "$REPORT_FILE"
echo "|--------------|---------|" >> "$REPORT_FILE"
for file in tests/e2e/nightly/multi_node/config/models/*.yaml; do
export CONFIG_YAML_PATH="$file"
echo "Running test with config: $CONFIG_YAML_PATH"
if pytest -sv tests/e2e/nightly/multi_node/test_multi_node.py; then
results["$file"]="✅ PASS"
((passed++))
else
results["$file"]="❌ FAIL"
((failed++))
fi
((total++))
echo "| \`$file\` | ${results[$file]} |" >> "$REPORT_FILE"
echo "------------------------------------------"
kill_npu_processes
done
shopt -u nullglob
echo "" >> "$REPORT_FILE"
echo "## Summary" >> "$REPORT_FILE"
echo "- **Total:** $total" >> "$REPORT_FILE"
echo "- **Passed:** $passed ✅" >> "$REPORT_FILE"
echo "- **Failed:** $failed ❌" >> "$REPORT_FILE"
echo
echo "✅ Markdown report written to: $REPORT_FILE"
pytest -sv tests/e2e/nightly/multi_node/test_multi_node.py
kill_npu_processes
ret=$?
if [ "$LWS_WORKER_INDEX" -eq 0 ]; then
mkdir -p "$(dirname "$RESULT_PATH")"
echo $ret > "$RESULT_PATH"
fi
return $ret
}
main() {
@@ -138,7 +167,12 @@ main() {
checkout_src
install_sys_dependencies
install_vllm
install_mooncake
# to speed up mooncake build process, install Go here
install_go
cd "$WORKSPACE/source_code"
. $SRC_DIR/vllm-ascend/tests/e2e/nightly/multi_node/scripts/build_mooncake.sh \
pooling_async_memecpy_v1 9d96b2e1dd76cc601d76b1b4c5f6e04605cd81d3
cd "$WORKSPACE/source_code/vllm-ascend"
run_tests
}

8
tests/e2e/nightly/multi_node/test_multi_node.py
View File

@@ -8,7 +8,10 @@ def test_multi_node() -> None:
env_dict = config.envs
# perf_cmd = config.perf_cmd
# acc_cmd = config.acc_cmd
server_port = config.server_port if not config.disaggregated_prefill else config.proxy_port
nodes_info = config.nodes_info
disaggregated_prefill = config.disaggregated_prefill
server_port = config.server_port
proxy_port = config.proxy_port
server_host = config.cluster_ips[0]
with config.launch_server_proxy(DISAGGREGATED_PREFILL_PROXY_SCRIPT):
with RemoteOpenAIServer(
@@ -18,6 +21,9 @@ def test_multi_node() -> None:
server_host=server_host,
env_dict=env_dict,
auto_port=False,
proxy_port=proxy_port,
disaggregated_prefill=disaggregated_prefill,
nodes_info=nodes_info,
max_wait_seconds=2000,
) as remote_server:
# base_url = remote_server.url_root