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[EPLB][CI] EPLB add aclgraph and redundant expert ci (#5625)

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### What this PR does / why we need it?
EPLB currently does not have CI related to aclgraph and redundancy
experts; this PR adds them.
release on #5529

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
Tested the use cases to be added in this PR.

PASSED

====================================================== warnings summary
==========================================================
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyPacked has no __module__ attribute

<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyObject has no __module__ attribute

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 1 passed, 2
warnings in 272.24s (0:04:32)
=====================================================

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
This commit is contained in:
LI SHENGYONG
2026-01-08 09:51:48 +08:00
committed by GitHub
parent 264cc254cc
commit b69db4ce55
1 changed files with 6 additions and 4 deletions
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10
tests/e2e/multicard/2-cards/test_qwen3_moe.py
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@@ -79,10 +79,11 @@ def test_qwen3_moe_distributed_aiv_tp2():
async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb(): async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
model = "vllm-ascend/Qwen3-30B-A3B-W8A8" model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
port = get_open_port() port = get_open_port()
compilation_config = json.dumps({"cudagraph_capture_sizes": [8]})
server_args = [ server_args = [
"--max_model_len", "8192", "--tensor_parallel_size", "2", "--max_model_len", "8192", "--tensor_parallel_size", "2",
"--enable_expert_parallel", "--quantization", "ascend", "--port", "--enable_expert_parallel", "--quantization", "ascend", "--port",
str(port), "--enforce_eager" str(port), "--compilation-config", compilation_config
] ]
env_dict = {"HCCL_BUFFSIZE": "1024"} env_dict = {"HCCL_BUFFSIZE": "1024"}
with RemoteOpenAIServer(model, with RemoteOpenAIServer(model,
@@ -93,7 +94,7 @@ async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
client = server.get_async_client() client = server.get_async_client()
batch = await client.completions.create(model=model, batch = await client.completions.create(model=model,
prompt="What is deeplearning?", prompt="What is deeplearning?",
max_tokens=300, max_tokens=400,
temperature=0, temperature=0,
top_p=1.0, top_p=1.0,
n=1) n=1)
@@ -106,7 +107,8 @@ async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
additional_config = { additional_config = {
"dynamic_eplb": True, "dynamic_eplb": True,
"num_iterations_eplb_update": 100, "num_iterations_eplb_update": 100,
"num_wait_worker_iterations": 20 "num_wait_worker_iterations": 20,
"num_redundant_experts": 2
} }
server_args.extend(["--additional-config", json.dumps(additional_config)]) server_args.extend(["--additional-config", json.dumps(additional_config)])
with RemoteOpenAIServer(model, with RemoteOpenAIServer(model,
@@ -117,7 +119,7 @@ async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
client = server.get_async_client() client = server.get_async_client()
batch = await client.completions.create(model=model, batch = await client.completions.create(model=model,
prompt="What is deeplearning?", prompt="What is deeplearning?",
max_tokens=300, max_tokens=400,
temperature=0, temperature=0,
top_p=1.0, top_p=1.0,
n=1) n=1)