refactor(e2e_test): fix smg ci e2e test code quality (#16664)

This commit is contained in:
Simo Lin
2026-01-07 06:59:49 -08:00
committed by GitHub
parent 7fc12e0bfa
commit 55b7936582
11 changed files with 100 additions and 91 deletions
@@ -17,6 +17,8 @@ from .constants import ( # Enums; Convenience sets; Fixture parameters; Default
HEALTH_CHECK_INTERVAL,
LOCAL_MODES,
LOCAL_RUNTIMES,
LOG_SEPARATOR_WIDTH,
MAX_RETRY_ATTEMPTS,
PARAM_BACKEND_ROUTER,
PARAM_MODEL,
PARAM_SETUP_BACKEND,
@@ -82,6 +84,8 @@ __all__ = [
"DEFAULT_STARTUP_TIMEOUT",
"DEFAULT_ROUTER_TIMEOUT",
"HEALTH_CHECK_INTERVAL",
"MAX_RETRY_ATTEMPTS",
"LOG_SEPARATOR_WIDTH",
# Env vars
"ENV_MODELS",
"ENV_BACKENDS",
@@ -58,3 +58,11 @@ DEFAULT_HOST = "127.0.0.1"
DEFAULT_STARTUP_TIMEOUT = 300
DEFAULT_ROUTER_TIMEOUT = 60
HEALTH_CHECK_INTERVAL = 5
# Retry configuration
MAX_RETRY_ATTEMPTS = (
6 # Max retries with exponential backoff (total ~63s: 1+2+4+8+16+32)
)
# Display formatting
LOG_SEPARATOR_WIDTH = 60 # Width for log separator lines (e.g., "="*60)
+28 -35
View File
@@ -328,6 +328,30 @@ class Gateway:
# Worker Management APIs
# -------------------------------------------------------------------------
def _worker_from_api_response(self, w: dict) -> WorkerInfo:
"""Convert API response dict to WorkerInfo.
Args:
w: Worker dict from API response.
Returns:
WorkerInfo object.
"""
status = "healthy" if w.get("is_healthy", False) else "unhealthy"
return WorkerInfo(
id=w.get("id", ""),
url=w.get("url", ""),
model=w.get("model_id"),
status=status,
pending_requests=w.get("load", 0),
metadata={
"worker_type": w.get("worker_type"),
"connection_mode": w.get("connection_mode"),
"priority": w.get("priority"),
"cost": w.get("cost"),
},
)
def list_workers(self, timeout: float = 5.0) -> list[WorkerInfo]:
"""List all workers connected to the gateway.
@@ -338,26 +362,9 @@ class Gateway:
resp = httpx.get(f"{self.base_url}/workers", timeout=timeout)
if resp.status_code == 200:
data = resp.json()
workers = []
for w in data.get("workers", []):
# Map API fields to WorkerInfo
status = "healthy" if w.get("is_healthy", False) else "unhealthy"
workers.append(
WorkerInfo(
id=w.get("id", ""),
url=w.get("url", ""),
model=w.get("model_id"),
status=status,
pending_requests=w.get("load", 0),
metadata={
"worker_type": w.get("worker_type"),
"connection_mode": w.get("connection_mode"),
"priority": w.get("priority"),
"cost": w.get("cost"),
},
)
)
return workers
return [
self._worker_from_api_response(w) for w in data.get("workers", [])
]
return []
except (httpx.RequestError, httpx.TimeoutException):
return []
@@ -374,21 +381,7 @@ class Gateway:
try:
resp = httpx.get(f"{self.base_url}/workers/{worker_id}", timeout=timeout)
if resp.status_code == 200:
w = resp.json()
status = "healthy" if w.get("is_healthy", False) else "unhealthy"
return WorkerInfo(
id=w.get("id", ""),
url=w.get("url", ""),
model=w.get("model_id"),
status=status,
pending_requests=w.get("load", 0),
metadata={
"worker_type": w.get("worker_type"),
"connection_mode": w.get("connection_mode"),
"priority": w.get("priority"),
"cost": w.get("cost"),
},
)
return self._worker_from_api_response(resp.json())
return None
except (httpx.RequestError, httpx.TimeoutException):
return None
@@ -227,7 +227,7 @@ class GPUAllocator:
name = pynvml.nvmlDeviceGetName(handle)
# Handle bytes vs string return type (varies by pynvml version)
if isinstance(name, bytes):
name = name.decode("utf-8")
name = name.decode("utf-8", errors="replace")
mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
# Convert bytes to MB
memory_mb = mem_info.total // (1024 * 1024)
+6 -22
View File
@@ -305,9 +305,6 @@ class ModelPool:
if ib_device:
logger.info("Detected InfiniBand device: %s", ib_device)
# Track bootstrap ports for PD groups (all PD workers of same model/mode share one)
pd_bootstrap_ports: dict[tuple[str, ConnectionMode], int] = {}
deferred: list[str] = []
# Process requirements in order - all workers treated uniformly
@@ -340,13 +337,8 @@ class ModelPool:
deferred.append(str(identity))
continue
# Get bootstrap port for PD workers (shared within model/mode group)
bootstrap_port = None
if identity.is_prefill or identity.is_decode:
pd_key = (identity.model_id, identity.mode)
if pd_key not in pd_bootstrap_ports:
pd_bootstrap_ports[pd_key] = get_open_port()
bootstrap_port = pd_bootstrap_ports[pd_key]
# Each prefill worker needs its own bootstrap port for PD communication
bootstrap_port = get_open_port() if identity.is_prefill else None
# Launch the worker
self._launch_model(
@@ -354,7 +346,7 @@ class ModelPool:
mode=identity.mode,
gpu_slot=slots[0],
worker_type=identity.worker_type,
bootstrap_port=bootstrap_port if identity.is_prefill else None,
bootstrap_port=bootstrap_port,
ib_device=(
ib_device if (identity.is_prefill or identity.is_decode) else None
),
@@ -888,25 +880,17 @@ class ModelPool:
has_pd = any(w.is_prefill or w.is_decode for w in valid_workers)
ib_device = detect_ib_device() if has_pd else None
# Track bootstrap ports for PD groups (shared within model/mode)
pd_bootstrap_ports: dict[tuple[str, ConnectionMode], int] = {}
instances: list[ModelInstance] = []
for w in valid_workers:
# Get bootstrap port for PD workers
bootstrap_port = None
if w.is_prefill or w.is_decode:
pd_key = (w.model_id, w.mode)
if pd_key not in pd_bootstrap_ports:
pd_bootstrap_ports[pd_key] = get_open_port()
bootstrap_port = pd_bootstrap_ports[pd_key]
# Each prefill worker needs its own bootstrap port for PD communication
bootstrap_port = get_open_port() if w.is_prefill else None
instance = self._launch_model(
model_id=w.model_id,
mode=w.mode,
gpu_slot=slot_map.get(w.key),
worker_type=w.worker_type,
bootstrap_port=bootstrap_port if w.is_prefill else None,
bootstrap_port=bootstrap_port,
ib_device=ib_device if (w.is_prefill or w.is_decode) else None,
instance_key=w.key,
)
+1 -3
View File
@@ -30,12 +30,10 @@ if TYPE_CHECKING:
from .simple_eval_common import Eval
from .simple_eval_common import ChatCompletionSampler, set_ulimit
from .simple_eval_mmlu import MMLU_DATASET_URL
logger = logging.getLogger(__name__)
# MMLU dataset URL
MMLU_DATASET_URL = "https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv"
@dataclass
class EvalConfig:
@@ -20,6 +20,8 @@ import requests
from openai import OpenAI
from tqdm import tqdm
from .constants import MAX_RETRY_ATTEMPTS
logger = logging.getLogger(__name__)
OPENAI_SYSTEM_MESSAGE_API = "You are a helpful assistant."
@@ -119,7 +121,6 @@ class ChatCompletionSampler(SamplerBase):
image: str,
encoding: str = "base64",
format: str = "png",
fovea: int = 768,
):
new_image = {
"type": "image_url",
@@ -141,7 +142,7 @@ class ChatCompletionSampler(SamplerBase):
self._pack_message("system", self.system_message)
] + message_list
trial = 0
while trial < 6: # Max 63 seconds backoff (1+2+4+8+16+32)
while trial < MAX_RETRY_ATTEMPTS:
try:
response = self.client.chat.completions.create(
model=self.model,
@@ -162,14 +163,15 @@ class ChatCompletionSampler(SamplerBase):
log_fn(
"Request failed (retry %d/%d, backoff %ds): %s",
trial + 1,
6,
MAX_RETRY_ATTEMPTS,
exception_backoff,
e,
)
time.sleep(exception_backoff)
trial += 1
logger.warning(
"All retry attempts exhausted after 6 retries, returning empty response"
"All retry attempts exhausted after %d retries, returning empty response",
MAX_RETRY_ATTEMPTS,
)
return ""
@@ -25,6 +25,9 @@ from .simple_eval_common import (
if TYPE_CHECKING:
from .simple_eval_common import SamplerBase
# MMLU dataset URL (hosted by OpenAI)
MMLU_DATASET_URL = "https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv"
SUBJECT_TO_CATEGORY = {
"abstract_algebra": "stem",
"anatomy": "other",