Files
sglang/python/sglang/multimodal_gen/test/test_utils.py

361 lines
11 KiB
Python

# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
import base64
import json
import os
import socket
import time
from pathlib import Path
import cv2
from PIL import Image
from sglang.multimodal_gen.runtime.utils.common import get_bool_env_var
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.runtime.utils.perf_logger import (
RequestPerfRecord,
get_diffusion_perf_log_dir,
)
logger = init_logger(__name__)
def is_image_url(image_path: str | Path | None) -> bool:
"""Check if image_path is a URL."""
if image_path is None:
return False
return isinstance(image_path, str) and (
image_path.startswith("http://") or image_path.startswith("https://")
)
def probe_port(host="127.0.0.1", port=30010, timeout=2.0) -> bool:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(timeout)
try:
s.connect((host, port))
return True
except OSError:
return False
def is_in_ci() -> bool:
return get_bool_env_var("SGLANG_IS_IN_CI")
def get_dynamic_server_port() -> int:
cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
if not cuda_devices:
cuda_devices = "0"
try:
first_device_id = int(cuda_devices.split(",")[0].strip()[0])
except (ValueError, IndexError):
first_device_id = 0
if is_in_ci():
base_port = 10000 + first_device_id * 2000
else:
base_port = 20000 + first_device_id * 1000
return base_port + 1000
def is_mp4(data: bytes) -> bool:
"""Check if data represents a valid MP4 file by magic bytes."""
if len(data) < 8:
return False
return data[4:8] == b"ftyp"
def is_jpeg(data: bytes) -> bool:
# JPEG files start with: FF D8 FF
return data.startswith(b"\xff\xd8\xff")
def is_png(data):
# PNG files start with: 89 50 4E 47 0D 0A 1A 0A
return data.startswith(b"\x89PNG\r\n\x1a\n")
def is_webp(data: bytes) -> bool:
# WebP files start with: RIFF....WEBP
return data[:4] == b"RIFF" and data[8:12] == b"WEBP"
def get_expected_image_format(
output_format: str | None = None,
background: str | None = None,
) -> str:
"""Infer expected image format based on request parameters.
Args:
output_format: The output_format parameter from the request (png/jpeg/webp/jpg)
background: The background parameter from the request (transparent/opaque/auto)
Returns:
Expected file extension: "jpg", "png", or "webp"
"""
fmt = (output_format or "").lower()
if fmt in {"png", "webp", "jpeg", "jpg"}:
return "jpg" if fmt == "jpeg" else fmt
if (background or "auto").lower() == "transparent":
return "png"
return "jpg" # Default
def wait_for_port(host="127.0.0.1", port=30010, deadline=300.0, interval=0.5):
end = time.time() + deadline
last_err = None
while time.time() < end:
if probe_port(host, port, timeout=interval):
return True
time.sleep(interval)
raise TimeoutError(f"Port {host}:{port} not ready. Last error: {last_err}")
def check_image_size(ut, image, width, height):
# check image size
ut.assertEqual(image.size, (width, height))
def get_perf_log_dir() -> Path:
"""Gets the performance log directory from the centralized sglang utility."""
log_dir_str = get_diffusion_perf_log_dir()
if not log_dir_str:
raise RuntimeError(
"Performance logging is disabled (SGLANG_PERF_LOG_DIR is empty), "
"but a test tried to access the log directory."
)
return Path(log_dir_str)
def _ensure_log_path(log_dir: Path) -> Path:
log_dir.mkdir(parents=True, exist_ok=True)
return log_dir / "performance.log"
def clear_perf_log(log_dir: Path) -> Path:
"""Delete the perf log file so tests can watch for fresh entries."""
log_path = _ensure_log_path(log_dir)
if log_path.exists():
log_path.unlink()
logger.info("[server-test] Monitoring perf log at %s", log_path.as_posix())
return log_path
def prepare_perf_log() -> tuple[Path, Path]:
"""Convenience helper to resolve and clear the perf log in one call."""
log_dir = get_perf_log_dir()
log_path = clear_perf_log(log_dir)
return log_dir, log_path
def read_perf_logs(log_path: Path) -> list[RequestPerfRecord]:
if not log_path.exists():
return []
records: list[RequestPerfRecord] = []
with log_path.open("r", encoding="utf-8") as fh:
for line in fh:
line = line.strip()
if not line:
continue
try:
record_dict = json.loads(line)
records.append(RequestPerfRecord(**record_dict))
except json.JSONDecodeError:
continue
return records
def wait_for_req_perf_record(
request_id: str,
log_path: Path,
timeout: float = 30.0,
) -> RequestPerfRecord | None:
"""
the stage metrics of this request should be in the performance_log file with {request-id}
"""
logger.info(f"Waiting for req perf record with request id: {request_id}")
deadline = time.time() + timeout
while time.time() < deadline:
records = read_perf_logs(log_path)
for record in records:
if record.request_id == request_id:
return record
time.sleep(0.5)
if os.environ.get("SGLANG_GEN_BASELINE", "0") == "1":
return None
logger.error(f"record: {records}")
raise AssertionError(f"Timeout waiting for stage metrics for request {request_id} ")
def validate_image(b64_json: str) -> None:
"""Decode and validate that image is PNG or JPEG."""
image_bytes = base64.b64decode(b64_json)
assert is_png(image_bytes) or is_jpeg(image_bytes), "Image must be PNG or JPEG"
def validate_video(b64_json: str) -> None:
"""Decode and validate that video is a valid format."""
video_bytes = base64.b64decode(b64_json)
is_webm = video_bytes[:4] == b"\x1a\x45\xdf\xa3"
assert is_mp4(video_bytes) or is_webm, "Video must be MP4 or WebM"
def validate_openai_video(video_bytes: bytes) -> None:
"""Validate that video is MP4 or WebM by magic bytes."""
is_webm = video_bytes.startswith(b"\x1a\x45\xdf\xa3")
assert is_mp4(video_bytes) or is_webm, "Video must be MP4 or WebM"
def validate_image_file(
file_path: str,
expected_filename: str,
expected_width: int | None = None,
expected_height: int | None = None,
output_format: str | None = None,
background: str | None = None,
) -> None:
"""Validate image output file: existence, extension, size, filename, format, dimensions."""
# Infer expected format from request parameters
expected_ext = get_expected_image_format(output_format, background)
# 1. File existence
assert os.path.exists(file_path), f"Image file does not exist: {file_path}"
# 2. Extension check
assert file_path.endswith(
f".{expected_ext}"
), f"Expected .{expected_ext} extension, got: {file_path}"
# 3. File size > 0
file_size = os.path.getsize(file_path)
assert file_size > 0, f"Image file is empty: {file_path}"
# 4. Filename validation
actual_filename = os.path.basename(file_path)
assert (
actual_filename == expected_filename
), f"Filename mismatch: expected '{expected_filename}', got '{actual_filename}'"
# 5. Image format validation (magic bytes check based on expected format)
with open(file_path, "rb") as f:
header = f.read(12) # Read enough bytes for webp detection
if expected_ext == "png":
assert is_png(header), f"File is not a valid PNG: {file_path}"
elif expected_ext == "jpg":
assert is_jpeg(header), f"File is not a valid JPEG: {file_path}"
elif expected_ext == "webp":
assert is_webp(header), f"File is not a valid WebP: {file_path}"
# 6. Image dimension validation (reuse PIL)
if expected_width is not None and expected_height is not None:
with Image.open(file_path) as img:
width, height = img.size
assert (
width == expected_width
), f"Width mismatch: expected {expected_width}, got {width}"
assert (
height == expected_height
), f"Height mismatch: expected {expected_height}, got {height}"
def _get_video_dimensions_from_metadata(
cap: cv2.VideoCapture,
) -> tuple[int, int] | None:
"""Get video dimensions from metadata properties.
Args:
cap: OpenCV VideoCapture object
Returns:
Tuple of (width, height) if successful, None if metadata is invalid
"""
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
if width == 0 or height == 0:
return None
return int(width), int(height)
def _get_video_dimensions_from_frame(cap: cv2.VideoCapture) -> tuple[int, int]:
"""Get video dimensions by reading the first frame.
Args:
cap: OpenCV VideoCapture object
Returns:
Tuple of (width, height)
"""
ret, frame = cap.read()
if not ret or frame is None:
raise ValueError("Unable to read video frame to get dimensions")
# frame.shape is (height, width, channels)
height, width = frame.shape[:2]
return int(width), int(height)
def get_video_dimensions(file_path: str) -> tuple[int, int]:
"""Get video dimensions (width, height) from a video file.
Tries to get dimensions from metadata first, falls back to reading first frame.
Returns:
Tuple of (width, height)
"""
cap = cv2.VideoCapture(file_path)
try:
# Try to get dimensions from metadata first
dimensions = _get_video_dimensions_from_metadata(cap)
if dimensions is not None:
return dimensions
# Fall back to reading first frame
return _get_video_dimensions_from_frame(cap)
finally:
cap.release()
def validate_video_file(
file_path: str,
expected_filename: str,
expected_width: int | None = None,
expected_height: int | None = None,
) -> None:
"""Validate video output file: existence, extension, size, filename, format, dimensions."""
# 1. File existence
assert os.path.exists(file_path), f"Video file does not exist: {file_path}"
# 2. Extension check
assert file_path.endswith(".mp4"), f"Expected .mp4 extension, got: {file_path}"
# 3. File size > 0
file_size = os.path.getsize(file_path)
assert file_size > 0, f"Video file is empty: {file_path}"
# 4. Filename validation
actual_filename = os.path.basename(file_path)
assert (
actual_filename == expected_filename
), f"Filename mismatch: expected '{expected_filename}', got '{actual_filename}'"
# 5. Video format validation (reuse is_mp4)
with open(file_path, "rb") as f:
header = f.read(32)
assert is_mp4(header), f"File is not a valid MP4: {file_path}"
# 6. Video dimension validation (using OpenCV)
if expected_width is not None and expected_height is not None:
actual_width, actual_height = get_video_dimensions(file_path)
assert (
actual_width == expected_width
), f"Video width mismatch: expected {expected_width}, got {actual_width}"
assert (
actual_height == expected_height
), f"Video height mismatch: expected {expected_height}, got {actual_height}"