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[diffusion] CI: improve diffusion CI (#13562)

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Co-authored-by: Adarsh Shirawalmath <114558126+adarshxs@users.noreply.github.com>
This commit is contained in:
Mick
2025-11-20 10:54:13 +08:00
committed by GitHub
parent af6bcadcf7
commit 127d59cd2c
19 changed files with 782 additions and 414 deletions
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110
python/sglang/multimodal_gen/envs.py
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@@ -15,26 +15,26 @@ from packaging import version
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
SGL_DIFFUSION_RINGBUFFER_WARNING_INTERVAL: int = 60
SGL_DIFFUSION_NCCL_SO_PATH: str | None = None
SGLANG_DIFFUSION_RINGBUFFER_WARNING_INTERVAL: int = 60
SGLANG_DIFFUSION_NCCL_SO_PATH: str | None = None
LD_LIBRARY_PATH: str | None = None
LOCAL_RANK: int = 0
CUDA_VISIBLE_DEVICES: str | None = None
SGL_DIFFUSION_CACHE_ROOT: str = os.path.expanduser("~/.cache/sgl_diffusion")
SGL_DIFFUSION_CONFIG_ROOT: str = os.path.expanduser("~/.config/sgl_diffusion")
SGL_DIFFUSION_CONFIGURE_LOGGING: int = 1
SGL_DIFFUSION_LOGGING_LEVEL: str = "INFO"
SGL_DIFFUSION_LOGGING_PREFIX: str = ""
SGL_DIFFUSION_LOGGING_CONFIG_PATH: str | None = None
SGL_DIFFUSION_TRACE_FUNCTION: int = 0
SGL_DIFFUSION_WORKER_MULTIPROC_METHOD: str = "fork"
SGL_DIFFUSION_TARGET_DEVICE: str = "cuda"
SGLANG_DIFFUSION_CACHE_ROOT: str = os.path.expanduser("~/.cache/sgl_diffusion")
SGLANG_DIFFUSION_CONFIG_ROOT: str = os.path.expanduser("~/.config/sgl_diffusion")
SGLANG_DIFFUSION_CONFIGURE_LOGGING: int = 1
SGLANG_DIFFUSION_LOGGING_LEVEL: str = "INFO"
SGLANG_DIFFUSION_LOGGING_PREFIX: str = ""
SGLANG_DIFFUSION_LOGGING_CONFIG_PATH: str | None = None
SGLANG_DIFFUSION_TRACE_FUNCTION: int = 0
SGLANG_DIFFUSION_WORKER_MULTIPROC_METHOD: str = "fork"
SGLANG_DIFFUSION_TARGET_DEVICE: str = "cuda"
MAX_JOBS: str | None = None
NVCC_THREADS: str | None = None
CMAKE_BUILD_TYPE: str | None = None
VERBOSE: bool = False
SGL_DIFFUSION_SERVER_DEV_MODE: bool = False
SGL_DIFFUSION_STAGE_LOGGING: bool = False
SGLANG_DIFFUSION_SERVER_DEV_MODE: bool = False
SGLANG_DIFFUSION_STAGE_LOGGING: bool = False
def _is_hip():
@@ -165,8 +165,8 @@ environment_variables: dict[str, Callable[[], Any]] = {
# ================== Installation Time Env Vars ==================
# Target device of sgl-diffusion, supporting [cuda (by default),
# rocm, neuron, cpu, openvino]
"SGL_DIFFUSION_TARGET_DEVICE": lambda: os.getenv(
"SGL_DIFFUSION_TARGET_DEVICE", "cuda"
"SGLANG_DIFFUSION_TARGET_DEVICE": lambda: os.getenv(
"SGLANG_DIFFUSION_TARGET_DEVICE", "cuda"
),
# Maximum number of compilation jobs to run in parallel.
# By default this is the number of CPUs
@@ -176,10 +176,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
# If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
"NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
# If set, sgl_diffusion will use precompiled binaries (*.so)
"SGL_DIFFUSION_USE_PRECOMPILED": lambda: bool(
os.environ.get("SGL_DIFFUSION_USE_PRECOMPILED")
"SGLANG_DIFFUSION_USE_PRECOMPILED": lambda: bool(
os.environ.get("SGLANG_DIFFUSION_USE_PRECOMPILED")
)
or bool(os.environ.get("SGL_DIFFUSION_PRECOMPILED_WHEEL_LOCATION")),
or bool(os.environ.get("SGLANG_DIFFUSION_PRECOMPILED_WHEEL_LOCATION")),
# CMake build type
# If not set, defaults to "Debug" or "RelWithDebInfo"
# Available options: "Debug", "Release", "RelWithDebInfo"
@@ -191,36 +191,36 @@ environment_variables: dict[str, Callable[[], Any]] = {
# Note that this not only affects how sgl_diffusion finds its configuration files
# during runtime, but also affects how sgl_diffusion installs its configuration
# files during **installation**.
"SGL_DIFFUSION_CONFIG_ROOT": lambda: os.path.expanduser(
"SGLANG_DIFFUSION_CONFIG_ROOT": lambda: os.path.expanduser(
os.getenv(
"SGL_DIFFUSION_CONFIG_ROOT",
"SGLANG_DIFFUSION_CONFIG_ROOT",
os.path.join(get_default_config_root(), "sgl_diffusion"),
)
),
# ================== Runtime Env Vars ==================
# Root directory for FASTVIDEO cache files
# Defaults to `~/.cache/sgl_diffusion` unless `XDG_CACHE_HOME` is set
"SGL_DIFFUSION_CACHE_ROOT": lambda: os.path.expanduser(
"SGLANG_DIFFUSION_CACHE_ROOT": lambda: os.path.expanduser(
os.getenv(
"SGL_DIFFUSION_CACHE_ROOT",
"SGLANG_DIFFUSION_CACHE_ROOT",
os.path.join(get_default_cache_root(), "sgl_diffusion"),
)
),
# Interval in seconds to log a warning message when the ring buffer is full
"SGL_DIFFUSION_RINGBUFFER_WARNING_INTERVAL": lambda: int(
os.environ.get("SGL_DIFFUSION_RINGBUFFER_WARNING_INTERVAL", "60")
"SGLANG_DIFFUSION_RINGBUFFER_WARNING_INTERVAL": lambda: int(
os.environ.get("SGLANG_DIFFUSION_RINGBUFFER_WARNING_INTERVAL", "60")
),
# Path to the NCCL library file. It is needed because nccl>=2.19 brought
# by PyTorch contains a bug: https://github.com/NVIDIA/nccl/issues/1234
"SGL_DIFFUSION_NCCL_SO_PATH": lambda: os.environ.get(
"SGL_DIFFUSION_NCCL_SO_PATH", None
"SGLANG_DIFFUSION_NCCL_SO_PATH": lambda: os.environ.get(
"SGLANG_DIFFUSION_NCCL_SO_PATH", None
),
# when `SGL_DIFFUSION_NCCL_SO_PATH` is not set, sgl_diffusion will try to find the nccl
# when `SGLANG_DIFFUSION_NCCL_SO_PATH` is not set, sgl_diffusion will try to find the nccl
# library file in the locations specified by `LD_LIBRARY_PATH`
"LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
# Internal flag to enable Dynamo fullgraph capture
"SGL_DIFFUSION_TEST_DYNAMO_FULLGRAPH_CAPTURE": lambda: bool(
os.environ.get("SGL_DIFFUSION_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1") != "0"
"SGLANG_DIFFUSION_TEST_DYNAMO_FULLGRAPH_CAPTURE": lambda: bool(
os.environ.get("SGLANG_DIFFUSION_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1") != "0"
),
# local rank of the process in the distributed setting, used to determine
# the GPU device id
@@ -228,62 +228,62 @@ environment_variables: dict[str, Callable[[], Any]] = {
# used to control the visible devices in the distributed setting
"CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
# timeout for each iteration in the engine
"SGL_DIFFUSION_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
os.environ.get("SGL_DIFFUSION_ENGINE_ITERATION_TIMEOUT_S", "60")
"SGLANG_DIFFUSION_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
os.environ.get("SGLANG_DIFFUSION_ENGINE_ITERATION_TIMEOUT_S", "60")
),
# Logging configuration
# If set to 0, sgl_diffusion will not configure logging
# If set to 1, sgl_diffusion will configure logging using the default configuration
# or the configuration file specified by SGL_DIFFUSION_LOGGING_CONFIG_PATH
"SGL_DIFFUSION_CONFIGURE_LOGGING": lambda: int(
os.getenv("SGL_DIFFUSION_CONFIGURE_LOGGING", "1")
# or the configuration file specified by SGLANG_DIFFUSION_LOGGING_CONFIG_PATH
"SGLANG_DIFFUSION_CONFIGURE_LOGGING": lambda: int(
os.getenv("SGLANG_DIFFUSION_CONFIGURE_LOGGING", "1")
),
"SGL_DIFFUSION_LOGGING_CONFIG_PATH": lambda: os.getenv(
"SGL_DIFFUSION_LOGGING_CONFIG_PATH"
"SGLANG_DIFFUSION_LOGGING_CONFIG_PATH": lambda: os.getenv(
"SGLANG_DIFFUSION_LOGGING_CONFIG_PATH"
),
# this is used for configuring the default logging level
"SGL_DIFFUSION_LOGGING_LEVEL": lambda: os.getenv(
"SGL_DIFFUSION_LOGGING_LEVEL", "INFO"
"SGLANG_DIFFUSION_LOGGING_LEVEL": lambda: os.getenv(
"SGLANG_DIFFUSION_LOGGING_LEVEL", "INFO"
),
# if set, SGL_DIFFUSION_LOGGING_PREFIX will be prepended to all log messages
"SGL_DIFFUSION_LOGGING_PREFIX": lambda: os.getenv(
"SGL_DIFFUSION_LOGGING_PREFIX", ""
# if set, SGLANG_DIFFUSION_LOGGING_PREFIX will be prepended to all log messages
"SGLANG_DIFFUSION_LOGGING_PREFIX": lambda: os.getenv(
"SGLANG_DIFFUSION_LOGGING_PREFIX", ""
),
# Trace function calls
# If set to 1, sgl_diffusion will trace function calls
# Useful for debugging
"SGL_DIFFUSION_TRACE_FUNCTION": lambda: int(
os.getenv("SGL_DIFFUSION_TRACE_FUNCTION", "0")
"SGLANG_DIFFUSION_TRACE_FUNCTION": lambda: int(
os.getenv("SGLANG_DIFFUSION_TRACE_FUNCTION", "0")
),
# Path to the attention configuration file. Only used for sliding tile
# attention for now.
"SGL_DIFFUSION_ATTENTION_CONFIG": lambda: (
"SGLANG_DIFFUSION_ATTENTION_CONFIG": lambda: (
None
if os.getenv("SGL_DIFFUSION_ATTENTION_CONFIG", None) is None
else os.path.expanduser(os.getenv("SGL_DIFFUSION_ATTENTION_CONFIG", "."))
if os.getenv("SGLANG_DIFFUSION_ATTENTION_CONFIG", None) is None
else os.path.expanduser(os.getenv("SGLANG_DIFFUSION_ATTENTION_CONFIG", "."))
),
# Use dedicated multiprocess context for workers.
# Both spawn and fork work
"SGL_DIFFUSION_WORKER_MULTIPROC_METHOD": lambda: os.getenv(
"SGL_DIFFUSION_WORKER_MULTIPROC_METHOD", "fork"
"SGLANG_DIFFUSION_WORKER_MULTIPROC_METHOD": lambda: os.getenv(
"SGLANG_DIFFUSION_WORKER_MULTIPROC_METHOD", "fork"
),
# Enables torch profiler if set. Path to the directory where torch profiler
# traces are saved. Note that it must be an absolute path.
"SGL_DIFFUSION_TORCH_PROFILER_DIR": lambda: (
"SGLANG_DIFFUSION_TORCH_PROFILER_DIR": lambda: (
None
if os.getenv("SGL_DIFFUSION_TORCH_PROFILER_DIR", None) is None
else os.path.expanduser(os.getenv("SGL_DIFFUSION_TORCH_PROFILER_DIR", "."))
if os.getenv("SGLANG_DIFFUSION_TORCH_PROFILER_DIR", None) is None
else os.path.expanduser(os.getenv("SGLANG_DIFFUSION_TORCH_PROFILER_DIR", "."))
),
# If set, sgl_diffusion will run in development mode, which will enable
# some additional endpoints for developing and debugging,
# e.g. `/reset_prefix_cache`
"SGL_DIFFUSION_SERVER_DEV_MODE": lambda: bool(
int(os.getenv("SGL_DIFFUSION_SERVER_DEV_MODE", "0"))
"SGLANG_DIFFUSION_SERVER_DEV_MODE": lambda: bool(
int(os.getenv("SGLANG_DIFFUSION_SERVER_DEV_MODE", "0"))
),
# If set, sgl_diffusion will enable stage logging, which will print the time
# taken for each stage
"SGL_DIFFUSION_STAGE_LOGGING": lambda: bool(
int(os.getenv("SGL_DIFFUSION_STAGE_LOGGING", "0"))
"SGLANG_DIFFUSION_STAGE_LOGGING": lambda: bool(
int(os.getenv("SGLANG_DIFFUSION_STAGE_LOGGING", "0"))
),
}