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[Cleanup] Refactor profiling env vars into a CLI config (#29912)

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Signed-off-by: Benjamin Chislett <bchislett@nvidia.com>
Signed-off-by: Benjamin Chislett <chislett.ben@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Benjamin Chislett
2025-12-09 13:29:33 -05:00
committed by GitHub
parent d471b2aff0
commit e858bfe051
22 changed files with 433 additions and 252 deletions
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110
vllm/envs.py
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@@ -89,20 +89,23 @@ if TYPE_CHECKING:
VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5 # seconds
VLLM_PLUGINS: list[str] | None = None
VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
VLLM_TORCH_CUDA_PROFILE: bool = False
# Deprecated env variables for profiling, kept for backward compatibility
# See also vllm/config/profiler.py and `--profiler-config` argument
VLLM_TORCH_CUDA_PROFILE: str | None = None
VLLM_TORCH_PROFILER_DIR: str | None = None
VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: bool = False
VLLM_TORCH_PROFILER_RECORD_SHAPES: str | None = None
VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: str | None = None
VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: str | None = None
VLLM_TORCH_PROFILER_WITH_STACK: str | None = None
VLLM_TORCH_PROFILER_WITH_FLOPS: str | None = None
VLLM_TORCH_PROFILER_USE_GZIP: str | None = None
VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: str | None = None
VLLM_PROFILER_DELAY_ITERS: str | None = None
VLLM_PROFILER_MAX_ITERS: str | None = None
# End of deprecated env variables for profiling
VLLM_USE_AOT_COMPILE: bool = False
VLLM_USE_BYTECODE_HOOK: bool = False
VLLM_FORCE_AOT_LOAD: bool = False
VLLM_TORCH_PROFILER_WITH_STACK: bool = True
VLLM_TORCH_PROFILER_WITH_FLOPS: bool = False
VLLM_PROFILER_DELAY_ITERS: int = 0
VLLM_PROFILER_MAX_ITERS: int = 0
VLLM_TORCH_PROFILER_USE_GZIP: bool = True
VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: bool = True
VLLM_USE_TRITON_AWQ: bool = False
VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
VLLM_SKIP_P2P_CHECK: bool = False
@@ -850,71 +853,52 @@ environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
"VLLM_LORA_RESOLVER_CACHE_DIR", None
),
# Enables torch CUDA profiling if set.
# On NVIDIA GPUs, this will start/stop cudaProfilerApi when triggered.
"VLLM_TORCH_CUDA_PROFILE": lambda: bool(
os.getenv("VLLM_TORCH_CUDA_PROFILE", "0") != "0"
),
# Enables torch CUDA profiling if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_CUDA_PROFILE": lambda: os.getenv("VLLM_TORCH_CUDA_PROFILE"),
# Enables torch profiler if set.
# Both AsyncLLM's CPU traces as well as workers'
# traces (CPU & GPU) will be saved under this directory.
# Note that it must be an absolute path.
"VLLM_TORCH_PROFILER_DIR": lambda: (
None
if (val := os.getenv("VLLM_TORCH_PROFILER_DIR")) is None
else (
val
if val.startswith("gs://") and val[5:] and val[5] != "/"
else os.path.abspath(os.path.expanduser(val))
)
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_DIR": lambda: os.getenv("VLLM_TORCH_PROFILER_DIR"),
# Enable torch profiler to record shapes if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: (
os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES")
),
# Enable torch profiler to record shapes if set
# VLLM_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
# not record shapes.
"VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"
# Enable torch profiler to profile memory if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: (
os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY")
),
# Enable torch profiler to profile memory if set
# VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
# will not profile memory.
"VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"
# Enable torch profiler to profile stack if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_WITH_STACK": lambda: (
os.getenv("VLLM_TORCH_PROFILER_WITH_STACK")
),
# Enable torch profiler to profile stack if set
# VLLM_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
# profile stack by default.
"VLLM_TORCH_PROFILER_WITH_STACK": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"
# Enable torch profiler to profile flops if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: (
os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS")
),
# Enable torch profiler to profile flops if set
# VLLM_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
# not profile flops.
"VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"
),
# Disable torch profiling of the AsyncLLMEngine process.
# If set to 1, will not profile the engine process.
"VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM", "0") != "0"
# Disable torch profiling of the AsyncLLMEngine process if set to 1.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: (
os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM")
),
# Delay number of iterations before starting profiling when using
# the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
"VLLM_PROFILER_DELAY_ITERS": lambda: int(
os.getenv("VLLM_PROFILER_DELAY_ITERS", "0")
),
# Deprecated, see profiler_config.
"VLLM_PROFILER_DELAY_ITERS": lambda: (os.getenv("VLLM_PROFILER_DELAY_ITERS")),
# Maximum number of iterations to profile when using the torch/torch CUDA profiler.
# If set to 0, will not limit the number of iterations.
"VLLM_PROFILER_MAX_ITERS": lambda: int(os.getenv("VLLM_PROFILER_MAX_ITERS", "0")),
"VLLM_PROFILER_MAX_ITERS": lambda: os.getenv("VLLM_PROFILER_MAX_ITERS"),
# Control whether torch profiler gzip-compresses profiling files.
# Set VLLM_TORCH_PROFILER_USE_GZIP=0 to disable gzip (enabled by default).
"VLLM_TORCH_PROFILER_USE_GZIP": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_USE_GZIP", "1") != "0"
),
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_USE_GZIP": lambda: os.getenv("VLLM_TORCH_PROFILER_USE_GZIP"),
# Control whether torch profiler dumps the self_cuda_time_total table.
# Set VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL=0 to disable dumping
# (enabled by default).
"VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: bool(
os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL", "1") != "0"
# Set to 0 to disable dumping the table.
# Deprecated, see profiler_config.
"VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: (
os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL")
),
# If set, vLLM will use Triton implementations of AWQ.
"VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),