Unverified Commit cac4c10e authored by ahao-anyscale's avatar ahao-anyscale Committed by GitHub
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[BUG] Make 'binary' default option for saving torch compile artifacts when...


[BUG] Make 'binary' default option for saving torch compile artifacts when using standalone_compile (#27616)
Signed-off-by: default avatarahao-anyscale <ahao@anyscale.com>
parent f7d2946e
......@@ -27,6 +27,8 @@ With all these factors taken into consideration, usually we can guarantee that t
A unique aspect of vLLM's `torch.compile` integration, is that we guarantee all the compilation finishes before we serve any requests. No requests will trigger new compilations. Otherwise, the engine would be blocked on that request, and the response time will have unexpected spikes.
By default, the cache saves compiled artifacts as binary files. If you would like to interact with the generated code for debugging purposes, set the field `compile_cache_save_format=unpacked` in the compilation config, or omit this and set the env variable `VLLM_COMPILE_CACHE_SAVE_FORMAT=unpacked`.
## Python Code Compilation
In the very verbose logs, we can see:
......
......@@ -51,7 +51,9 @@ def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
and hasattr(torch._inductor, "standalone_compile")
):
logger.debug("Using InductorStandaloneAdaptor")
return InductorStandaloneAdaptor()
return InductorStandaloneAdaptor(
compilation_config.compile_cache_save_format
)
else:
logger.debug("Using InductorAdaptor")
return InductorAdaptor()
......
......@@ -6,7 +6,7 @@ import hashlib
import os
from collections.abc import Callable
from contextlib import ExitStack
from typing import Any
from typing import Any, Literal
from unittest.mock import patch
import torch
......@@ -175,6 +175,9 @@ class InductorStandaloneAdaptor(CompilerInterface):
name = "inductor_standalone"
def __init__(self, save_format: Literal["binary", "unpacked"]):
self.save_format = save_format
def compute_hash(self, vllm_config: VllmConfig) -> str:
factors = get_inductor_factors()
hash_str = hashlib.md5(
......@@ -220,7 +223,7 @@ class InductorStandaloneAdaptor(CompilerInterface):
assert key is not None
path = os.path.join(self.cache_dir, key)
if not envs.VLLM_DISABLE_COMPILE_CACHE:
compiled_graph.save(path=path, format="unpacked")
compiled_graph.save(path=path, format=self.save_format)
compilation_counter.num_compiled_artifacts_saved += 1
return compiled_graph, (key, path)
......@@ -237,7 +240,7 @@ class InductorStandaloneAdaptor(CompilerInterface):
assert isinstance(handle[1], str)
path = handle[1]
inductor_compiled_graph = torch._inductor.CompiledArtifact.load(
path=path, format="unpacked"
path=path, format=self.save_format
)
from torch._inductor.compile_fx import graph_returns_tuple
......
......@@ -7,11 +7,12 @@ from collections import Counter
from collections.abc import Callable
from dataclasses import asdict, field
from pathlib import Path
from typing import TYPE_CHECKING, Any, ClassVar
from typing import TYPE_CHECKING, Any, ClassVar, Literal
from pydantic import TypeAdapter, field_validator
from pydantic.dataclasses import dataclass
import vllm.envs as envs
from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
from vllm.config.utils import config
from vllm.logger import init_logger
......@@ -208,6 +209,15 @@ class CompilationConfig:
"""The directory to store the compiled graph, to accelerate Inductor
compilation. By default, it will use model-related information to generate
a cache directory."""
compile_cache_save_format: Literal["binary", "unpacked"] = field(
default_factory=lambda: envs.VLLM_COMPILE_CACHE_SAVE_FORMAT
)
"""Format for saving torch compile cache:\n
- "binary": saves as binary file (multiprocess safe)\n
- "unpacked": saves as directory structure for inspection/debugging
(NOT multiprocess safe)\n
Defaults to `VLLM_COMPILE_CACHE_SAVE_FORMAT` if not specified.
"""
backend: str = ""
"""The backend for compilation. It needs to be a string:
......@@ -479,6 +489,7 @@ class CompilationConfig:
factors.append(self.inductor_compile_config)
factors.append(self.inductor_passes)
factors.append(self.pass_config.uuid())
factors.append(self.compile_cache_save_format)
return hashlib.sha256(str(factors).encode()).hexdigest()
def __repr__(self) -> str:
......@@ -520,6 +531,16 @@ class CompilationConfig:
return CUDAGraphMode[value.upper()]
return value
@field_validator("compile_cache_save_format")
@classmethod
def validate_compile_cache_save_format(cls, value: str) -> str:
if value not in ("binary", "unpacked"):
raise ValueError(
f"compile_cache_save_format must be 'binary' or 'unpacked', "
f"got: {value}"
)
return value
def __post_init__(self) -> None:
if self.level is not None:
logger.warning(
......
......@@ -218,6 +218,7 @@ if TYPE_CHECKING:
VLLM_USE_FBGEMM: bool = False
VLLM_GC_DEBUG: str = ""
VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
def get_default_cache_root():
......@@ -1442,6 +1443,15 @@ environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: os.getenv(
"VLLM_DISABLE_SHARED_EXPERTS_STREAM", False
),
# Format for saving torch.compile cache artifacts
# - "binary": saves as binary file
# Safe for multiple vllm serve processes accessing the same torch compile cache.
# - "unpacked": saves as directory structure (for inspection/debugging)
# NOT multiprocess safe - race conditions may occur with multiple processes.
# Allows viewing and setting breakpoints in Inductor's code output files.
"VLLM_COMPILE_CACHE_SAVE_FORMAT": env_with_choices(
"VLLM_COMPILE_CACHE_SAVE_FORMAT", "binary", ["binary", "unpacked"]
),
}
# --8<-- [end:env-vars-definition]
......
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