Skip to content

Support FIPS enabled machines with MD5 hashing #15299

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Mar 27, 2025
Merged

Support FIPS enabled machines with MD5 hashing #15299

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
fe26018
fix: support fips enabled machines with md5 hashing
MattTheCuber Mar 21, 2025
3305fad
chore: fix formatting
MattTheCuber Mar 21, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion tests/compile/piecewise/test_toy_llama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,8 @@ def compute_hash(self) -> str:
factors.append((k, v))
factors.sort()
import hashlib
return hashlib.md5(str(factors).encode()).hexdigest()
return hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()

def __post_init__(self):
assert self.mlp_size >= self.hidden_size
Expand Down
7 changes: 4 additions & 3 deletions vllm/compilation/backends.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -376,16 +376,17 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable:
with open(filepath) as f:
hash_content.append(f.read())
import hashlib
code_hash = hashlib.md5(
"\n".join(hash_content).encode()).hexdigest()
code_hash = hashlib.md5("\n".join(hash_content).encode(),
usedforsecurity=False).hexdigest()
factors.append(code_hash)

# 3. compiler hash
compiler_hash = self.compiler_manager.compute_hash(vllm_config)
factors.append(compiler_hash)

# combine all factors to generate the cache dir
hash_key = hashlib.md5(str(factors).encode()).hexdigest()[:10]
hash_key = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()[:10]

cache_dir = os.path.join(
envs.VLLM_CACHE_ROOT,
Expand Down
3 changes: 2 additions & 1 deletion vllm/compilation/compiler_interface.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -139,7 +139,8 @@ def compute_hash(self, vllm_config: VllmConfig) -> str:
from torch._inductor.codecache import torch_key
torch_factors = torch_key()
factors.append(torch_factors)
hash_str = hashlib.md5(str(factors).encode()).hexdigest()[:10]
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()[:10]
return hash_str

def initialize_cache(self, cache_dir: str, disable_cache: bool = False):
Expand Down
42 changes: 28 additions & 14 deletions vllm/config.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1093,7 +1093,8 @@ def compute_hash(self) -> str:
factors: list[Any] = []
factors.append(self.cache_dtype)
# `cpu_offload_gb` does not use `torch.compile` yet.
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __init__(
Expand Down Expand Up @@ -1220,7 +1221,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -1324,7 +1326,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -1644,7 +1647,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self) -> None:
Expand Down Expand Up @@ -1780,7 +1784,8 @@ def compute_hash(self) -> str:
# the device/platform information will be summarized
# by torch/vllm automatically.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __init__(self, device: str = "auto") -> None:
Expand Down Expand Up @@ -1826,7 +1831,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# spec decode does not use `torch.compile` yet.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

@staticmethod
Expand Down Expand Up @@ -2311,7 +2317,8 @@ def compute_hash(self) -> str:
factors.append(self.lora_extra_vocab_size)
factors.append(self.long_lora_scaling_factors)
factors.append(self.bias_enabled)
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -2383,7 +2390,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -2428,7 +2436,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def get_limit_per_prompt(self, modality: str) -> int:
Expand Down Expand Up @@ -2494,7 +2503,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

@staticmethod
Expand Down Expand Up @@ -2775,7 +2785,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -2820,7 +2831,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

def __post_init__(self):
Expand Down Expand Up @@ -2882,7 +2894,8 @@ def compute_hash(self) -> str:
# no factors to consider.
# this config will not affect the computation graph.
factors: list[Any] = []
hash_str = hashlib.md5(str(factors).encode()).hexdigest()
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()
return hash_str

@classmethod
Expand Down Expand Up @@ -3379,7 +3392,8 @@ def compute_hash(self) -> str:
vllm_factors.append("None")
factors.append(vllm_factors)

hash_str = hashlib.md5(str(factors).encode()).hexdigest()[:10]
hash_str = hashlib.md5(str(factors).encode(),
usedforsecurity=False).hexdigest()[:10]
return hash_str

def pad_for_cudagraph(self, batch_size: int) -> int:
Expand Down