Support FIPS enabled machines with MD5 hashing (#15299)

Signed-off-by: Matthew Vine <32849887+MattTheCuber@users.noreply.github.com>

tests/compile/piecewise/test_toy_llama.py

@@ -63,7 +63,8 @@ class LlamaConfig:
             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

vllm/compilation/backends.py

@@ -381,8 +381,8 @@ class VllmBackend:
                 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
@@ -390,7 +390,8 @@ class VllmBackend:
             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,

vllm/compilation/compiler_interface.py

@@ -139,7 +139,8 @@ class InductorAdaptor(CompilerInterface):
         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):

vllm/config.py

@@ -1111,7 +1111,8 @@ class CacheConfig:
         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__(
@@ -1243,7 +1244,8 @@ class TokenizerPoolConfig:
         # 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):
@@ -1354,7 +1356,8 @@ class LoadConfig:
         # 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):
@@ -1674,7 +1677,8 @@ class SchedulerConfig:
         # 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:
@@ -1810,7 +1814,8 @@ class DeviceConfig:
         # 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:
@@ -1983,7 +1988,8 @@ class SpeculativeConfig:
         # 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
 
     @classmethod
@@ -2358,7 +2364,8 @@ class LoRAConfig:
         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):
@@ -2424,7 +2431,8 @@ class PromptAdapterConfig:
         # 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):
@@ -2469,7 +2477,8 @@ class MultiModalConfig:
         # 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:
@@ -2535,7 +2544,8 @@ class PoolerConfig:
         # 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
@@ -2816,7 +2826,8 @@ class DecodingConfig:
         # 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):
@@ -2866,7 +2877,8 @@ class ObservabilityConfig:
         # 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):
@@ -2928,7 +2940,8 @@ class KVTransferConfig(BaseModel):
         # 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
@@ -3425,7 +3438,8 @@ class VllmConfig:
             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: