[TRTLLM-8817][chore] Set default value of KvCacheConfig.free_gpu_memory_fraction explicitly

tensorrt_llm/_torch/pyexecutor/_util.py

@@ -101,12 +101,6 @@ def __init__(
         self._kv_cache_manager_cls = get_kv_cache_manager_cls(
             model_engine.model.model_config)
 
-    def _get_free_gpu_memory_fraction(self) -> float:
-        fraction = self._kv_cache_config.free_gpu_memory_fraction
-        if fraction is None:
-            fraction = 0.9
-        return fraction
-
     def _get_kv_size_per_token(self):
         model_config = self._model_engine.model.model_config
         mapping = self._mapping
@@ -299,7 +293,7 @@ def configure_kv_cache_capacity(self, py_executor: PyExecutor) -> None:
         # TODO: support CP by generating dummy requests for it.
         assert 'cp_type' not in mapping.cp_config
 
-        fraction = self._get_free_gpu_memory_fraction()
+        fraction = self._kv_cache_config.free_gpu_memory_fraction
 
         torch.cuda.empty_cache()
         torch.cuda.reset_peak_memory_stats()

tensorrt_llm/llmapi/llm_args.py

@@ -1259,7 +1259,7 @@ class KvCacheConfig(StrictBaseModel, PybindMirror):
         description=
         "Number of sink tokens (tokens to always keep in attention window).")
     free_gpu_memory_fraction: Optional[float] = Field(
-        default=None,
+        default=0.9,
         description=
         "The fraction of GPU memory fraction that should be allocated for the KV cache. Default is 90%. If both `max_tokens` and `free_gpu_memory_fraction` are specified, memory corresponding to the minimum will be used."
     )
@@ -1341,6 +1341,16 @@ def _to_pybind(self):
             attention_dp_events_gather_period_ms,
             max_gpu_total_bytes=self.max_gpu_total_bytes)
 
+    @field_validator('free_gpu_memory_fraction')
+    @classmethod
+    def validate_free_gpu_memory_fraction(cls, v: float):
+        """Validates that the fraction is between 0.0 and 1.0."""
+        if not 0 <= v <= 1:
+            raise ValueError(
+                "kv_cache_config.free_gpu_memory_fraction must be a float between 0 and 1"
+            )
+        return v
+
     @field_validator('max_gpu_total_bytes')
     @classmethod
     def validate_max_gpu_total_bytes(cls, v: int):