nvidia-trt[patch]: add TritonTensorRTLLM(verbose_client=False) (#16848)

- **Description:** adding verbose flag to TritonTensorRTLLM, 
  - **Issue:** nope,
  - **Dependencies:** not any,
  - **Twitter handle:**

libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py

@@ -40,6 +40,7 @@ class TritonTensorRTLLM(BaseLLM):
         length_penalty: (float) The penalty to apply repeated tokens
         tokens: (int) The maximum number of tokens to generate.
         client: The client object used to communicate with the inference server
+        verbose_client: flag to pass to the client on creation
 
     Example:
         .. code-block:: python
@@ -73,6 +74,7 @@ class TritonTensorRTLLM(BaseLLM):
         description="Request the inference server to load the specified model.\
             Certain Triton configurations do not allow for this operation.",
     )
+    verbose_client: bool = False
 
     def __del__(self):
         """Ensure the client streaming connection is properly shutdown"""
@@ -82,7 +84,9 @@ class TritonTensorRTLLM(BaseLLM):
     def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Validate that python package exists in environment."""
         if not values.get("client"):
-            values["client"] = grpcclient.InferenceServerClient(values["server_url"])
+            values["client"] = grpcclient.InferenceServerClient(
+                values["server_url"], verbose=values.get("verbose_client", False)
+            )
         return values
 
     @property

libs/partners/nvidia-trt/tests/unit_tests/test_llms.py

@@ -1,7 +1,33 @@
 """Test TritonTensorRT Chat API wrapper."""
+import sys
+from io import StringIO
+from unittest.mock import patch
+
 from langchain_nvidia_trt import TritonTensorRTLLM
 
 
 def test_initialization() -> None:
     """Test integration initialization."""
     TritonTensorRTLLM(model_name="ensemble", server_url="http://localhost:8001")
+
+
+@patch("tritonclient.grpc.service_pb2_grpc.GRPCInferenceServiceStub")
+def test_default_verbose(ignore) -> None:
+    llm = TritonTensorRTLLM(server_url="http://localhost:8001", model_name="ensemble")
+    captured = StringIO()
+    sys.stdout = captured
+    llm.client.is_server_live()
+    sys.stdout = sys.__stdout__
+    assert "is_server_live" not in captured.getvalue()
+
+
+@patch("tritonclient.grpc.service_pb2_grpc.GRPCInferenceServiceStub")
+def test_verbose(ignore) -> None:
+    llm = TritonTensorRTLLM(
+        server_url="http://localhost:8001", model_name="ensemble", verbose_client=True
+    )
+    captured = StringIO()
+    sys.stdout = captured
+    llm.client.is_server_live()
+    sys.stdout = sys.__stdout__
+    assert "is_server_live" in captured.getvalue()