fix: Update the KVBM <> TRT-LLM integration interface to match the latest...

fix: Update the KVBM <> TRT-LLM integration interface to match the latest TRT-LLM connector API (#2979)
Signed-off-by: richardhuo-nv <rihuo@nvidia.com>

components/backends/trtllm/README.md

@@ -303,3 +303,9 @@ sampling_params.logits_processor = create_trtllm_adapters(processors)
 ## Performance Sweep
 
 For detailed instructions on running comprehensive performance sweeps across both aggregated and disaggregated serving configurations, see the [TensorRT-LLM Benchmark Scripts for DeepSeek R1 model](./performance_sweeps/README.md). This guide covers recommended benchmarking setups, usage of provided scripts, and best practices for evaluating system performance.
+
+## Dynamo KV Block Manager Integration
+
+Dynamo with TensorRT-LLM currently supports integration with the Dynamo KV Block Manager. This integration can significantly reduce time-to-first-token (TTFT) latency, particularly in usage patterns such as multi-turn conversations and repeated long-context requests.
+
+Here is the instruction: [Running KVBM in TensorRT-LLM](./../../../docs/guides/run_kvbm_in_trtllm.md) .

container/Dockerfile.trtllm

@@ -4,6 +4,7 @@
 ARG BASE_IMAGE="nvcr.io/nvidia/pytorch"
 ARG BASE_IMAGE_TAG="25.06-py3"
 ARG RELEASE_BUILD
+ARG ENABLE_KVBM=false
 ARG RUNTIME_IMAGE="nvcr.io/nvidia/cuda"
 ARG RUNTIME_IMAGE_TAG="12.9.1-runtime-ubuntu24.04"
 
@@ -234,6 +235,7 @@ ARG ARCH_ALT
 FROM quay.io/pypa/manylinux_2_28_${ARCH_ALT} AS wheel_builder
 ARG RELEASE_BUILD
 ARG CARGO_BUILD_JOBS
+ARG ENABLE_KVBM
 # Set CARGO_BUILD_JOBS to 16 if not provided
 # This is to prevent cargo from building $(nproc) jobs in parallel,
 # which might exceed the number of opened files limit.
@@ -279,16 +281,21 @@ COPY launch /workspace/launch
 RUN cargo build \
 	--release \
 	--locked \
-	--features dynamo-llm/block-manager \
+	--features block-manager \
 	--workspace
 
 # Build dynamo wheels
 RUN uv build --wheel --out-dir /workspace/dist && \
     cd /workspace/lib/bindings/python && \
-    uv build --wheel --out-dir /workspace/dist --python 3.12 && \
+    uv pip install maturin[patchelf] && \
+    if [ "$ENABLE_KVBM" = "true" ]; then \
+        maturin build --release --features block-manager --out /workspace/dist; \
+    else \
+        maturin build --release --out /workspace/dist; \
+    fi && \
     if [ "$RELEASE_BUILD" = "true" ]; then \
-        uv build --wheel --out-dir /workspace/dist --python 3.11 && \
-        uv build --wheel --out-dir /workspace/dist --python 3.10; \
+        uv run --python 3.11 maturin build --release --out /workspace/dist && \
+        uv run --python 3.10 maturin build --release --out /workspace/dist; \
     fi
 
 ########################################

docs/guides/run_kvbm_in_trtllm.md

@@ -38,8 +38,12 @@ To use KVBM in TensorRT-LLM, you can follow the steps below:
 # start up etcd for KVBM leader/worker registration and discovery
 docker compose -f deploy/docker-compose.yml up -d
 
-# Build a container that includes TensorRT-LLM and KVBM. Note: KVBM integration is only available in TensorRT-LLM commit ce580ce4f52af3ad0043a800b3f9469e1f1109f6 or newer.
-./container/build.sh --framework trtllm --tensorrtllm-commit ce580ce4f52af3ad0043a800b3f9469e1f1109f6 --enable-kvbm
+# Build a container that includes TensorRT-LLM and KVBM. Note: KVBM integration is only available in TensorRT-LLM commit dcd110cfac07e577ce01343c455917832b0f3d5e or newer.
+# When building with the --tensorrtllm-commit option, you may notice that https://github.com keeps prompting for a username and password.
+# This happens because cloning TensorRT-LLM can hit GitHub’s rate limit.
+# To work around this, you can keep pressing "Enter" or "Return.".
+# Setting "export GIT_LFS_SKIP_SMUDGE=1" may also reduce the number of prompts.
+./container/build.sh --framework trtllm --tensorrtllm-commit dcd110cfac07e577ce01343c455917832b0f3d5e --enable-kvbm
 
 # launch the container
 ./container/run.sh --framework trtllm -it --mount-workspace --use-nixl-gds

lib/bindings/python/src/dynamo/llm/trtllm_integration/connector/kvbm_connector_leader.py

@@ -8,8 +8,8 @@ from tensorrt_llm._torch.pyexecutor.kv_cache_connector import (
     KvCacheConnectorScheduler,
     SchedulerOutput,
 )
-from tensorrt_llm.bindings.executor import ExecutorConfig
 from tensorrt_llm.bindings.internal.batch_manager import LlmRequest
+from tensorrt_llm.llmapi.llm_args import TorchLlmArgs
 
 from dynamo.llm import KvbmLeader
 from dynamo.llm.trtllm_integration.rust import KvbmRequest
@@ -21,21 +21,21 @@ from dynamo.runtime import DistributedRuntime
 
 
 class DynamoKVBMConnectorLeader(KvCacheConnectorScheduler):
-    def __init__(self, executor_config: ExecutorConfig):
-        super().__init__(executor_config)
+    def __init__(self, llm_args: TorchLlmArgs):
+        super().__init__(llm_args)
         self.drt = DistributedRuntime.detached()
 
-        world_size = self._config.mapping.world_size
-        self.block_size = self._config.tokens_per_block
+        mappings = self._llm_args.parallel_config.to_mapping()
+
+        world_size = mappings.world_size
+        self.block_size = self._llm_args.kv_cache_config.tokens_per_block
 
         # Set bytes_per_block to 0, because we will retrieve the actual value from the worker side.
         leader = KvbmLeader(world_size, drt=self.drt)
 
-        print(
-            f"KvConnectorLeader initialized with rank: {executor_config.mapping.rank}"
-        )
+        print(f"KvConnectorLeader initialized with rank: {mappings.rank}")
         self._connector = RustKvConnectorLeader(
-            executor_config.mapping.rank, self.drt, self.block_size, leader
+            mappings.rank, self.drt, self.block_size, leader
         )
 
     def build_connector_meta(self, scheduler_output: SchedulerOutput) -> bytes:

lib/bindings/python/src/dynamo/llm/trtllm_integration/connector/kvbm_connector_worker.py

@@ -4,7 +4,7 @@
 import torch
 from tensorrt_llm import logger
 from tensorrt_llm._torch.pyexecutor.kv_cache_connector import KvCacheConnectorWorker
-from tensorrt_llm.bindings.executor import ExecutorConfig
+from tensorrt_llm.llmapi.llm_args import TorchLlmArgs
 
 from dynamo.llm.trtllm_integration.rust import (
     KvConnectorWorker as RustKvConnectorWorker,
@@ -13,16 +13,15 @@ from dynamo.runtime import DistributedRuntime
 
 
 class DynamoKVBMConnectorWorker(KvCacheConnectorWorker):
-    def __init__(self, executor_config: ExecutorConfig):
-        super().__init__(executor_config)
+    def __init__(self, llm_args: TorchLlmArgs):
+        super().__init__(llm_args)
 
         self.drt = DistributedRuntime.detached()
 
-        self.rank = executor_config.mapping.rank
+        mappings = self._llm_args.parallel_config.to_mapping()
+        self.rank = mappings.rank
 
-        self._connector = RustKvConnectorWorker(
-            self.drt, str(executor_config.mapping.rank)
-        )
+        self._connector = RustKvConnectorWorker(self.drt, str(self.rank))
 
     def register_kv_caches(self, kv_cache_tensor: torch.Tensor):
         """
@@ -33,11 +32,11 @@ class DynamoKVBMConnectorWorker(KvCacheConnectorWorker):
         """
         print(f"Register KV Caches on rank {self.rank}")
         logger.info(
-            f"KvConnectorWorker started registering the kv caches on rank {self._config.mapping.rank}"
+            f"KvConnectorWorker started registering the kv caches on rank {self.rank}"
         )
 
         num_device_blocks = kv_cache_tensor.shape[0]
-        page_size = self._config.tokens_per_block
+        page_size = self._llm_args.kv_cache_config.tokens_per_block
         device_id = kv_cache_tensor.device.index
         kv_cache_dtype = kv_cache_tensor.dtype