Disaggregated-Service (experimental)

Note

Note: This feature is currently experimental, and the related API is subjected to change in future versions.

Currently TRT-LLM supports disaggregated-service, where the context and generation phases of a request can run on different executors. TRT-LLM’s disaggregated service relies on the executor API, please make sure to read the executor page before reading the document.

For more information on disaggregated service in LLM inference, one can refer to papers such as DistServe, SplitWise.

Usage

enum class RequestType
{
    REQUEST_TYPE_CONTEXT_AND_GENERATION = 0,
    REQUEST_TYPE_CONTEXT_ONLY = 1,
    REQUEST_TYPE_GENERATION_ONLY = 2
};

The TRT-LLM executor can execute three types of requests: REQUEST_TYPE_CONTEXT_AND_GENERATION, REQUEST_TYPE_CONTEXT_ONLY, and REQUEST_TYPE_GENERATION_ONLY. An executor instance could execute the context phase of the context-only request or the generation phase of the generation-only request. When the executor completes the context phase of a context-only request, it maintains the corresponding kvCache, which will be requested by the executor for the subsequent generation-only request.

Note that the environment variable TRTLLM_USE_MPI_KVCACHE=1 should be set for disaggregated-service.

Here are some key APIs to use disaggregated service:

Request request{...};

request.setRequestType(tensorrt_llm::executor::RequestType::REQUEST_TYPE_CONTEXT_ONLY);

auto contextRequestId = contextExecutor.enqueueRequest(request);

auto contextResponses = contextExecutor.awaitResponses(contextRequestId);

auto contextPhaseParams = contextResponses.back().getResult().contextPhaseParams.value();

request.setContextPhaseParams(contextPhaseParams);

request.setRequestType(tensorrt_llm::executor::RequestType::REQUEST_TYPE_GENERATION_ONLY);

auto generationRequestId = generationExecutor.enqueueRequest(request);

auto genResponses = generationExecutor.awaitResponses(generationRequestId);

The generationExecutor will require data such as kvCache from the corresponding contextExecutor based on the contextPhaseParams attached to the request, so please make sure that the corresponding contextExecutor is not shut down before getting the generationExecutor’s response.

In the code example above, the contextRequestId assigned by the contextExecutor and the generationRequestId assigned by the generationExecutor are independent, it is the user’s responsibility to manage the mapping of the requestId for context-only requests to the requestId for generation-only requests. The contextResponses contains the first output token generated by the context phase, and the genResponses also contains the first output token generated by the contextExecutor, so all output tokens can be obtained from generationExecutor’s responses.

An orchestrator is required in disaggregated-service to manage multiple executor instances and route requests to different executors, TRT-LLM provides class DisaggExecutorOrchestrator in cpp/include/tensorrt_llm/executor/disaggServerUtil.h to launch multiple executor instances, however, DisaggExecutorOrchestrator only routes requests to executors in a simple round-robin policy, users need to implement their own orchestrator for disaggregated-service based on their usage scenario.

TRT-LLM currently implements kvCache transfer using CUDA-aware MPI, and all executor processes involved need to hold the same MPI world communicator. Therefore, TRT-LLM only supports launching multiple executors using MPI, and the CommunicationMode of the executors must be set to kLEADER or kORCHESTRATOR with SpawnProcesses=false for disaggregated-service, TRT-LLM will relax this restriction in future version to manage executors with greater ease.

Example

Please refer to examples/cpp/executor/executorExampleDisaggregated.cpp

Benchmarks

Please refer to benchmarks/cpp/disaggServerBenchmark.cpp and benchmarks/cpp/README.md

Troubleshooting and FAQ

General FAQs

Q. What are the limitations of disaggregated-service in TRT-LLM?

A. Currently, only decoder-only engine and beamWidth=1 are supported, and the kvCache at each layer of the model is required to be homogeneous, with the same data type and the same number of attention headers.

Q. Is the engine used by disaggregated-service different from other engines?

A. No. There are no special requirements for the arguments to build engine.

Q. Do the engines used by the context executor and generation executor need to be the same?

A. No. The engines used by context executor and generation executor can be different, and their parallelism can be heterogeneous, i.e., TP,PP can be different, and TRT-LLM will handle the heterogeneity of kvCache.

Q. Does TRT-LLM support running multiple context executor instances and generation executor instances?

A. Yes. TRT-LLM supports running multiple context executors and generation executors at the same time, and each executor can use different engine, but it is the user’s responsibility to route requests to different executors and manage requestId.

Q. Can an executor handle both context-only requests and generation-only requests?

A. Yes, but it’s not recommended, TRT-LLM does not implement proper scheduling for the case where the executor handles mixed context-only requests and generation-only requests, it’s better to run context-only requests and generation-only requests on different executors.

Q. Does disaggregated-service in TRT-LLM support multi-gpu and multi-node?

A. Yes, it’s recommended that different executor use different GPUs . We support context-only executor and genertion-only executor run on same node or different nodes. The participantIds and deviceIds used by each executor need to be explicitly set by the user, and the participantIds of each executor must not be intersecting.

Q. What’s the requirement for disaggregated-service in TRT-LLM?

A. TRT-LLM requires UCX-backend CUDA-aware MPI currently, TRT-LLM implements kvCache transfer with CUDA-aware MPI, and will support more communication components for kvCache transfer in future version.

Debugging FAQs

Q. How to handle error Disaggregated serving is not enabled, please check the configuration?

A. please set env

export TRTLLM_USE_MPI_KVCACHE=1

Q. Why do some profiling tools show that TRT-LLM’s kvCache transfer does not utilize NVLink even on devices equipped with NVLink?

A. Ensure TRT-LLM is running with UCX-backend CUDA-aware MPI , and check version of UCX with ucx_info -v. If version of UCX <=1.17, set env UCX_RNDV_FRAG_MEM_TYPE=cuda and UCX_MEMTYPE_CACHE=n to enable NVLink. If version of UCX =1.18, set env UCX_CUDA_COPY_ASYNC_MEM_TYPE=cuda, UCX_CUDA_COPY_DMABUF=no and UCX_MEMTYPE_CACHE=n.