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 KV cache, 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 KV cache 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.

disaggregated-service usage

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.

Example#

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

Benchmarks#

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

Environment Variables#

TRT-LLM uses some environment variables to control the behavior of disaggregated service.

  • TRTLLM_USE_MPI_KVCACHE: Whether to use MPI to transfer KV cache. Currently, the default value is 0.

  • TRTLLM_USE_UCX_KVCACHE: Whether to use UCX to transfer KV cache. Currently, the default value is 0. To use disaggregated service, either TRTLLM_USE_MPI_KVCACHE=1 or TRTLLM_USE_UCX_KVCACHE=1 is required to be set.

  • TRTLLM_PARALLEL_CACHE_SEND: If set to 1, contextExecutor will attempt to send KV cache for multiple requests in parallel. The default value is 0.

  • TRTLLM_DISABLE_KV_CACHE_TRANSFER_OVERLAP: If set to 1, generationExecutor will not overlap KV cache transfer with model inference. The default value is 0.

  • TRTLLM_ENABLE_KVCACHE_RECEIVE_PARALLEL: When the generation rank receives KV cache from multiple context ranks within a single context instance, it will receive KV cache from each rank sequentially. If set to 1, the generation rank will receive KV cache from each rank within one context instance in parallel. The default value is 0.

  • TRTLLM_REQUEST_KV_CACHE_CONCURRENT: If set to 1, generationExecutor prepares independent resources for each context executor to receive KV cache, requests whose KV cache are received from different context executors will be processed concurrently. If set to 0, the generation executor will reuse the same resource to process KV cache transfer for each request sequentially, reducing the resources used by KV cache transmission and thereby lowering the risk of running out of memory. The default value is 0.

  • TRTLLM_TRY_ZCOPY_FOR_KVCACHE_TRANSFER: TRT-LLM typically copies non-contiguous data into a temporary buffer before sending KV cache. If set to 1, TRT-LLM will attempt to directly transmit each KV cache block, eliminating extra copies. The default value is 0.

  • TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE: By default, TRT-LLM uses a stream-ordered memory allocator to allocate temporary buffers. If this environment variable is set to #Size, TRT-LLM will use cudaMalloc to allocate buffer of size #Size for KV cache transmission. The default value is 512MB. Users can set TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=1GB to allocate a 1 GB buffer with cudaMalloc for KV cache transmission.

  • TRTLLM_KVCACHE_TRANSFER_USE_ASYNC_BUFFER: If set to 1, TRT-LLM will use cudaMallocAsync to allocate buffers for KV cache transmission. The default value is 0. This environment variable only takes effect when TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE is greater than 0.

  • TRTLLM_KVCACHE_SEND_MAX_CONCURRENCY_NUM: The maximum number of concurrent KV cache sends. The default value is 4. This environment variable only takes effect when TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE is greater than 0.

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 KV cache 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 KV cache.

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 KV cache transfer with CUDA-aware MPI, and will support more communication components for KV cache transfer in future version.

Debugging FAQs#

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

A. please set the environment variables

export TRTLLM_USE_MPI_KVCACHE=1

or

export TRTLLM_USE_UCX_KVCACHE=1

When the environment variable TRTLLM_USE_MPI_KVCACHE=1 is set, TRT-LLM will transfer the KV cache using CUDA-aware MPI. All executor processes involved must share the same MPI world communicator. Consequently, with TRTLLM_USE_MPI_KVCACHE=1, TRT-LLM only supports launching multiple executors via MPI. Additionally, the CommunicationMode for the executors must be set to kLEADER or kORCHESTRATOR with SpawnProcesses=false for the disaggregated-service. These restrictions do not apply when TRTLLM_USE_UCX_KVCACHE=1 is set.

Q. Why do some profiling tools show that TRT-LLM’s KV cache 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 the version of UCX <=1.17, set the environment variables UCX_RNDV_FRAG_MEM_TYPE=cuda and UCX_MEMTYPE_CACHE=n to enable NVLink. For BlackWell architecture GPUs, UCX version >=1.19 is required to enable NVLink. If the version of UCX >=1.18, there are several ways to enable NVLink:

  1. Set the environment variables TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=0B,UCX_CUDA_COPY_ASYNC_MEM_TYPE=cuda, UCX_CUDA_COPY_DMABUF=no, UCX_MEMTYPE_CACHE=n and UCX_RNDV_PIPELINE_ERROR_HANDLING=y.

  2. Set the environment variables TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=$Size, UCX_MEMTYPE_CACHE=n and UCX_RNDV_PIPELINE_ERROR_HANDLING=y. $Size represents the size of the buffer for KV cache transfer, which is recommended to be larger than the size of the KV cache for the longest request.

Q. Does TRT-LLM support using GPU direct RDMA for inter-node KV Cache transfer?

A. Yes, TRT-LLM supports using GPU direct RDMA for inter-node KV cache transfer, but it is not enabled by default. There are several ways to enable GPU direct RDMA:

  1. Set the environment variables TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=0B,UCX_RNDV_FRAG_MEM_TYPE=cuda, UCX_MEMTYPE_CACHE=n and UCX_RNDV_PIPELINE_ERROR_HANDLING=y.

  2. Set the environment variables TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=$Size, UCX_MEMTYPE_CACHE=n and UCX_RNDV_PIPELINE_ERROR_HANDLING=y, $Size represents the size of the buffer for KV cache transfer, which is recommended to be larger than the size of the KV cache for the longest request. To achieve the optimal performance when using GPU direct RDMA, it is advisable to create CUDA context before MPI initialization when TRTLLM_USE_MPI_KVCACHE=1 is set. One possible approach is to rely on MPI environment variables to set the correct device before MPI initialization.

Q. Are there any guidelines for performance tuning of KV cache transfer?

A. Depending on the user’s use case, certain sets of environment variables can help avoid poor KV cache transfer performance.

Environment Variable Set A

export TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=0B
export UCX_RNDV_FRAG_MEM_TYPES=cuda
export UCX_MEMTYPE_CACHE=n
export UCX_RNDV_PIPELINE_ERROR_HANDLING=y

This set allows KV cache transfers to utilize NVLink within nodes and GDRDMA between nodes.

Environment Variable Set B

export TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=0B
export UCX_CUDA_COPY_ASYNC_MEM_TYPE=cuda
export UCX_CUDA_COPY_DMABUF=no
export UCX_MEMTYPE_CACHE=n
export UCX_RNDV_PIPELINE_ERROR_HANDLING=y

Set B may provide slightly better performance on a single node compared to Set A. However, when transferring KV cache across multiple nodes, it may cause program instability.

Environment Variable Set C

export TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=$Size
export UCX_MEMTYPE_CACHE=n
export UCX_RNDV_PIPELINE_ERROR_HANDLING=y

Set C can achieve better performance than Sets A and B, both within and between nodes. However, if the KV cache size exceeds the specified $Size, performance may degrade.