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.

An architectural and performance overview, as well as usage examples, are provided.

Environment Variables#

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

  • 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.

Debugging FAQs#

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

A. please set backendType of CacheTransceiverConfig.

ExecutorConfig executorConfig{...};

executorConfig.setCacheTransceiverConfig(texec::CacheTransceiverConfig(BackendType::DEFAULT));

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. 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.

Q. What causes the substantial bandwidth fluctuations in kvCache transfers, especially during the first few requests following service initialization?

A. The communication for kvCache transfer between executors are established dynamically. The connection establishment process incurs significant overhead, which explains the apparently lower kvCache transfer bandwidth observed during the initial requests after service startup. This lower bandwidth reflects the inclusion of connection establishment overhead. When conducting benchmarks, it is recommended to perform a warm-up phase to ensure accurate performance measurements.