TensorRT-LLMs/examples/disaggregated

TRT-LLM Disaggregated Serving

To run TRT-LLM in disaggregated mode, you must first launch context (prefill) and generation (decode) servers using trtllm-serve. Depending on your deployment environment, this can be done in different ways.

Launching context and generation servers using multiple independent trtllm-serve commands

You can use multiple trtllm-serve commands to launch the context and generation servers that will be used for disaggregated serving. For example, you could launch two context servers and one generation servers as follows:

export TRTLLM_USE_UCX_KVCACHE=1
#Context servers
trtllm-serve TinyLlama/TinyLlama-1.1B-Chat-v1.0 --host localhsot --port 8001 --backend pytorch &> log_ctx_0 &
trtllm-serve TinyLlama/TinyLlama-1.1B-Chat-v1.0 --host localhsot --port 8002 --backend pytorch &> log_ctx_1 &
#Generation servers
trtllm-serve TinyLlama/TinyLlama-1.1B-Chat-v1.0 --host localhsot --port 8003 --backend pytorch &> log_gen_0 &

Once the context and generation servers are launched, you can launch the disaggregated server, which will accept requests from clients and do the orchestration between context and generation servers. The disaggregated server can be launched with:

trtllm-serve disaggregated -c disagg_config.yaml

where disagg_config.yaml contains information about the context and generation servers. For the current example, it would look like:

hostname: localhost
port: 8000
backend: pytorch
context_servers:
  urls:
      - "localhost:8001"
      - "localhost:8002"
generation_servers:
  urls:
      - "localhost:8003"

Clients can then send requests to the disaggregated server at localhost:8000, which is an OpenAI compatible endpoint.

Launching context and generation servers using MPI

One can also launch all context and generation servers using MPI. This can be done by issuing the following command:

export TRTLLM_USE_MPI_KVCACHE=1
mpirun -n <total_num_ranks> trtllm-serve disaggregated_mpi_worker -c disagg_config.yaml

where <total_num_ranks> is the sum of TP*PP for all context and generation servers. For the example above, total_num_ranks is 3 since TP and PP is 1 for all context and generation servers.

The disagg_config.yaml file must now contain the configuration parameters of the context and generation servers. For example, it could look like:

hostname: localhost
port: 8000
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
backend: "pytorch"
pytorch_backend_config:
  use_cuda_graph: False
  enable_overlap_scheduler: False
context_servers:
  num_instances: 1
  tensor_parallel_size: 1
  pipeline_parallel_size: 1
  kv_cache_config:
    free_gpu_memory_fraction: 0.9
  urls:
      - "localhost:8001"
      - "localhost:8002"
generation_servers:
  num_instances: 1
  tensor_parallel_size: 1
  pipeline_parallel_size: 1
  urls:
      - "localhost:8003"

Once the context and generation servers are launched, you can again launch the disaggregated server with

trtllm-serve disaggregated -c disagg_config.yaml

Sending requests to the disaggregated server

Once the context, generation and disaggregated servers are launched, you can send requests to the disaggregated server using curl:

curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d '{
        "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        "prompt": "NVIDIA is a great company because",
        "max_tokens": 16,
        "temperature": 0
    }' -w "\n"

Or using the provided client:

python3 ./clients/disagg_client.py -c disagg_config.yaml -p ./clients/prompts.json