[https://nvbugs/5560921][fix] GenerationExecutor RPC #8209
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📝 WalkthroughWalkthroughIntroduces two static helper methods on GenerationExecutor to centralize creation of RPC and IPC executors, refactors create logic to use them, and tightens orchestrator_type checking. Re-enables previously skipped RPC-related tests by removing skip markers in two test modules. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor Client
participant GenExec as GenerationExecutor.create
participant Config as Config/Args
participant RPC as GenerationExecutorRpcProxy
participant IPCProxy as GenerationExecutorProxy
participant IPCWorker as GenerationExecutorWorker
Client->>GenExec: create(config)
GenExec->>Config: Inspect orchestrator_type, worker mode
alt orchestrator_type == "rpc"
note over GenExec,RPC: New helper path
GenExec->>RPC: _create_rpc_executor(worker_kwargs, ...)
RPC-->>GenExec: RPC proxy instance
else IPC path
alt use_worker == true
note over GenExec,IPCWorker: Single-process worker mode
GenExec->>IPCWorker: _create_ipc_executor(..., use_worker=true)
IPCWorker-->>GenExec: Worker instance
else use_worker == false
note over GenExec,IPCProxy: IPC proxy mode
GenExec->>IPCProxy: _create_ipc_executor(..., use_worker=false)
IPCProxy-->>GenExec: Proxy instance
end
end
GenExec-->>Client: Executor handle
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
✨ Finishing touches
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📒 Files selected for processing (3)
tensorrt_llm/executor/executor.py(5 hunks)tests/unittest/llmapi/test_llm_multi_gpu_pytorch.py(0 hunks)tests/unittest/llmapi/test_llm_pytorch.py(0 hunks)
💤 Files with no reviewable changes (2)
- tests/unittest/llmapi/test_llm_multi_gpu_pytorch.py
- tests/unittest/llmapi/test_llm_pytorch.py
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🧬 Code graph analysis (1)
tensorrt_llm/executor/executor.py (7)
tensorrt_llm/llmapi/mpi_session.py (1)
MpiSession(84-129)tensorrt_llm/executor/postproc_worker.py (1)
PostprocWorkerConfig(42-49)tensorrt_llm/llmapi/llm_args.py (1)
KvCacheConnectorConfig(421-433)tensorrt_llm/executor/rpc_proxy.py (1)
GenerationExecutorRpcProxy(24-375)tensorrt_llm/executor/worker.py (1)
GenerationExecutorWorker(41-229)tensorrt_llm/executor/proxy.py (1)
GenerationExecutorProxy(37-449)tensorrt_llm/executor/utils.py (1)
ProcessPoolExecutorSession(80-104)
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🔇 Additional comments (6)
tensorrt_llm/executor/executor.py (6)
378-395: LGTM!The helper method cleanly centralizes RPC executor creation with appropriate parameters and a clear docstring.
396-426: LGTM!The helper method appropriately abstracts IPC executor creation with clear documentation on the
use_workerparameter controlling single-process vs. multi-process modes.
500-515: LGTM!The refactored calls to
_create_rpc_executorand_create_ipc_executorcorrectly handle the spawn_workers/mpirun_launch path with appropriate parameters.
526-541: LGTM!The return_logits/single-process TP1 path correctly uses
use_worker=Truefor_create_ipc_executorto create a single-process worker, appropriately handling the performance optimization mentioned in the comment at line 517-520.
549-564: LGTM!The non-Windows default path correctly uses the helper methods with
mpi_session=None(line 560) to leverage mpi4py for IPC communication.
570-578: Note: RPC support for Windows is pending.The TODO comment at line 570 indicates that RPC executor support for Windows is not yet implemented. Currently, only IPC executor is created for Windows platforms.
Is RPC support for Windows planned, or should this TODO be tracked in a separate issue?
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Superjomn
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Can we mention the option of rpc here https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/llmapi/llm_args.py#L1558? Other than that, this MR LGTM.
As a side note - I plan to add doc regarding orchestrator_type for ray this week per requested, and will involve you for the RPC part if needed.
Signed-off-by: Superjomn <[email protected]>
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Signed-off-by: Superjomn <[email protected]>
Signed-off-by: Superjomn <[email protected]> Signed-off-by: yufeiwu-nv <[email protected]>
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Summary by CodeRabbit
Refactor
Tests
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PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
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