[TRTLLM-17054][refactor] Reuse PyExecutor across profiling resource transitions

[zhaoyangwang-nvidia]

Dev Engineer Review

Refactors PyExecutor to reuse one executor across profiling and serving. Adds PyExecutorResources, release_resources(), and rebind_resources(). Resource teardown retains reusable model, sampler, streams, and distributed state. Final shutdown remains terminal and idempotent. Failed transitions close the executor.

The lifecycle guardrails do not detect resources added directly to PyExecutor. A follow-up is planned to centralize ownership and cleanup.

QA Engineer Review

Modified tests cover resource release and rebinding, teardown ordering, worker hangs, transceiver failures, profiling cleanup, startup allocation, state restoration, and failed transitions. The reported results include 132 related CPU tests and pre-commit passing. Full GPU qualification remains pending. Reported CI pipelines 64270, 65182, and 65238 failed. No changed tests are referenced by the inspected integration test lists.

Coverage verdict: needs follow-up.

Per-File QA Perspective

• tensorrt_llm/_torch/pyexecutor/_util.py: Adds shared resource construction. Verify resource ownership and initialization parity.
• tensorrt_llm/_torch/pyexecutor/py_executor.py: Adds resource release and rebinding APIs. Verify teardown ordering, retained state, failed transitions, optional resources, and shutdown.
• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py: Reuses the profiling executor for final KV-cache resources. Verify profiling cleanup, warmup, and fail-closed behavior.
• tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py: Covers profiling cleanup and state restoration after failure. No integration test-list entry was found.
• tests/unittest/_torch/executor/test_executor_resource_rebind.py: Covers lifecycle contracts, resource binding, hangs, failures, and shutdown. No integration test-list entry was found.
• tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py: Covers startup allocation phases and failed transitions. No integration test-list entry was found.
• tests/unittest/_torch/executor/test_gpu_keepalive.py: Verifies keepalive wiring during execution-phase initialization. No integration test-list entry was found.
• tests/unittest/_torch/executor/test_kv_connector_executor_lifetime.py: Updates executor fixture lifecycle state. No integration test-list entry was found.
• tests/unittest/_torch/executor/test_pp_mpi_progress_thread.py: Updates executor fixture lifecycle and transceiver state. No integration test-list entry was found.

Description

Memory profiling currently constructs a temporary PyExecutor, shuts it down, and constructs another executor for serving. Reuse one executor across this transition while replacing resources whose size or registration depends on the final KV-cache allocation.

Introduce PyExecutorResources and a shared execution-phase initializer. release_resources() stops execution, destroys graphs, unregisters transfers, and releases phase-owned resources while retaining the model, sampler, streams, and distributed context. rebind_resources() installs the final resources and warms them before starting the worker. Invalid transitions and initialization failures fail closed; final shutdown remains terminal and idempotent.

Both profiling and final-resource warmups remain necessary and are preserved. The steady-state executor loop, model forward path, and user-facing configuration are unchanged. This is a lifecycle refactor, not a claim of significant startup or inference speedup.

This PR is based directly on main (306a8a7c30) and contains the executor-reuse change and its test/review follow-ups touching six files. It has no dependency on #20253. Main's native MPI progress-thread shutdown behavior is preserved during resource release, with regression coverage for normal pipeline shutdown and the hung-worker path.

Test Coverage

Committed regression tests (run with the project test environment and LLM_MODELS_ROOT configured where model weights are needed):

pytest tests/unittest/_torch/executor/test_executor_resource_rebind.py
pytest tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py::test_profiling_failure_releases_resources_and_restores_response_state
pytest tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py::test_startup_allocation_phases
pytest tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py::test_failed_resource_transition_closes_executor

There are 17 added CPU-only mock cases: 13 lifecycle cases, three factory failure stages, and one profiling-failure case. Draft-graph and async-sampler teardown assertions share the main teardown case; repeated release shares the final-shutdown case. Existing factory tests also assert single-executor construction without adding model runs. No GPU benchmark or long-running qualification harness is added to CI.

These cover teardown order, model retention, sampler/worker termination, transfer-unregistration failure, invalid/repeated transitions, final shutdown, profiling failure, and final allocation/rebind failure.

Current revision (dfb3554774) validation: all 132 tests passed across test_executor_resource_rebind.py, test_profile_endpoints.py, kv_cache/test_kv_cache_estimation.py, and test_py_executor_creator_mla_cache_reuse_sync.py in an isolated CPU container using current Python source and compatible retained native artifacts (PyTorch 26.08). Pre-commit passed; mypy skips these files. The existing successful-rebind test now executes the real _initialize_execution, with GPU/distributed collaborators mocked, and checks phase resets, final-cache binding, profiler markers, and no worker start. Removing the marker-reset assignments in a temporary source copy makes that regression fail. The number of added test cases is unchanged. Full GPU qualification of the main-based revision remains pending.

CI investigation: build 64270, on the preceding head, failed test_llm_disagg_streaming_gen_cancelled[PYTHON] on A100 with a request timeout and KV-transfer errors. Original shard logs confirm the same test and KV-transfer-error signature in build 64230 (PR #20279, Qwen tool-parser changes) and build 64266 (PR #20290, Blackwell top-k changes). This establishes a shared failure independent of executor reuse; it does not exclude additional impact from this PR without a controlled GPU comparison. SBSA aborts and blocked multi-GPU stages were cascading failures. No manual full-CI rerun was requested for this follow-up.

Historical GPU qualification only: the following results compare cleanup baseline 8fe47ceaf with the previous candidate 1e7f0845b, before transplanting onto main. They do not establish correctness or performance of current head dfb3554774, including its integration with main's newer MPI progress thread. H200 tests used identical native artifacts for those two historical revisions:

• Selected unit regression suite: baseline 657 passed and candidate 676 passed, plus 14 subtests each. Pre-commit passed again before opening this draft; mypy skipped these files because they are outside its allow-list.
• Standalone GPU A/B qualification: normal profiling constructs the executor 2→1 times; skip-estimation stays at one. KV V1/V2, graph/eager fallback, overlap, chunked prefill, observed eviction/recomputation, constrained VRAM, TP2/PP2, speculative decoding, real MLA/hybrid Mamba, nonzero LoRA eviction/reload, and final-KV-allocation failure cleanup passed.
• Real two-GPU NIXL context-to-generation transfer with graph on/off: reference parity, cancellation, orphan-context timeout reclamation, recovery, and shutdown passed. This is not a cross-node bandwidth benchmark.
• Five create/infer/shutdown cycles reclaimed models and workers. Both revisions exhibit the same framework-global cuBLAS workspace retention; clearing only that cache isolates stable executor-owned allocation. Raw measurements were retained.
• Controlled precision comparisons: 80 context/generation-logit tensors matched exactly; a further 10-minute run per revision matched all 3,159 paired requests and cache-hit records with zero same-shape allocation growth. Exact comparisons align batch admission; MLA additionally fixes the execution path by disabling autotuning. Unmodified-baseline repeats reproduced the asynchronous/autotuner differences, so default execution is not claimed to be bitwise deterministic.
• HTTP streaming: three alternating A/B rounds, concurrency 1/8/32, 120 mixed-length requests per phase. Input lengths 64/4096/8192 and output lengths 32/128/256; default autotuning and asynchronous scheduling retained. Throughput medians changed +0.005%/+0.133%/+0.231%. An additional concurrency-8 check used two alternating pairs of 600-request runs. All 2,280 paired HTTP outputs matched.
• The small concurrency-8 TPOT P99 increase was investigated rather than omitted: the larger-sample paired changes were +1.828% and -0.045%, with an aggregate +0.885%; throughput increased +0.213%. Aggregate metrics stayed within the 3% regression gate. Large-sample TTFT P50 varied between approximately 133–163 ms in both revisions, so single-run median shifts are not interpreted as stable gains or regressions.

The GPU qualification harnesses and raw artifacts are retained separately from this production patch; they are not additional committed CI tests. Results establish the tested coverage and tolerance, not universal zero-difference performance.

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

• Update tava architecture diagram if there is a significant design change in PR.

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Walkthrough

The change separates PyExecutor execution resources from retained model state. It adds resource release and rebinding, then reuses the profiling executor during KV-cache estimation.

Changes

Executor Resource Reuse

Layer / File(s)	Summary
Resource bundle and executor initialization tensorrt_llm/_torch/pyexecutor/py_executor.py, tensorrt_llm/_torch/pyexecutor/_util.py	PyExecutorResources groups replaceable resources and capacity settings. Resource construction returns this bundle, and executor initialization binds its contents.
Release and rebind execution resources tensorrt_llm/_torch/pyexecutor/py_executor.py, tests/unittest/_torch/executor/test_executor_resource_rebind.py, tests/unittest/_torch/executor/*	PyExecutor releases execution resources and can bind new resources after release while retaining model engines. Shutdown handles final closure. Tests cover teardown order, lifecycle conditions, failure handling, keepalive wiring, and executor state fixtures.
Reuse the profiling executor tensorrt_llm/_torch/pyexecutor/py_executor_creator.py, tensorrt_llm/_torch/pyexecutor/_util.py, tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py, tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py	KV-cache estimation releases profiling resources, creates final resources, and rebinds them to the existing executor. Tests cover successful reuse and cleanup after failures.

Priority: ⬇️ Low

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Refactor

Sequence Diagram(s)

sequenceDiagram
  participant PyExecutorCreator
  participant PyExecutor
  participant create_py_executor_resources
  PyExecutorCreator->>PyExecutor: release_resources()
  PyExecutor-->>PyExecutorCreator: resources released
  PyExecutorCreator->>create_py_executor_resources: build final execution resources
  create_py_executor_resources-->>PyExecutorCreator: return PyExecutorResources
  PyExecutorCreator->>PyExecutor: rebind_resources(resources)

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Merge Risk: 🔵 Low · up to 31d7d

This change reuses one executor across KV-cache profiling and serving instead of building two. The reviewed lifecycle paths look correct. One gap remains: the creator's success-path test does not confirm that the final resources are actually rebound, so a future regression there could merge unnoticed. Merging is reasonable once that assertion is added or the gap is accepted. Pending GPU qualification still applies.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 41.86% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 43 functions across 8 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly and concisely describes the main change: reusing one PyExecutor across profiling resource transitions. It follows the required ticket and type format.
Description check		The description is complete and relevant. It explains the problem, solution, lifecycle behavior, testing, known CI limitations, and checklist status. It provides substantial test coverage details and …
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Description check

Explanation

The description is complete and relevant. It explains the problem, solution, lifecycle behavior, testing, known CI limitations, and checklist status. It provides substantial test coverage details and identifies that current full GPU qualification remains pending.

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Actionable comments posted: 1

🧹 Nitpick comments: 1

tensorrt_llm/_torch/pyexecutor/py_executor.py: 1

1255-1273: 📐 Maintainability & Code Quality | 🔵 Trivial | 🏗️ Heavy lift

Add a test that runs the real _initialize_execution during a rebind.

All tests for rebind_resources replace _initialize_execution with a Mock. No test checks that a second initialization on a reused executor resets the per-phase state that matters for serving:

• iter_counter
• _ctx_mla_kv_len_cap must return to "unset", so the serving cap comes from the final KV manager and not from the throwaway manager.
• _disagg_coordinator
• worker_started
• A new shutdown_event
• kv_cache_manager must point at the new resource manager.

A regression that leaves profiling-phase state behind would pass the current tests. For example, a cached _ctx_mla_kv_len_cap of None would silently disable context-MLA admission capping in serving.

Smallest practical scenario: in tests/unittest/_torch/executor/test_executor_resource_rebind.py, patch the heavy collaborators that _initialize_execution calls: model_engine.warmup, dist.barrier, ADPRouter.create, HangDetector, ExecutorRequestQueue and _emit_initial_stats. Then call release_resources() and then rebind_resources(...) with a fake PyExecutorResources. Assert that the fields above are reset and that start_worker was not called. This file is already collected by l0_h100.yml through the unittest/_torch/executor subtree.

As per path instructions: "For each new or materially changed observable behavior, determine whether this PR adds, updates, or clearly identifies an existing test that meaningfully exercises the change."

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Review comment at @tensorrt_llm/_torch/pyexecutor/py_executor.py around lines
1255 - 1273:
Add a test in the existing resource-rebind test suite that calls the real
PyExecutor._initialize_execution through release_resources() and
rebind_resources(), mocking only its heavy collaborators. Verify that
iter_counter, _ctx_mla_kv_len_cap, _disagg_coordinator, worker_started,
shutdown_event, and kv_cache_manager reflect the new phase and resources, and
that start_worker is not called.

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---

• 🪄 Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/py_executor.py:
- Around line 615-619: Update _initialize_execution to reload the
environment-configured profile start and stop iteration indexes when resetting
iter_counter, and restore profile_start_iters and profile_stop_iters so reused
executors honor TLLM_PROFILE_START_STOP in the new phase.

---

Nitpick comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/py_executor.py:
- Around line 1255-1273: Add a test in the existing resource-rebind test suite
that calls the real PyExecutor._initialize_execution through release_resources()
and rebind_resources(), mocking only its heavy collaborators. Verify that
iter_counter, _ctx_mla_kv_len_cap, _disagg_coordinator, worker_started,
shutdown_event, and kv_cache_manager reflect the new phase and resources, and
that start_worker is not called.

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📒 Files selected for processing: 6

• tensorrt_llm/_torch/pyexecutor/_util.py
• tensorrt_llm/_torch/pyexecutor/py_executor.py
• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tests/unittest/_torch/executor/kv_cache/test_kv_cache_estimation.py
• tests/unittest/_torch/executor/test_executor_resource_rebind.py
• tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py

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[eopXD]

The refactor looks good.

Can we improve further? If another resource is introduced, how do we know to add them to the PyExecutorResources? Can this refactor PR also bring-in fool-proof mechanisms for related resources?

[zhaoyangwang-nvidia]

The refactor looks good.

Can we improve further? If another resource is introduced, how do we know to add them to the PyExecutorResources? Can this refactor PR also bring-in fool-proof mechanisms for related resources?

Thanks for the suggestion! Added a small guardrail in this PR:

Documented which resources belong in PyExecutorResources and where phase initialization and teardown must be handled.
Extended the existing rebind test to require explicit coverage for every resource field and verify that rebinding replaces stale references, including optional resources set to None.
This keeps runtime behavior and teardown ordering unchanged. All 132 related CPU tests pass, and mutation checks confirmed that the test catches both an unaccounted-for field and an omitted binding.

This is not fully foolproof yet: it cannot detect a phase resource added directly to PyExecutor. In a follow-up PR, we plan to consolidate phase-resource ownership, reduce duplicate references, and centralize cleanup while preserving dependency ordering. That should make lifecycle handling less dependent on developers updating multiple places.

[zhaoyangwang-nvidia]

Hi @tongyuantongyu @zhaoyuanh-nvidia could you help to reivew this PR, thanks a lot.

[eopXD]

We plan to consolidate phase-resource ownership, reduce duplicate references, and centralize cleanup while preserving dependency ordering. That should make lifecycle handling less dependent on developers updating multiple places.

Sounds good. Looking forward to this! Thank you for the reply.

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tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py: 1

400-400: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Assert that the estimation path rebinds the final resources.

The success path cannot detect a missing rebind_resources call. _DummyPyExecutor.__init__ wraps the shared resources dict. build_managers(resources, False) then mutates that same dict. As a result, py_executor.resource_manager.get_resource_manager(KV_CACHE_MANAGER) returns the final KV cache manager even if the creator never calls rebind_resources. test_startup_allocation_phases[True] would still pass after a regression that drops Line 1296 of py_executor_creator.py.

Record the object returned by _create_py_executor_resources. When estimation runs, assert that py_executor.resource_manager is that object.

♻️ Proposed assertion

+    created_resources = []
+
     def _create_py_executor_resources(**kwargs):
         if ctx_chunk_configs is not None:
             ctx_chunk_configs.append(kwargs["ctx_chunk_config"])
-        return _DummyResourceManager(kwargs["resources"])
+        bound = _DummyResourceManager(kwargs["resources"])
+        created_resources.append(bound)
+        return bound
@@
     assert created_executors == [py_executor]
+    if created_resources:
+        assert created_resources == [py_executor.resource_manager]

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Review comment at
@tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py
at line 400:
Update the estimation-path test around _create_py_executor_resources to record
each returned resource manager, then assert that any created resource manager is
identical to py_executor.resource_manager after startup. Preserve the existing
executor assertion and only apply the new identity check when the estimation
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Review comments at
@tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py:
- Line 400: Update the estimation-path test around _create_py_executor_resources
to record each returned resource manager, then assert that any created resource
manager is identical to py_executor.resource_manager after startup. Preserve the
existing executor assertion and only apply the new identity check when the
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📒 Files selected for processing: 8

• tensorrt_llm/_torch/pyexecutor/_util.py
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• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tests/unittest/_torch/executor/test_executor_resource_rebind.py
• tests/unittest/_torch/executor/test_gpu_keepalive.py
• tests/unittest/_torch/executor/test_kv_connector_executor_lifetime.py
• tests/unittest/_torch/executor/test_pp_mpi_progress_thread.py
• tests/unittest/_torch/executor/test_py_executor_creator_mla_cache_reuse_sync.py

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