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[https://nvbugs/7053521][fix] Stop turning on the HF weight cache in reused test MPI pools - #22074
Reused MPI pools now inherit the suite environment. The default HF cache overlay and env_overlay API were removed.
Explicit TRTLLM_HF_WEIGHT_CACHE values still propagate through environment inheritance. The changed tests cover "0" and "1", but do not directly cover TRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES.
Prefetcher callers, worker-pool construction, and test doubles use the updated signatures consistently.
The GLM-5.2 test now uses the inherited cache setting. Load-time behavior should be monitored because cache-enabled and rank-striped loading have different performance characteristics.
QA Engineer Review
Five test-code files changed. No test-list files changed.
Renamed test_weight_cache_env_scoped_to_spawn to test_weight_cache_left_at_default.
Parameterized test_weight_cache_env_respects_user_setting for cache values "0" and "1".
Updated shadow-pool, in-flight-build, cache handover, default-environment, and GLM-5.2 accuracy coverage.
test_session_reuse.py and test_session_prefetcher.py are listed in tests/integration/test_lists/test-db/l0_cpu.yml. The GLM-5.2 accuracy test is listed in tests/integration/test_lists/test-db/l0_dgx_b300.yml with a timeout.
Reported validation includes 86 passes and seven failures. Coverage verdict: needs follow-up.
Per-File QA Perspective
tests/integration/defs/accuracy/modeling_v2/glm_moe_dsa/glm_5_2_nvfp4__sm_103__tep4/test_accuracy.py: Removes the forced cache-disable setting from test_gsm8k_identity_vs_mtp1_and_acceptance. The test is listed in l0_dgx_b300.yml; verify accuracy and runtime with the inherited environment.
tests/test_common/session_prefetcher.py: Removes environment-overlay handling from shadow builds. Its behavior is covered by the listed unittest/llmapi/test_session_prefetcher.py test.
tests/test_common/session_reuse.py: Reused pools now inherit environment variables without injected cache defaults. Its behavior is covered by the listed unittest/llmapi/test_session_reuse.py test.
tests/unittest/llmapi/test_session_prefetcher.py: Updates build stubs and removes overlay-forwarding coverage. The file is listed in l0_cpu.yml.
tests/unittest/llmapi/test_session_reuse.py: Verifies default cache absence, explicit cache values, shadow reuse, synchronous fallback, and restocking. The file is listed in l0_cpu.yml.
Description
Test session reuse (tests/test_common/session_reuse.py) spawned every MPI pool it manages with TRTLLM_HF_WEIGHT_CACHE=1 and TRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES=1. Production leaves this cache off. Most bare LLM(...) calls in the unit and integration test trees run on one of these pools, so most LLM API tests in CI loaded their checkpoints with the cache on.
With the cache on, HfWeightLoader behaves in two ways that production never does:
It treats rank-striped read-ahead as ineligible and loads natively, logging effective=native, fallback_reason=rank 0: the raw HF weight cache is enabled. checkpoint_io_policy="auto" otherwise selects rank-striped read-ahead for HF checkpoints.
On a hit it returns the cached SafeTensors tensors without the prefetch a miss runs. These tensors are mapped from the checkpoint files; the cache holds no copy of the data. When the page cache has dropped those pages, the hit's integrity check and the weight materialization read the checkpoint back through page faults. On the B300 nodes measured in https://nvbugs/7043301, the page cache had dropped most of the checkpoint 12-14 minutes after the first load prefetched it, while the Lustre client released its locks on the files.
Session reuse no longer sets the cache variables. Pool workers inherit the suite's environment and load checkpoints the way production does. An explicit TRTLLM_HF_WEIGHT_CACHE setting still reaches the workers.
The prefetcher's env_overlay parameter, which carried only this setting, is removed.
The GLM-5.2 SM103 TEP4 test no longer sets TRTLLM_HF_WEIGHT_CACHE=0. That is now the default, and setting it changed the environment, so the test retired the previous test's cached pool and spawned a new one.
HfWeightLoader and its opt-in cache are unchanged.
Test Coverage
tests/unittest/llmapi/test_session_reuse.py and tests/unittest/llmapi/test_session_prefetcher.py (both in l0_cpu.yml): 86 passed. With these tests and the previous tests/test_common, 7 fail, among them the new test_weight_cache_left_at_default, whose pool was spawned with {'TRTLLM_HF_WEIGHT_CACHE': '1', 'TRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES': '1'}.
Back-to-back loads on one reused pool, 4x B200, with the CI wheel of L0_PostMerge doc: document running CI stage locally #3060 (main 0fe3f26; the loader is unchanged since except for exception types). One pytest session loads Qwen3-235B-A22B NVFP4 (125 GB, TP4, EP4) three times, all from NFS with the checkpoint fully in the page cache. main and this change alternated, two sessions each. Times are rank 0's total_model_loading_seconds, and for loads 2-3 also the time until LLM(...) returned:
first load
loads 2-3
LLM(...) ready, loads 2-3
main
47.4-48.6 s, effective=native
21.1-22.3 s, cache hits
32.6-33.2 s
this change
46.2-46.8 s, effective=rank_striped_read_ahead
44.7-47.1 s
54.5-58.3 s
Every load after the first reused the session's pool, and every load generated text. When the node keeps the whole checkpoint cached between loads, as here, a hit saves about 23 s per reload of this 125 GB checkpoint, and the saving scales with checkpoint size. When the page cache drops the checkpoint between loads, a hit costs far more instead, as in the GLM-5.2 runs above.
The same with DeepSeek-V3-Lite BF16 (54 GB, TP4), one session each: main 7.2 s for the first load (native) and 3.2-3.3 s for the cache hits, LLM(...) ready in 13.1-14.2 s; this change 5.6 s for the first load and 5.3-5.5 s for the reloads, ready in 14.2-16.5 s.
Ten back-to-back DeepSeek-V3-Lite loads on one reused pool with this change all used rank-striped read-ahead and took 5.4-6.2 s each. After every LLM shutdown the pool's workers held the same 1090 threads and 810 open files, and their RSS stayed at 27.2-27.7 GiB.
CI: the standard pre-merge stages, which run their LLM API tests on reused pools, plus DGX_B300-4_GPUs-PyTorch-Post-Merge-1 and -2, which run the GLM-5.2 test.
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.
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.
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No actionable comments were generated in the recent review.
Walkthrough
MPI pool creation and shadow prefetching no longer apply HF weight-cache environment overrides. Tests check inherited or user-provided cache settings. The GLM-5.2 accuracy test no longer sets the cache variable.
Changes
MPI pool environment inheritance
Layer / File(s)
Summary
Pool creation and shadow prefetching tests/test_common/session_prefetcher.py, tests/test_common/session_reuse.py
The prefetcher no longer accepts environment overlays. Fresh and shadow pools are created without environment overrides. Snapshot capture and prefetch restocking remain.
Prefetcher tests no longer check overlay forwarding. Session-reuse tests check the default and explicit cache values. The GLM-5.2 accuracy test no longer sets the cache variable.
This change stops test MPI pools from forcing the HF weight cache setting, so reused workers inherit the suite environment, and explicit settings still reach them. It affects test infrastructure only. No actionable merge-blocking risk was found.
Security architecture risk: Minimal · up to cab48
The change aligns test checkpoint loading with production defaults while preserving explicit settings. No new access path, increased privilege, or material security risk was identified.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The demonstrated exposure is limited to checkpoint-loading behavior in workers managed by the test suite. Explicit cache configuration already came from the suite environment before this PR. Removing synthesized defaults does not expand which environment variables can be forwarded or grant new worker-launch authority.
Trust Boundaries and Controls
observed — The existing handoff controls remain: shadow adoption checks worker identities and configuration snapshots, cached reuse checks its snapshot and resets workers, and a shadow build timeout invalidates late publication while refusing a concurrent replacement bootstrap. A released wrapper also rejects a late abort that could otherwise terminate a pool already handed to another test.
Resilience and Maintainability Implications
observed — Failure containment retains its existing limits rather than gaining a stronger guarantee: retirement and drain use bounded waits, worker termination checks recorded process identities, and unidentified workers may remain until runtime teardown. Snapshot capture also still precedes the constructor's environment read. These pre-existing behaviors are not changed by removing cache defaults.
Pre-merge checks: 4
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The title names the main change: it stops session reuse from turning on the HF weight cache in reused test MPI pools. The changeset matches this. It removes the TRTLLM_HF_WEIGHT_CACHE and TRTLLM_HF_WE…
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The title names the main change: it stops session reuse from turning on the HF weight cache in reused test MPI pools. The changeset matches this. It removes the TRTLLM_HF_WEIGHT_CACHE and TRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES overrides in session_reuse.py, removes the env_overlay parameter from the prefetcher, and drops the GLM-5.2 test's TRTLLM_HF_WEIGHT_CACHE=0 setting. The title follows the repository template with an NVBugs ID and the [fix] type. It is 90 characters, slightly longer than ideal, but still clear.
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_FakePool reads the parent environment, so these assertions can pass if TRTLLM_HF_WEIGHT_CACHE stops reaching workers. Keep the isolated tests and add a real-worker test through the reuse entrypoint for unset, "0", and "1" values.
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Review comment at @tests/unittest/llmapi/test_session_reuse.py at line 294:
The isolated cache-setting assertions do not verify that the setting reaches a reused worker. Keep the existing tests and add a test using the reuse entrypoint with a real worker that checks the worker’s observed TRTLLM_HF_WEIGHT_CACHE value when unset and when set to "0" or "1"; use the existing worker setup and reuse symbols in this test module.
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only still-valid issues, skip the rest with a brief reason, keep changes
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Nitpick comments:
Review comments at @tests/unittest/llmapi/test_session_reuse.py:
- Line 294: The isolated cache-setting assertions do not verify that the setting
reaches a reused worker. Keep the existing tests and add a test using the reuse
entrypoint with a real worker that checks the worker’s observed
TRTLLM_HF_WEIGHT_CACHE value when unset and when set to "0" or "1"; use the
existing worker setup and reuse symbols in this test module.
---
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…reused test MPI pools
Test session reuse spawned every MPI pool it manages with
TRTLLM_HF_WEIGHT_CACHE=1, while production leaves the cache off. With the
cache on, HfWeightLoader skips rank-striped read-ahead, the default I/O
policy for HF checkpoints, and loads natively. A SafeTensors cache entry
holds tensors mapped from the checkpoint files rather than a copy, and a
hit returns them without the prefetch a miss runs. When the page cache
has dropped those pages, the hit reads the checkpoint back through page
faults: in the GLM-5.2 SM103 TEP4 test the second load took 1675 s
against 420 s for the first.
Reused pools now inherit the suite's environment, so their loads take the
production path. An explicit TRTLLM_HF_WEIGHT_CACHE setting still reaches
the workers.
Also remove the prefetcher's env_overlay channel, whose only use was
this setting, and the GLM-5.2 SM103 TEP4 test's opt-out, which now
matches the default.
Signed-off-by: qgai <qgai@nvidia.com>
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Dev Engineer Review
env_overlayAPI were removed.TRTLLM_HF_WEIGHT_CACHEvalues still propagate through environment inheritance. The changed tests cover"0"and"1", but do not directly coverTRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES.QA Engineer Review
test_schedule_shadow_passes_env_overlay_to_build.test_weight_cache_env_scoped_to_spawntotest_weight_cache_left_at_default.test_weight_cache_env_respects_user_settingfor cache values"0"and"1".test_session_reuse.pyandtest_session_prefetcher.pyare listed intests/integration/test_lists/test-db/l0_cpu.yml. The GLM-5.2 accuracy test is listed intests/integration/test_lists/test-db/l0_dgx_b300.ymlwith a timeout.Per-File QA Perspective
tests/integration/defs/accuracy/modeling_v2/glm_moe_dsa/glm_5_2_nvfp4__sm_103__tep4/test_accuracy.py: Removes the forced cache-disable setting fromtest_gsm8k_identity_vs_mtp1_and_acceptance. The test is listed inl0_dgx_b300.yml; verify accuracy and runtime with the inherited environment.tests/test_common/session_prefetcher.py: Removes environment-overlay handling from shadow builds. Its behavior is covered by the listedunittest/llmapi/test_session_prefetcher.pytest.tests/test_common/session_reuse.py: Reused pools now inherit environment variables without injected cache defaults. Its behavior is covered by the listedunittest/llmapi/test_session_reuse.pytest.tests/unittest/llmapi/test_session_prefetcher.py: Updates build stubs and removes overlay-forwarding coverage. The file is listed inl0_cpu.yml.tests/unittest/llmapi/test_session_reuse.py: Verifies default cache absence, explicit cache values, shadow reuse, synchronous fallback, and restocking. The file is listed inl0_cpu.yml.Description
Test session reuse (
tests/test_common/session_reuse.py) spawned every MPI pool it manages withTRTLLM_HF_WEIGHT_CACHE=1andTRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES=1. Production leaves this cache off. Most bareLLM(...)calls in the unit and integration test trees run on one of these pools, so most LLM API tests in CI loaded their checkpoints with the cache on.With the cache on,
HfWeightLoaderbehaves in two ways that production never does:effective=native, fallback_reason=rank 0: the raw HF weight cache is enabled.checkpoint_io_policy="auto"otherwise selects rank-striped read-ahead for HF checkpoints.In CI:
TestModelingV2Glm52Nvfp4Sm103Tep4::test_gsm8k_identity_vs_mtp1_and_acceptanceon 4x B300 loads one checkpoint twice on one reused pool. With the cache on it took 1788-2669 s in 18 runs (10-27 to 11-09) against its 2700 s limit. In L0_PostMerge doc: Update DeepSeekV3 doc #3052 it timed out: its second load, a cache hit, took 1675 s, against 420 s for the first (https://nvbugs/7043301). [https://nvbugs/7043301][test] Turn off the HF weight cache in the GLM-5.2 SM103 TEP4 GSM8K test #21692 turned the cache off for this test only; since then it has taken 930-1076 s in 9 runs.TestDeepSeekV4ProDSpark::test_gsm8k_dep4_megamoe_cutedslon 4x GB300 spent 924.7 s and 707.7 s in checkpoint preparation with the cache, against 27.1 s and 29.7 s without (https://nvbugs/6997728). [https://nvbugs/6997728][fix] Fix DeepSeek-V4-Pro DEP4 MegaMoE CuTe DSL test: skip HF weight cache for oversized checkpoints, drop hard GSM8K floor, fix bootstrap hang #21225 made the loader skip the cache for checkpoints larger than host memory.Change
TRTLLM_HF_WEIGHT_CACHEsetting still reaches the workers.env_overlayparameter, which carried only this setting, is removed.TRTLLM_HF_WEIGHT_CACHE=0. That is now the default, and setting it changed the environment, so the test retired the previous test's cached pool and spawned a new one.HfWeightLoaderand its opt-in cache are unchanged.Test Coverage
tests/unittest/llmapi/test_session_reuse.pyandtests/unittest/llmapi/test_session_prefetcher.py(both inl0_cpu.yml): 86 passed. With these tests and the previoustests/test_common, 7 fail, among them the newtest_weight_cache_left_at_default, whose pool was spawned with{'TRTLLM_HF_WEIGHT_CACHE': '1', 'TRTLLM_HF_WEIGHT_CACHE_MAX_ENTRIES': '1'}.Back-to-back loads on one reused pool, 4x B200, with the CI wheel of L0_PostMerge doc: document running CI stage locally #3060 (main 0fe3f26; the loader is unchanged since except for exception types). One pytest session loads Qwen3-235B-A22B NVFP4 (125 GB, TP4, EP4) three times, all from NFS with the checkpoint fully in the page cache.
mainand this change alternated, two sessions each. Times are rank 0'stotal_model_loading_seconds, and for loads 2-3 also the time untilLLM(...)returned:LLM(...)ready, loads 2-3maineffective=nativeeffective=rank_striped_read_aheadEvery load after the first reused the session's pool, and every load generated text. When the node keeps the whole checkpoint cached between loads, as here, a hit saves about 23 s per reload of this 125 GB checkpoint, and the saving scales with checkpoint size. When the page cache drops the checkpoint between loads, a hit costs far more instead, as in the GLM-5.2 runs above.
The same with DeepSeek-V3-Lite BF16 (54 GB, TP4), one session each:
main7.2 s for the first load (native) and 3.2-3.3 s for the cache hits,LLM(...)ready in 13.1-14.2 s; this change 5.6 s for the first load and 5.3-5.5 s for the reloads, ready in 14.2-16.5 s.Ten back-to-back DeepSeek-V3-Lite loads on one reused pool with this change all used rank-striped read-ahead and took 5.4-6.2 s each. After every
LLMshutdown the pool's workers held the same 1090 threads and 810 open files, and their RSS stayed at 27.2-27.7 GiB.CI: the standard pre-merge stages, which run their LLM API tests on reused pools, plus
DGX_B300-4_GPUs-PyTorch-Post-Merge-1and-2, which run the GLM-5.2 test.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-compatibleorapi-breaking. Forapi-breaking, includeBREAKINGin 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.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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