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[TRTLLM-16465][perf] Release unused attention workspace memory - #19051
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WalkthroughThe change adds native workspace-size reporting and eager workspace reclamation for eligible attention execution. It adds backend capability metadata, shrink-policy logic, model-engine integration, documentation, and CPU-focused tests. ChangesEager attention workspace reclamation
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant ModelEngine
participant EagerWorkspaceReclaimer
participant FallbackFmha
participant NativeAttention
ModelEngine->>EagerWorkspaceReclaimer: enter eligible eager forward
EagerWorkspaceReclaimer->>FallbackFmha: provide workspace_required_bytes
FallbackFmha->>NativeAttention: execute attention and report requirement
NativeAttention-->>EagerWorkspaceReclaimer: update required workspace bytes
EagerWorkspaceReclaimer->>EagerWorkspaceReclaimer: evaluate shrink policy
EagerWorkspaceReclaimer->>ModelEngine: finish forward with current storage
Suggested reviewers: Merge Risk: 🟡 Moderate · up to Automatic GPU workspace replacement lacks a real CUDA lifetime regression test, while two eligibility and safety checks remain ineffective. Resolve these coverage gaps before merging this memory-management change. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 11.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 35 functions across 11 files. (1 skipped: 1 unsupported.) ✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@tensorrt_llm/_torch/pyexecutor/model_engine.py`:
- Line 6856: Add CUDA regression coverage for the workspace-reclamation path
around reclaim_scope: enable eager workspace shrinking, warm up, execute three
underfilled supported fallback forwards on one stream, assert CUDA workspace
storage capacity decreases, then run another forward and validate its output.
Keep the test GPU-specific and verify actual storage shrinkage rather than only
CPU wiring.
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tensorrt_llm/_torch/attention/ATTENTION_DEVELOPER_GUIDE.mdtensorrt_llm/_torch/attention/backends/fmha/fallback.pytensorrt_llm/_torch/attention/backends/fmha/interface.pytensorrt_llm/_torch/attention/backends/trtllm.pytensorrt_llm/_torch/pyexecutor/model_engine.py
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Inline comments:
In `@tests/unittest/_torch/attention/test_eager_workspace.py`:
- Line 105: Update test_unsafe_paths_do_not_reclaim to initialize
_ScratchMetadata.cuda_graph_workspace with a distinct graph workspace tensor
before entering the is_cuda_graph scope, then assert the workspace remains the
identical tensor afterward. Ensure the setup makes both clearing and replacement
observable.
In `@tests/unittest/_torch/executor/test_eager_workspace_engine.py`:
- Line 68: The test test_ineligible_modes_and_workspaces_do_not_create_reclaimer
must cover the encoder-decoder eligibility guard by making
_is_encoder_decoder_model configurable or overriding it to return True when
exercising _freeze_eager_workspace_floor(). Assert that no reclaimer is created
for this case, while preserving the existing ineligible-mode and workspace
coverage.
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Inline comments:
In `@tests/unittest/_torch/attention/test_eager_workspace.py`:
- Line 64: Restore a CUDA-gated regression test for workspace replacement in the
eager attention tests, covering the third underfilled forward: queue same-stream
work that reads the old workspace, trigger replacement, synchronize the CUDA
stream, and verify the queued work completes correctly before the old allocation
can be reused. Keep the existing CPU/mock ownership coverage separate and do not
rely on it for asynchronous GPU lifetime behavior.
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Updated Moonlight-16B-A3B performance resultsRetested the local optimization that uses Single H200, TP=1, BF16 model / FP8 KV cache, eager execution with prefix reuse. Five independent model runs (~154 minutes), alternating paired reclamation-on/off trials using the same model instance. The workload and container match the previous Moonlight test; the GPU node changed, so these results should not be used to quantify improvement over the previous run.
Low/medium/high cycles contain one prefix-reuse burst followed by 100/10/1 short requests. The no-reclamation scenario includes 4,000 short requests per arm. These are serial end-to-end output-throughput measurements, not saturated serving throughput. The across-run 95% t intervals for paired latency change are:
All four intervals include zero: no statistically clear regression was detected in this experiment, which does not establish zero overhead or performance equivalence. Memory/correctness checks passed: real requests grew the workspace from 136.034 MiB to 264.226 MiB, and reclamation returned it to the warmup baseline, releasing 128.193 MiB of retained workspace capacity. Separate untimed checks verified 50 growths and 50 reclamations, and output-token equality checks passed. This is retained workspace reduction, not a new measurement of whole-model peak or allocator-reserved memory reduction. All five processes exited with code 0. The previously observed interpreter-shutdown |
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Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <265160349+zhaoyangwang-nvidia@users.noreply.github.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Replace CPU demand reporting with per-forward logical-size accumulation and rename the opt-out setting to TRTLLM_RECLAIM_WORKSPACE. Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
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7 participants
Description
Several runtime workspaces retain their high-water allocations instead of
shrinking after demand falls. Their peaks can occur at different times and
under different request shapes: attention may grow during one burst, while
another workspace grows during a later phase. Over a long-running service,
these buffers can therefore retain the sum of their historical peaks, even
when those peak demands were never simultaneous. Much of that retained memory
may be idle during typical traffic, leaving less headroom for subsequent
allocations and increasing the risk of OOM.
This PR introduces reclamation for eager attention scratch as the first step
toward reducing that accumulated high-water retention. The direction is to
extend reclamation to other growable workspace buffers in follow-up changes,
after checking each buffer's ownership, lifetime, stream usage, and CUDA graph
address requirements. This PR does not yet reclaim those other workspaces.
Eager attention scratch reclamation is enabled automatically after warmup
for supported execution paths, integrated into native sizing, metadata ownership,
and the PyTorch model engine. Set
TLLM_STRICT_WORKSPACE_MEMORY=0before engine initialization to disable reclamation if needed; the default is
1.a CPU scalar. No duplicated sizing formula or GPU synchronization.
max(warmup floor, window maximum requirement). Growth, exact-capacity demand,or failure resets the counter; no report leaves it unchanged.
CUDA graph storage is untouched. Only pure fallback FMHA scratch opts in;
unsupported backends/modes and stream changes disable reclamation.
Storage returns to PyTorch's allocator, not necessarily the CUDA driver. This
does not prevent peak OOM; alternating large/small requests can add overhead.
Three forwards is an initial heuristic. Existing backend, mode, and stream
safety exclusions remain in place; representative performance validation is still needed.
Test Coverage
441039154e: 8 CPU tests and 8 subtests passed with therebased workspace and engine modules in the existing built environment;
pre-commit passed for the full PR diff. The attention conflict preserves both
reclamation eligibility checks and main's failure-context logging. No new
native build or GPU/performance run was performed at that validation point; the subsequent GPU memory experiment at
2eb2788f78is reported in Section 4 below.injected 16 MiB excess scratch was reclaimed on the third underfilled forward,
while CUDA graph workspace identity, pointer, and size remained unchanged.
policy/ownership/engine tests and 8 subtests passed using the updated engine module in the existing
built environment; pre-commit passed. No new native build or GPU run for this
Python-only default-enablement change.
the internal invalid-parameter test and both GPU tests were removed.
All 8 remaining tests passed again after removal. GPU asynchronous lifetime
and native requirement reporting no longer have dedicated regression tests
in this PR; the L4 results above are historical validation before removal.
Long-running production stress and peak-OOM validation remain outside the completed test scope.
Attention Workspace Reclamation Test Report
Policy: reclaim excess capacity after three consecutive underfilled forwards, preserving the warmup baseline. Compare reclamation disabled/enabled (
TRTLLM_RECLAIM_WORKSPACE=0/1in the later memory experiments).Test Environment
Normal warmup; real requests triggered growth. No manual workspace resizing, reduced warmup baseline, or request-phase
empty_cache()calls.1. Performance
Five independent model runs, approximately 154 minutes total. Serial end-to-end output throughput, not saturated serving throughput.
Low/medium/high cycles: one prefix-reuse burst followed by 100/10/1 short requests. The no-reclamation scenario contains 4,000 short requests per arm.
No statistically clear performance regression was detected; this does not establish zero overhead or performance equivalence.
2. Memory Reclamation
Sequence: establish a 3,904-token prefix; submit 512 requests sharing it, each adding 16 tokens; run short requests to trigger reclamation; then run ordinary batches up to 16,384 new tokens.
The overall allocated peak occurred during the prefix-reuse burst, before reclamation.
3. Subsequent Buffer Growth
The async result is a single synthetic on/off pair within the same allocator backend. It is not evidence of a lower whole-sequence device-memory peak, nor a performance result. Benefits from switching allocators are not attributed to this PR.
4. Whole-Sequence Device Memory Reduction: Context Logits
Single H200, TP=1, DeepSeek-V3-Lite (FP8 weights / FP8 KV cache), eager Dense MLA + CUTLASS MoE. Tested the Python overlay at
2eb2788f78with the previously compiled Hopper libraries; no new native build. Normal warmup,max_num_tokens=1024,max_batch_size=64,max_seq_len=4096, KVmax_tokens=131072, KV fraction0.5, attended-KV cap131072, and batch wait1000 ms.No manual allocations, workspace resizing, altered warmup internals, stream changes, or request-phase
empty_cache(). The returned context logits use their normal API-managed lifetime; no extra tensors are retained artificially.Two independent async pairs were run in opposite orders (off/on, then on/off). Each pair uses the same allocator backend, configuration, and requests, changing only reclamation. The 544–608 MiB device-peak reduction is an on/off benefit within cudaMallocAsync, not a comparison against native. Native showed a 600 MiB allocated-peak reduction but no reserved/device-peak reduction for this workload.
NVML was sampled every 5 ms, so these are observed peaks rather than a guarantee of capturing every transient. The highest samples occurred in the final logits phase; including observed startup/warmup does not change these device peaks. End-of-phase driver usage independently showed the same 544 / 608 MiB differences. Async reserved capacity varied by about 32 MiB before reclamation; pool granularity and run variation mean the measured savings need not equal the exact 600 MiB workspace reduction.
All six processes exited with code 0; paired output tokens matched. Context-logits shapes/dtypes matched and values were finite (checked on CPU), but full logits were not compared elementwise. The known interpreter-shutdown cleanup exception still occurred after measurement. This is a constructed, legal logits-heavy workload, not production traffic or a performance benchmark; it does not establish a higher OOM limit or justify changing the default allocator.
Correctness and Measurement Scope
allocatedmeasures live PyTorch allocations;reservedmeasures allocator-pool capacity. Neither is total device memory usage. Workspace capacity is measured withuntyped_storage().nbytes().441039154e(optimization originally8224fe4ed9, rebased asfdca0add0d). Section 4 tests the Python overlay at2eb2788f78with previously built native libraries; performance was not remeasured in that experiment.Conclusion
Reclamation demonstrably reduces retained workspace memory and can also reduce whole-sequence GPU memory peaks under suitable workload and allocator conditions. In the context-logits experiment, two independent cudaMallocAsync pairs released 600 MiB of retained workspace and reduced both reserved peaks and sampled device-memory peaks by 544 / 608 MiB. The same workload under native reduced allocated peaks but not reserved/device peaks. Released capacity need not immediately return to the driver to reduce later pool growth.
The earlier Moonlight performance tests detected no statistically clear regression. The new memory experiment is a constructed workload, not evidence of universal savings, production representativeness, zero overhead, or increased capacity before OOM.
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/bot help.Dev Engineer Review
EagerWorkspaceReclaimerfreezes a minimum capacity, tracks native workspace requirements, and replaces storage after three underfilled forwards.int64tensor for native workspace requirement reporting.TLLM_STRICT_WORKSPACE_MEMORY=0disables it.QA Engineer Review
tests/unittest/_torch/attention/test_eager_workspace.py.tests/unittest/_torch/executor/test_eager_workspace_engine.py.WorkspaceShrinkPolicy,EagerWorkspaceReclaimer, engine initialization, forward-scope usage, warmup bypass, unsupported modes, empty workspaces, non-reclaimable workspaces, failure cleanup, and overlapping forwards.Per-File QA Perspective
cpp/tensorrt_llm/nanobind/thop/bindings.cpp: Exposes the optionalworkspace_required_bytesargument. Verify default behavior and Python binding compatibility.cpp/tensorrt_llm/thop/attentionOp.cpp: Validates and updates the host CPUint64workspace requirement tensor. Verify contiguous, single-element, device, and accumulation behavior.cpp/tensorrt_llm/thop/attentionOp.h: Updates the public C++ API with an optional argument. Verify callers remain source-compatible.tensorrt_llm/_torch/attention/ATTENTION_DEVELOPER_GUIDE.md: Documents automatic enablement, eligibility, capacity rules, and validation boundaries.tensorrt_llm/_torch/attention/backends/fmha/fallback.py: Enables reclamation support for fallback FMHA and passes workspace metadata. Verify only supported fallback paths enable reclamation.tensorrt_llm/_torch/attention/backends/fmha/interface.py: Adds a default-disabled reclamation capability flag. Verify other FMHA backends remain excluded.tensorrt_llm/_torch/attention/backends/trtllm.py: Adds host workspace sizing and non-reclaimable state to metadata. Verify eager forwards update state while CUDA-graph forwards do not.tensorrt_llm/_torch/attention/workspace.py: Adds warmup tracking, stream and metadata safeguards, delayed shrinking, and storage replacement. Verify asynchronous lifetime safety and allocator behavior on CUDA.tensorrt_llm/_torch/pyexecutor/model_engine.py: Enables reclamation by default with an environment-variable escape hatch for eligible engines. Verify unsupported modes and workspaces do not create a reclaimer.tests/unittest/_torch/attention/test_eager_workspace.py: Covers policy and reclaimer behavior with CPU mocks. It is not reported in an integration test list.tests/unittest/_torch/executor/test_eager_workspace_engine.py: Covers engine eligibility and forward-scope integration. It is not reported in an integration test list.