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[TRTLLM-6904][feat] add timing metrics for input processing and multimodal encoding - #20739
Expose server input preparation and multimodal encoder timing through Prometheus, with CPU input timings retained in existing request JSONL and opt-in Server-Timing / SSE output.
Measure total input preparation through synchronous preprocessing and request submission, excluding the wait for generation.
Separate media resolution, chat rendering, processor execution/queue, and per-item HTTP download/decode/queue by modality.
Exclude health-generation probes from input histograms and shared HTTP session setup from download timing. Anthropic Messages total starts in the delegated chat handler. Encoder-only serving exposes the same Prometheus and completed-request timing paths; process-pool media loading retains picklable submissions.
Preserve input timings across context/generation handoff. HTTP requests and background Message Batch items use separate contexts and completed-request records.
Reuse CUDA events and iteration statistics for rank-0 encoder call/batch timing without adding GPU synchronization or collectives.
Use existing return_perf_metrics, enable_iter_perf_stats and perf_metrics_output_dir options. Stages can overlap; their averages are not additive. Encoder GPU time remains a batch measurement.
Test Coverage
CodeRabbit follow-up f51b20d448: 54 serving/media CPU tests passed with current serving/media modules in a release runtime. Three regressions reproduced before the fix: process-pool serialization, the encoder-only Prometheus endpoint and request timing export. 5 isolated callback-gate cases passed using the current method in the release executor class; full rebased executor/GPU validation still requires matching native binaries. Pre-commit passed.
Rebased 09b94b583d onto main b752bf4691; preserved main's request validation, NCCL window lifetime handling and asynchronous metrics collection. Pre-commit passed.
The earlier 41-test media/request subset is included in the current CPU run. Full-source/GPU validation was not rerun: the available native build predates main's NCCL window operators.
Reuse the existing cache-hit test to check encoder timing hooks; its cpu_only mark enables that coverage in L0 CPU CI.
Earlier whole-PR validation: 568 CPU tests passed; 22 tokenizer-dependent cases skipped on e55226305b after rebase onto main af3e5eb257; pre-commit passed. Metric regressions are registered in CPU CI.
Prior serving/export validation (d595f0159f), H200 / Qwen3-VL-8B-Instruct-FP8: HTTP download, inline/multiple images, streaming, text-only, Responses and mixed Message Batch requests; Prometheus, request JSONL and response timing values matched where available. AIPerf exported all four histogram families.
Instrumentation overhead
return_perf_metrics=False, enable_iter_perf_stats=False and no JSONL output directory are the defaults. ON below enables both existing statistics flags, so ON/OFF includes existing collection costs as well as the new timers.
Base af3e5eb257 vs PR e55226305b: AIPerf 0.8.0, Qwen3-VL-8B-Instruct-FP8, TP1 on one H200 NVL. Fixed max batch 64, max tokens/sequence 8192, decoder CUDA graphs [1,2,4,8,16,32,64], and no KV/encoder reuse. JSONL, requested response timing and profiling-time metrics scraping were disabled.
Long image-concurrency-64 validation: 65,536/65,536 successful measured requests, all 16 output tokens. Two balanced repeats per condition, 8,192 measured requests and 512 excluded warmups per repeat. Client and server used separate CPU affinities.
Condition
Mean TTFT
Mean req/s
Req/s per repeat
Base OFF
212.63 ms
68.82
67.63, 70.00
PR OFF
216.23 ms
67.87
70.62, 65.11
Base ON
220.89 ms
64.52
66.73, 62.32
PR ON
236.12 ms
67.19
65.75, 68.63
Base → PR OFF: +3.60 ms TTFT (+1.69%), -1.38% req/s. With both ON: +15.23 ms TTFT (+6.90%), +4.13% mean req/s. The ON throughput comparison changed sign between repeats; the earlier short C64 decrease did not reproduce consistently. These results do not establish zero overhead or an incremental throughput gain.
The initial text/image concurrency 1/8/64/128 screen completed another 26,624 successful requests. Image C1 PR OFF → ON TTFT was 70.22 → 72.37 ms (+2.14 ms, +3.05%); C128 was 867.10 → 886.15 ms (+19.06 ms, +2.20%). Text C64 ON throughput was 191.56 → 191.16 req/s (-0.21%) from base to PR, despite a larger total ON/OFF statistics cost. JSONL/SSE export overhead was not measured.
PR Checklist
Reviewed coding guidelines, tests, documentation, DCO sign-off and backward-compatible API labeling. No new dependencies or configuration fields.
Dev Engineer Review
The change adds request-scoped input-processing timings, Prometheus histograms for input stages and media work, JSONL and Server-Timing export, and encoder GPU-time reporting. The timing context covers preprocessing and request submission, not generation wait. The instrumentation also covers encoder-only serving and background batch requests.
The implementation adds executor-aware media timing and avoids timing decode work in process pools. Encoder timings use CUDA events and iteration statistics without extra GPU synchronization or collectives. Stage averages can overlap, and encoder GPU time is a batch measurement; consumers must not treat these values as exclusive request latency.
The main verification risk is the unvalidated GPU and executor path: the author reports that full rebased executor/GPU validation was not run because matching native binaries were unavailable. The author reports passing CPU and callback-gate tests, but JSONL/SSE export overhead was not measured. The reported performance results varied between repeats when statistics were enabled.
QA Engineer Review
Changed tests cover request and media timing, failure paths, request isolation, context and JSONL/header export, encoder callback gates and CUDA-event collection, rank fanout, and legacy stats serialization. The CPU CI list adds targeted media cases, the OpenAI input-metrics test file, and selected request-metrics cases. No manual-QA list registration is shown.
Coverage verdict: needs follow-up. The reported CPU coverage is useful, but full GPU/executor validation and export-overhead measurement remain outstanding.
Per-File QA Perspective
docs/source/features/multi-modality.md — Documents serving options, histogram scopes, exports, limitations, and a scraping example. Verify the documented names and timing boundaries match the implementation.
tensorrt_llm/_torch/models/modeling_multimodal_mixin.py — Routes encoder paths through an optional context hook. Verify full-batch, scheduled, partial-cache, and prefetch paths preserve behavior with and without the hook.
tensorrt_llm/_torch/pyexecutor/perf_metrics_manager.py — Adds non-blocking CUDA-event timing collection and event reuse. Verify pending, failed, and completed event paths on supported GPU execution.
tensorrt_llm/_torch/pyexecutor/py_executor.py — Installs encoder timing conditionally and adds samples to iteration statistics. Verify callback gates, rank-0 behavior, and attention-DP fanout.
tensorrt_llm/executor/base_worker.py — Serializes optional encoder timing while retaining compatibility with older stats tuples. Verify both tuple shapes.
tensorrt_llm/inputs/media_io.py — Adds download, decode-queue, and decode timing by modality, with special handling for process pools. Verify HTTP, data, and file inputs and decode failures.
tensorrt_llm/inputs/utils.py — Times media retrieval and threaded chat-template rendering; returns early when no media data is pending. Verify empty and populated retrieval paths.
tensorrt_llm/metrics/collector.py — Adds input-stage, processor-queue, media, and encoder histograms. Verify stage classification, modality labels, and milliseconds-to-seconds conversion.
tensorrt_llm/metrics/perf_utils.py — Adds context-local timing helpers that record in finally when an observer exists. Verify exception handling and disabled-observer behavior.
tensorrt_llm/serve/_perf_metrics_schema.py — Adds optional input_processing_metrics to PerfMetrics. Verify compatibility with records that omit the field.
tensorrt_llm/serve/openai_server.py — Instruments request preprocessing and submission across several endpoints and adds a timed processor helper. Verify error paths, generation handoff, batch isolation, and encoder-only requests.
tests/integration/test_lists/test-db/l0_cpu.yml — Adds three targeted media tests, the OpenAI input-metrics test file, and two input-metrics request tests while retaining two KV-transfer selectors. These entries register the changes for CPU CI.
tests/unittest/_torch/executor/test_perf_metrics_manager.py — Covers timing callback gates, event readiness and reuse, failures, and rank fanout. The file is included through the unittest/_torch/executor CI entry.
tests/unittest/_torch/multimodal/test_multimodal_mixin.py — Verifies the encoder context hook runs once across repeated cache lookups. The file is included through the unittest/_torch/multimodal CI entry.
tests/unittest/executor/test_stats_serializer.py — Covers populated and empty encoder samples and legacy tuple serialization. The file is included through the unittest/executor/test_stats_serializer.py CI entry.
tests/unittest/llmapi/apps/test_media_io.py — Covers modality timing, failure recording, and process-pool picklability. Three targeted test functions are listed in CPU CI.
tests/unittest/llmapi/apps/test_openai_input_metrics.py — Covers processor timing, errors, disabled timing, route registration, endpoint totals, and batch isolation. The test file is listed in CPU CI.
tests/unittest/llmapi/apps/test_request_metrics.py — Covers header, disaggregation, JSONL, and request-local timing behavior. Two existing KV-transfer selectors and two new input-metrics selectors are listed in CPU CI.
tests/unittest/metrics/test_collector.py — Covers encoder, input-stage, queue, and per-modality histogram observations. The file is included through the unittest/metrics CI entry.
yechank-nvidia
changed the title
[TRTLLM-6904][feat] expose input processing and encoder timing
[TRTLLM-6904][feat] add timing metrics for input processing and multimodal encoding
Oct 22, 2026
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Change: Feature
Merge Risk: Minimal · previous review 076cb
The previously identified media-decoding and encoder-timing export risks are addressed. No actionable merge-blocking risk remains after normal checks.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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Docstring Coverage
Docstring coverage is 33.62% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 116 functions across 19 files. (2 skipped…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
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Explanation
Title check
The title clearly identifies the ticket, feature type, and primary change: timing metrics for input processing and multimodal encoding.
Description check
The description is complete and relevant. It explains the purpose, implementation scope, limitations, test coverage, performance impact, and checklist status.
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Check skipped because no linked issues were found for this pull request.
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Full details: Docstring Coverage
Explanation
Docstring coverage is 33.62% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 116 functions across 19 files. (2 skipped: 2 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches
🧪 Generate unit tests (beta)
Create a new PR
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Reviewing files that changed from the base of the PR and between f51b20d and 076cbca.
Files selected for processing: 6
docs/source/features/multi-modality.md
tensorrt_llm/_torch/pyexecutor/py_executor.py
tensorrt_llm/executor/base_worker.py
tensorrt_llm/inputs/utils.py
tensorrt_llm/serve/openai_server.py
tests/integration/test_lists/test-db/l0_cpu.yml
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The existing tests cover record_encoder_forward and multimodal callback use, but they do not exercise the production gate that assigns the callback. Add a focused test for rank 0 with iteration statistics enabled and a multimodal model. Also cover that disabled statistics and nonzero ranks do not install the callback.
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Review comment at @tensorrt_llm/_torch/pyexecutor/py_executor.py around lines
940 - 942:
Add a focused test for the callback installation gate in the PyExecutor path
that assigns `_encoder_forward_context`. Verify that rank 0 with iteration
statistics enabled and a multimodal model installs `record_encoder_forward`,
while disabled statistics or a nonzero rank leaves the callback uninstalled.
This PR adds a copyright and license header to l0_cpu.yml. Files under tests/integration/test_lists/ are plain-text test-list data. By repository convention they carry no copyright or license header. The new entries on Lines 197-211 are valid selectors. Drop Lines 1-2 to keep the file consistent with the other lists.
Proposed change
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.-# SPDX-License-Identifier: Apache-2.0
As per path instructions: "By repository convention, they carry no NVIDIA copyright/license header."
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Review comment at @tests/integration/test_lists/test-db/l0_cpu.yml around lines
1 - 2:
Remove the two SPDX header lines from the l0_cpu.yml test-list file, leaving its
selectors unchanged.
Source: Path instructions
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Nitpick comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/py_executor.py:
- Around line 940-942: Add a focused test for the callback installation gate in
the PyExecutor path that assigns `_encoder_forward_context`. Verify that rank 0
with iteration statistics enabled and a multimodal model installs
`record_encoder_forward`, while disabled statistics or a nonzero rank leaves the
callback uninstalled.
Review comments at @tests/integration/test_lists/test-db/l0_cpu.yml:
- Around line 1-2: Remove the two SPDX header lines from the l0_cpu.yml
test-list file, leaving its selectors unchanged.
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Approving — the comments below are optional touch-ups, not blockers.
The ContextVar design and the event-pool handling both hold up on inspection: pending encoder pairs never re-enter the shared pool until query() succeeds, and copy_context().run is the right fix for run_in_executor dropping request context. Tests cover both lifecycles plus the batch-item isolation. Remaining comments are about what the exported numbers mean, not correctness.
Two whole-PR notes:
/health_generate reuses openai_chat and passes through PerfMetricsMiddleware, so periodic liveness probes will populate trtllm_input_processing_seconds{stage="total"} (and processor/processor_queue) with sub-millisecond samples. The existing request histograms already include probes via _extract_metrics, so this is consistent, but since these new histograms are explicitly for bottleneck attribution it is worth a sentence in the docs or a follow-up to skip probe requests.
Unrelated hunks: the SPDX header added to l0_cpu.yml and the cpu_only mark on one pre-existing test in test_multimodal_mixin.py. Harmless, but neither is mentioned in the description.
Notes on lines outside the diff:
tensorrt_llm/serve/openai_server.py:2439: anthropic_messages delegates to openai_chat, so its total sample starts inside openai_chat and excludes convert_anthropic_request plus the Anthropic-side validation above. The docs list chat/completion/encoder/Harmony/Responses as the handlers whose entry starts total; either add this route to that list with the caveat, or start the timer here and pass through (the inner openai_chat timer would then need to be suppressed to avoid a double total observation).
Keep process-pool media loads on a picklable submission path.
When BaseMediaIO.set_executor receives a ProcessPoolExecutor and input-processing timing is active for a request, _run_in_executor submits copy_context().run with the local decode closure. A process pool must pickle submitted callables, and a nested function cannot be pickled. HTTP, data, and file media loads on that path then fail. The previous direct submission of self.load_bytes, self.load_base64, and self.load_file did not use a nested closure or a Context object.
The repository configures only a ThreadPoolExecutor, so this affects deployments that pass a process pool. The executor parameter is typed as the general Executor, so a process pool is an accepted input at the call site.
- if queued is None:+ if queued is None or isinstance(BaseMediaIO._executor, ProcessPoolExecutor):
return await loop.run_in_executor(BaseMediaIO._executor, fn, *args)
This fix keeps the direct submission for process pools. It removes the decode_queue and decode timings for that configuration. Thread-pool and default-executor behavior is unchanged.
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Review comment at @tensorrt_llm/inputs/media_io.py at line 1056:
Update BaseMediaIO._run_in_executor to submit the original callable and
arguments directly when the configured executor is a ProcessPoolExecutor, rather
than wrapping them in copy_context().run or a local decode closure. Preserve the
current context-propagation and timing behavior for thread-pool and default
executors.
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Outside diff comments:
Review comments at @tensorrt_llm/inputs/media_io.py:
- Line 1056: Update BaseMediaIO._run_in_executor to submit the original callable
and arguments directly when the configured executor is a ProcessPoolExecutor,
rather than wrapping them in copy_context().run or a local decode closure.
Preserve the current context-propagation and timing behavior for thread-pool and
default executors.
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review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
🚧 Files skipped from review as they are similar to previous changes: 2
docs/source/features/multi-modality.md
tensorrt_llm/_torch/pyexecutor/py_executor.py
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…cess pools
Mount the existing Prometheus endpoint for encoder-only serving and publish completed request metrics through the shared extraction path. Keep process-pool media submissions picklable and test the optional encoder timing callback gate, including compiled models.
Signed-off-by: Yechan Kim <161688079+yechank-nvidia@users.noreply.github.com>
Restore the CPU list entries that the original change added for the
new input timing tests, so CI keeps running them after the rebase.
Signed-off-by: Yechan Kim <161688079+yechank-nvidia@users.noreply.github.com>
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Description
Expose server input preparation and multimodal encoder timing through Prometheus, with CPU input timings retained in existing request JSONL and opt-in
Server-Timing/ SSE output.totalstarts in the delegated chat handler. Encoder-only serving exposes the same Prometheus and completed-request timing paths; process-pool media loading retains picklable submissions.Use existing
return_perf_metrics,enable_iter_perf_statsandperf_metrics_output_diroptions. Stages can overlap; their averages are not additive. Encoder GPU time remains a batch measurement.Test Coverage
f51b20d448: 54 serving/media CPU tests passed with current serving/media modules in a release runtime. Three regressions reproduced before the fix: process-pool serialization, the encoder-only Prometheus endpoint and request timing export. 5 isolated callback-gate cases passed using the current method in the release executor class; full rebased executor/GPU validation still requires matching native binaries. Pre-commit passed.09b94b583donto mainb752bf4691; preserved main's request validation, NCCL window lifetime handling and asynchronous metrics collection. Pre-commit passed.cpu_onlymark enables that coverage in L0 CPU CI.e55226305bafter rebase onto mainaf3e5eb257; pre-commit passed. Metric regressions are registered in CPU CI.d595f0159f), H200 / Qwen3-VL-8B-Instruct-FP8: HTTP download, inline/multiple images, streaming, text-only, Responses and mixed Message Batch requests; Prometheus, request JSONL and response timing values matched where available. AIPerf exported all four histogram families.Instrumentation overhead
return_perf_metrics=False,enable_iter_perf_stats=Falseand no JSONL output directory are the defaults. ON below enables both existing statistics flags, so ON/OFF includes existing collection costs as well as the new timers.Base
af3e5eb257vs PRe55226305b: AIPerf 0.8.0, Qwen3-VL-8B-Instruct-FP8, TP1 on one H200 NVL. Fixed max batch 64, max tokens/sequence 8192, decoder CUDA graphs[1,2,4,8,16,32,64], and no KV/encoder reuse. JSONL, requested response timing and profiling-time metrics scraping were disabled.Long image-concurrency-64 validation: 65,536/65,536 successful measured requests, all 16 output tokens. Two balanced repeats per condition, 8,192 measured requests and 512 excluded warmups per repeat. Client and server used separate CPU affinities.
Base → PR OFF: +3.60 ms TTFT (+1.69%), -1.38% req/s. With both ON: +15.23 ms TTFT (+6.90%), +4.13% mean req/s. The ON throughput comparison changed sign between repeats; the earlier short C64 decrease did not reproduce consistently. These results do not establish zero overhead or an incremental throughput gain.
The initial text/image concurrency 1/8/64/128 screen completed another 26,624 successful requests. Image C1 PR OFF → ON TTFT was 70.22 → 72.37 ms (+2.14 ms, +3.05%); C128 was 867.10 → 886.15 ms (+19.06 ms, +2.20%). Text C64 ON throughput was 191.56 → 191.16 req/s (-0.21%) from base to PR, despite a larger total ON/OFF statistics cost. JSONL/SSE export overhead was not measured.
PR Checklist
Dev Engineer Review
The change adds request-scoped input-processing timings, Prometheus histograms for input stages and media work, JSONL and
Server-Timingexport, and encoder GPU-time reporting. The timing context covers preprocessing and request submission, not generation wait. The instrumentation also covers encoder-only serving and background batch requests.The implementation adds executor-aware media timing and avoids timing decode work in process pools. Encoder timings use CUDA events and iteration statistics without extra GPU synchronization or collectives. Stage averages can overlap, and encoder GPU time is a batch measurement; consumers must not treat these values as exclusive request latency.
The main verification risk is the unvalidated GPU and executor path: the author reports that full rebased executor/GPU validation was not run because matching native binaries were unavailable. The author reports passing CPU and callback-gate tests, but JSONL/SSE export overhead was not measured. The reported performance results varied between repeats when statistics were enabled.
QA Engineer Review
Changed tests cover request and media timing, failure paths, request isolation, context and JSONL/header export, encoder callback gates and CUDA-event collection, rank fanout, and legacy stats serialization. The CPU CI list adds targeted media cases, the OpenAI input-metrics test file, and selected request-metrics cases. No manual-QA list registration is shown.
Coverage verdict: needs follow-up. The reported CPU coverage is useful, but full GPU/executor validation and export-overhead measurement remain outstanding.
Per-File QA Perspective
docs/source/features/multi-modality.md— Documents serving options, histogram scopes, exports, limitations, and a scraping example. Verify the documented names and timing boundaries match the implementation.tensorrt_llm/_torch/models/modeling_multimodal_mixin.py— Routes encoder paths through an optional context hook. Verify full-batch, scheduled, partial-cache, and prefetch paths preserve behavior with and without the hook.tensorrt_llm/_torch/pyexecutor/perf_metrics_manager.py— Adds non-blocking CUDA-event timing collection and event reuse. Verify pending, failed, and completed event paths on supported GPU execution.tensorrt_llm/_torch/pyexecutor/py_executor.py— Installs encoder timing conditionally and adds samples to iteration statistics. Verify callback gates, rank-0 behavior, and attention-DP fanout.tensorrt_llm/executor/base_worker.py— Serializes optional encoder timing while retaining compatibility with older stats tuples. Verify both tuple shapes.tensorrt_llm/inputs/media_io.py— Adds download, decode-queue, and decode timing by modality, with special handling for process pools. Verify HTTP, data, and file inputs and decode failures.tensorrt_llm/inputs/utils.py— Times media retrieval and threaded chat-template rendering; returns early when no media data is pending. Verify empty and populated retrieval paths.tensorrt_llm/metrics/collector.py— Adds input-stage, processor-queue, media, and encoder histograms. Verify stage classification, modality labels, and milliseconds-to-seconds conversion.tensorrt_llm/metrics/perf_utils.py— Adds context-local timing helpers that record infinallywhen an observer exists. Verify exception handling and disabled-observer behavior.tensorrt_llm/serve/_perf_metrics_schema.py— Adds optionalinput_processing_metricstoPerfMetrics. Verify compatibility with records that omit the field.tensorrt_llm/serve/openai_server.py— Instruments request preprocessing and submission across several endpoints and adds a timed processor helper. Verify error paths, generation handoff, batch isolation, and encoder-only requests.tensorrt_llm/serve/perf_metrics.py— Adds request-local timing collection, observer middleware, header formatting/reconstruction, and JSONL export. Verify probe exclusion, concurrent request isolation, and header behavior.tests/integration/test_lists/test-db/l0_cpu.yml— Adds three targeted media tests, the OpenAI input-metrics test file, and two input-metrics request tests while retaining two KV-transfer selectors. These entries register the changes for CPU CI.tests/unittest/_torch/executor/test_perf_metrics_manager.py— Covers timing callback gates, event readiness and reuse, failures, and rank fanout. The file is included through theunittest/_torch/executorCI entry.tests/unittest/_torch/multimodal/test_multimodal_mixin.py— Verifies the encoder context hook runs once across repeated cache lookups. The file is included through theunittest/_torch/multimodalCI entry.tests/unittest/executor/test_stats_serializer.py— Covers populated and empty encoder samples and legacy tuple serialization. The file is included through theunittest/executor/test_stats_serializer.pyCI entry.tests/unittest/llmapi/apps/test_media_io.py— Covers modality timing, failure recording, and process-pool picklability. Three targeted test functions are listed in CPU CI.tests/unittest/llmapi/apps/test_openai_input_metrics.py— Covers processor timing, errors, disabled timing, route registration, endpoint totals, and batch isolation. The test file is listed in CPU CI.tests/unittest/llmapi/apps/test_request_metrics.py— Covers header, disaggregation, JSONL, and request-local timing behavior. Two existing KV-transfer selectors and two new input-metrics selectors are listed in CPU CI.tests/unittest/metrics/test_collector.py— Covers encoder, input-stage, queue, and per-modality histogram observations. The file is included through theunittest/metricsCI entry.