feat: make 4096-token prompt ceiling explicit + experimental findings#60
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nmrenyi wants to merge 12 commits into
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feat: make 4096-token prompt ceiling explicit + experimental findings#60nmrenyi wants to merge 12 commits into
nmrenyi wants to merge 12 commits into
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The Kotlin EngineConfig accepts a `maxNumTokens` parameter that defaults to null. The previous buildEngine() call left it null, so the prompt ceiling that nurses and midwives ran into at high k was an inferred property of the stack rather than a stated choice in source. Pass `maxNumTokens = 4096` explicitly. This makes the ceiling visible at the call site and self-documents the deployment constraint — anyone reading RagPipeline.kt can now find the number directly instead of grepping LiteRT-LM error strings. Why 4096 specifically (verified empirically on 2026-05-16): - Lower-bound test: passing 2048 clamps the engine to 2048 and the error message reports `>= 2048`, so the value really is plumbed through to the native runtime, not a no-op. - Upper-bound test: passing 8192 does NOT cause an init failure — the artifact does not enforce a hard 4096 cap. However, on the GPU backend the model produces degenerate output (repetition loops) for prompts past 4096 tokens, even when no error is thrown. CPU stays coherent in the same scenario. - 4096 is therefore the highest cross-backend-safe value for this artifact family. Full experimental notes will land in latency_report_v2.md in the next commit.
Expand the "Errors and the 4096-token context wall" section with the findings from the 2026-05-16 Phase B/C experiments on the OnePlus OPD2413 test device: - The cap is enforced in liblitertlm_jni.so (native runtime), confirmed by extracting the AAR and locating the error template. - Passing maxNumTokens=2048 actually clamps to 2048 on both backends (lower-bound knob check). - Passing maxNumTokens=8192 succeeds at init on both backends — the 4096 ceiling is therefore NOT artifact-baked, contrary to the prior claim in this section. - At 8192, k=20 prompts now run end-to-end. CPU output stays clean; GPU output degenerates into a repetition loop past the 4096-token boundary. Update Key finding #5 to reflect the corrected story: 4096 isn't a hard runtime cap, it's the highest cross-backend-safe value. The deployment ceiling is driven by GPU output quality, not by an init- time or runtime constraint. Both updates land in aggregate_k_sweep.py (the canonical source) and regenerate the markdown report.
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Pull request overview
This PR makes the LiteRT-LM 4096-token ceiling explicit in the Android RAG engine setup and updates the latency report plus its generator to document the experimental basis for keeping that ceiling.
Changes:
- Sets
maxNumTokens = 4096explicitly when constructingEngineConfig. - Updates the generated latency report with findings from 2048/8192 token-limit experiments.
- Keeps
aggregate_k_sweep.pyin sync so future regenerated reports preserve the updated explanation.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
app/android/app/src/main/kotlin/com/example/app/RagPipeline.kt |
Makes the LLM token ceiling explicit in EngineConfig with explanatory context. |
evaluation/aggregate_k_sweep.py |
Updates generated report text for the 4096-token context-wall findings. |
evaluation/reports/latency_report_v2.md |
Regenerates/updates the latency report with the new experimental conclusion. |
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… context A standalone investigation note documenting the follow-up to PR #60's Phase A/B/C experiments. Combines two pieces of work: Step 4 — Source-code precision check (LiteRT-LM OSS + native lib): - GPU on Android defaults to FP16 for text decoder activations. Verified from a literal string in liblitertlm_jni.so: "System's default activation type for Text decoder is fp16. Vision encoder and audio encoder default is fp32." - CPU runs FP32 via XNNPACK. No FP16 attention kernels in CPU paths. - Our Kotlin code doesn't override the default — we are running on whatever the system picks per backend. - Side finding: maxNumTokens is *total context* (prompt + response), equivalent to KV cache size, per the upstream header comment. Step 1 — Transition-point analysis on the bad GPU response: - long_01 at k=20 with maxNumTokens=8192 has a deterministic 4917-token prompt. Response begins coherent for ~50 generated tokens (medical prose, references, formatting) and then collapses sharply into `* * * ...` for the remaining ~1450 tokens. - Transition is at total context ~5000 — not 4096, not gradual. - The model successfully prefills past 4096; what breaks is decode- side KV writes past prompt end. Most consistent with a kernel boundary that FP32 (CPU) absorbs and FP16 (GPU) does not. - Operational consequence: the 4096 deployment ceiling has ~900 tokens of safety margin. k=15 deployment is nowhere near the cliff — rules out a silent-degradation concern. Doc also lays out three open questions (Steps 2/3 and the artifact's prefer_activation_type field) for follow-up if anyone wants to write this up rigorously upstream.
…t_v2 The latency report's context-wall section now points to the standalone investigation doc and lists the four headline findings: - GPU defaults to FP16 (Android Adreno OpenCL); CPU runs FP32 (XNNPACK) - At maxNumTokens=8192, GPU collapse happens at total context ~5000 — not 4096, with a sharp transition over ~50 generated tokens - Failure is decode-side (KV writes past prompt end), not prefill- side - 4096 deployment ceiling has ~900 tokens of safety margin; current k=15 ships well below the breakdown zone Source is in aggregate_k_sweep.py so the cross-link survives any future report regeneration. Regenerated the markdown report.
…ty constraint Step 2 result (bit-identical across 3 reps on GPU at maxNumTokens=8192): - All 3 reps produced response_length_chars=6027, est_tokens=1506, transition at char 400 (total context ~5017), and bit-identical response head and tail. - Decode times vary by 0.05% — system jitter only. Model outputs are identical token-for-token. - This rules out stochastic FP16 drift as the proximate cause. GPU uses greedy decoding by default (max_top_k=1 from GpuConfig), so with the same prompt and same numerical paths the output is the same every time. The breakdown is a *deterministic* kernel-level issue that FP16 produces consistently. Reachability constraint added (a new section): - The natural FP32-on-GPU control test is not possible from the public Kotlin API in LiteRT-LM 0.11.0. Verified from four sources: Config.kt (no precision field on EngineConfig), Engine.kt (the nativeCreateEngine signature has no precision arg), LiteRtLmJni.kt (no SetActivationDataType bridge), and the C++ CreateDefault() factory (doesn't set activation_data_type_). - The C++ side has the SetActivationDataType method, but it is not wired to the Kotlin/JNI layer in this version. - Three plausible unblocks: (b) modify the .litertlm artifact's metadata to set prefer_activation_type=FLOAT32 — half day of flatc work; (c) file upstream to expose the API — right systemic fix but doesn't help us now; (d) build a custom AAR — multi-day, out of scope. - Mechanism story is well-anchored even without the control test; 4096 stays the ship value regardless. Updates: Refined mechanism hypothesis paragraph to mark FP16 as "candidate differentiator" rather than confirmed; "What's still open" table marks Step 2 + reachability resolved; adds new open questions for the artifact-modification path.
…latency_report_v2 The latency report's context-wall section now mentions: - 3-rep reproducibility test returned bit-identical output — same chars, same transition position, same head, same tail. With greedy decoding this rules out stochastic FP16 noise; the breakdown is deterministic. - The FP32-on-GPU control test would directly confirm or refute FP16 as the root cause, but the Kotlin API in LiteRT-LM 0.11.0 does not expose SetActivationDataType — the JNI bridge has no precision parameter. - The investigation doc lists three plausible unblocks (artifact-header modification, upstream API exposure, custom AAR build). Source is in aggregate_k_sweep.py so the cross-link survives any future report regeneration. Regenerated the markdown report.
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| // Set the prompt-budget ceiling explicitly so the limit is visible at the | ||
| // call site rather than inferred from runtime errors at high k. Empirical | ||
| // testing on Gemma 4 E4B/E2B .litertlm (see latency_report_v2.md §context | ||
| // wall): the engine accepts higher values (8192 init OK on both backends), | ||
| // but on GPU the output degenerates into a repetition loop past 4096 even | ||
| // when no error is thrown. 4096 is the highest value that produces clean | ||
| // generations across both backends for this artifact family. |
| - **(c) File an upstream issue with `google-ai-edge/LiteRT-LM`** to expose `SetActivationDataType` in the Kotlin `EngineConfig` API. Doesn't unblock us now, but is the right systemic fix. | ||
| - **(d) Build a custom LiteRT-LM AAR.** Clone the repo, add a field to the Kotlin `EngineConfig` + parameter to `nativeCreateEngine` + plumbing to the C++ setter. Multi-day project; out of scope. | ||
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| For now this PR documents the constraint and the experimental options. The mechanism story is already well-anchored even without the FP32 control: Steps 4, 1, 2 together make the FP16-induced determinstic kernel-zone breakdown the obvious hypothesis. The control test would *confirm* it; it doesn't change the deployment recommendation either way. |
The FP32-on-GPU control test that we'd flagged as blocked-by-API-gap is now done, via option (b) — modifying the .litertlm artifact's section metadata to inject `prefer_activation_type=float32`. The litert-lm-builder pip package supports this directly via the TOML `additional_metadata` field; no AAR fork or Kotlin patching needed. Mechanism: - Used litert-lm-peek to dump E4B sections + auto-generated model.toml. - Added `prefer_activation_type=float32` under additional_metadata on the prefill_decode section in the TOML. - Rebuilt the .litertlm with litert-lm-builder. Section data byte-identical; only metadata header changed. - Pushed to device, rebuilt GPU APK at maxNumTokens=5000, ran long_01 at k=15. Result: - Logcat at engine init: "section_prefer_activation_type: float32" and "activation_data_type: FLOAT32" — runtime honored the override. - Response: 998 chars / 249 tokens of clean medical reasoning, no degeneration anywhere in the response. Total context at response end was ~4514 — past 4096 but below the FP16 cliff at ~5000. Conclusion: FP16 is the root cause of the GPU breakdown, not a symptom amplifier. Same artifact, same prompt, same backend, same greedy decoding — only the activation precision changed, and it fixed the output. The "kernel boundary independent of precision" hypothesis is ruled out. Memory observations on the test device: - maxNumTokens=8192 with FP32 OOMs the app at first large prefill (KV cache ~5.8 GB + model ~3.4 GB + activations + overhead exceeds the ~10 GB available RAM). - maxNumTokens=5000 works cleanly. - Practical FP32-GPU ceiling on this hardware tier is somewhere in the 6500–7500 token range; we didn't bisect since latency is the bigger deployment blocker. Updates: Step 3 section added, Refined mechanism hypothesis #4 upgraded from "candidate differentiator" to "confirmed root cause", What's still open table refreshed (FP16-root-cause and artifact- metadata questions moved to Resolved), TL;DR rewritten.
The latency report's context-wall section bullet about the FP32 test gap is updated to reflect Step 3's result: FP16 is confirmed as the root cause via the artifact-metadata override path. The Kotlin API still doesn't expose SetActivationDataType, but the per-section `prefer_activation_type` key in the .litertlm metadata is honored by the runtime and gives us the same control. Bullet now states the resolved mechanism plus the deployment-relevant caveats: FP32 GPU is ~2-3x slower at decode and the KV cache doubles in size, so it's not a free swap from FP16. Source change is in aggregate_k_sweep.py so the cross-link survives any future report regeneration. Regenerated the report.
Ran the same 8-cell sweep we did for FP16 GPU in PR #57/#59, but against the FP32-tagged artifact at maxNumTokens=4096. ~4.5 hours total wall-clock on the OnePlus OPD2413 (Snapdragon 8 Elite). Result is a clean cell-by-cell comparison. Headline: FP32 GPU is **~25% slower than FP16 GPU at k=15** (~6 s extra wait per query), much less than the ~3× I'd estimated from the single-data-point Step 3 measurement. That earlier number was wrong — I had accidentally been comparing FP32-E4B against FP16-**E2B** (the smaller model), not the matched FP16-E4B baseline. The slowdown is almost entirely in TTFT (prefill): - FP16 TTFT 0.96–4.0s, FP32 TTFT 2.0–9.8s (~2–2.5× across all k) - FP16 decode 11–18s, FP32 decode 12–19s (essentially identical) Mechanism: prefill is compute-bound (one parallel forward pass over the input), so FP16's 2× arithmetic throughput on Adreno helps a lot. Decode is bandwidth-bound (sequential token-at-a-time loading of weights), so the FP16/FP32 precision choice barely matters. Same 24 errors at k=20 on FP32 GPU as on FP16 GPU — the prompt-cap rejection at maxNumTokens=4096 is precision-agnostic, just a config check. What this means: **FP32 GPU is a real shipping option, not just an experiment.** At maxNumTokens=4096 the latency cost is ~25% (no quality benefit — we're below the FP16 cliff anyway). At higher maxNumTokens (e.g., 5500), FP32 GPU enables clean output past the FP16 cliff at the same ~25% latency hit. Memory ceiling caps maxNumTokens at ~6500–7500 on this 16 GB device since KV cache doubles vs FP16. The choice between FP16 GPU and FP32 GPU is now a UX-vs-margin tradeoff at the deployment level, not a feasibility question. Updates: new Step 5 section with full sweep table + corrected slowdown narrative; updated TL;DR bullet on FP32 latency cost; "What's still open" table marks FP32 latency curve resolved; full 8-JSON inventory added to References.
The investigation doc and the latency report now open with a clear, unmissable callout about the failure mode anyone touching this stack needs to know about: - The default activation precision on Android GPU in LiteRT-LM is FP16. - FP16 attention causes a silent, deterministic decoding failure (collapse into `*` repetition) once total context exceeds the artifact's calibrated zone (~5000 tokens for the current Gemma 4 .litertlm artifacts). - No error is raised — the response just becomes garbage tokens. - The breakdown is bit-exactly reproducible because GPU defaults to greedy decoding. References the concrete example JSON that captures this failure (benchmark_20260516T104730_k20.json) and notes it should be kept in the repo as the reference case. The current MAM-AI deployment stays below the breakdown by shipping maxNumTokens=4096, so we're safe today. The warning is for future contributors who consider lifting the cap, switching artifacts, or debugging unexpected output on GPU. Updates: - New blockquote callout at the top of maxnumtoken_investigation.md before the existing TL;DR section. - Headline bullets in latency_report_v2.md (sourced from aggregate_k_sweep.py) lead with the same warning + pointer to the example JSON.
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| > A concrete example of this failure is captured in [`benchmark_20260516T104730_k20.json`](../latency_results/benchmark_20260516T104730_k20.json): query `long_01` at k=20, FP16 GPU, maxNumTokens=8192. The response opens with coherent medical reasoning for the first ~50 generated tokens, then deterministically collapses into an asterisk-repetition loop for the remaining ~1450 tokens. **Keep this file in the repo as the reference example of the failure mode.** | ||
| > |
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| // Set the prompt-budget ceiling explicitly so the limit is visible at the | ||
| // call site rather than inferred from runtime errors at high k. Empirical | ||
| // testing on Gemma 4 E4B/E2B .litertlm (see latency_report_v2.md §context | ||
| // wall): the engine accepts higher values (8192 init OK on both backends), | ||
| // but on GPU the output degenerates into a repetition loop past 4096 even | ||
| // when no error is thrown. 4096 is the highest value that produces clean | ||
| // generations across both backends for this artifact family. |
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| "[`benchmark_20260516T104730_k20.json`](../latency_results/benchmark_20260516T104730_k20.json) " | ||
| "(long_01, k=20, FP16 GPU, maxNumTokens=8192). Today's 4096 cap stays " | ||
| "well below the breakdown; lifting the cap on FP16 GPU is unsafe without " | ||
| "switching to FP32.") |
… in JSON
Three connected changes to make benchmark JSONs self-describing for the
FP16-vs-FP32 / context-window comparison:
1. Single source of truth for max_num_tokens
- Add `engine.max_num_tokens: 4096` to runtime_config.json
- RagPipeline.kt reads it from there instead of hardcoded literal in
EngineConfig(maxNumTokens = 4096)
- BenchmarkForegroundService.kt reads it from the same source and
records the actual value in the JSON's config block as
`max_num_tokens`
2. Remove the max_tokens=32000 fiction
- The previous JSON config block recorded `"max_tokens": 32000` as a
hardcoded literal. The value was never passed to any API or
enforced anywhere; it was a documentation lie that obscured what
the runtime actually did. Removed entirely.
- Sampler params (temperature/top_p/top_k) were also hardcoded in
two places (BenchmarkForegroundService + the runtime_config.json
RagPipeline reads from). Consolidated to read from runtime_config.
3. Add provenance fields to the benchmark JSON
- `artifact_fingerprint`: SHA-256 of the first 64 KB of the loaded
.litertlm. The FlatBuffers header lives in that region, so this
hash uniquely identifies the artifact variant (e.g. distinguishing
the default Gemma 4 build from a `prefer_activation_type=float32`-
tagged rebuild). Critical for reviewing FP16-vs-FP32 comparisons
where the only difference is the artifact metadata.
- `git_commit_sha`: wired into BuildConfig via a new gitShortSha()
helper in app/android/app/build.gradle.kts. Reviewers can trace
any benchmark JSON back to the exact source state that produced
it.
- `litertlm_version`: existed in BuildConfig already, now also
surfaced in benchmark JSON metadata.
Verified via smoke benchmark (medium_01 k=3 GPU): JSON now contains
max_num_tokens=4096, the actual sampler params from runtime_config,
the artifact_fingerprint of the FP32-tagged Gemma 4 E4B artifact
currently installed on the test device, and the git_commit_sha of the
parent commit. No max_tokens=32000 field.
Motivation: while writing up the FP32-vs-FP16 latency comparison,
discovered that the existing JSONs cannot distinguish runs from
different (maxNumTokens, activation_precision) configurations because
none of those settings were recorded. The first benchmark file in the
investigation (long_01 at k=20, asterisk-loop failure) doesn't say
which maxNumTokens it ran at — that fact came from session memory only.
Going forward, JSONs are self-describing for these comparisons.
The benchmark JSONs from 52e11e9 onward record `config.artifact_fingerprint` (SHA-256 of the first 64 KB of the loaded .litertlm). This is critical because the FP32-tagged rebuild and the original FP16 default share the same filename — without the fingerprint, a JSON can't tell us which precision the run used. Add a new "Reference: artifact fingerprint mapping" section to the investigation doc with the two fingerprints + the precision they correspond to, verified cryptographically against the local files: - 9fdf9dd1... = FP32-tagged Gemma 4 E4B (prefer_activation_type=float32 injected on prefill_decode section). All Step 3 and Step 5 FP32 GPU runs use this artifact. - cfa067b6... = Original litert-community/gemma-4-E4B-it-litert-lm from HuggingFace. FP16 by runtime default per Step 4. Both files are exactly 3,654,467,584 bytes — litert-lm-builder preserved section offsets, only metadata bytes differ. Includes a 5-line Python snippet so anyone can re-verify the mapping against their own local artifacts. Future artifact variants should extend the table. Why this matters now: the overnight FP32-vs-FP16 GPU sweep produces JSONs whose precision condition is only verifiable via this mapping (no direct activation_data_type field in the JSON — adding that would require parsing the .litertlm FlatBuffers header in Kotlin, out of scope for tonight). With this doc committed, every future JSON is classifiable.
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Summary
maxNumTokens = 4096explicitly inEngineConfigat RagPipeline.kt:buildEngine(). The value was already being applied via the engine's null-default; this commit makes the constraint visible at the call site.2048) and upper (8192) values on both backends to pin down what 4096 actually means. Findings landed in latency_report_v2.md §"Errors and the 4096-token context wall".Why this matters
The prior latency report claimed the 4096 wall was "a property of the .litertlm artifact format." That was an inference based on (a) we didn't set it in our code, (b) the API docstring said "default from model or engine." This PR actually tested it and the inference was wrong.
What the experiment found
maxNumTokens = 2048(both backends)>= 2048instead of>= 4096maxNumTokens = 8192(CPU)Engine.initialize()succeeds;long_01andlong_03at k=20 (previously failed at 4096) now generate clean responses ending with proper referencesmaxNumTokens = 8192(GPU)Engine.initialize()succeeds;long_01at k=20 generates real medical text initially then degenerates into a repetition loop (* * * * ...) for ~5000 charsSo 4096 isn't an architectural cap — it's the highest cross-backend-safe value for clean output on the current Gemma 4
.litertlmartifacts. Lifting it works mechanically but breaks GPU output, which is the deployment-relevant backend.Test plan
flutter build apk --release -PuseGpuForLlm=false) → install → smoke test at k=3 succeeds.flutter build apk --release -PuseGpuForLlm=true) → install → smoke test at k=3 succeeds.>= 4096error as before this PR (no regression in wall behavior).What this PR does NOT do
app_config.jsonstill ships E4B.Future work (not in this PR)
litert-community/for a Gemma 4.litertlmbuild with a larger context window + matching higher-quality decode behavior.🤖 Generated with Claude Code