Run Laya encoder and decision layers on CUDA - #49

Purpose

Run Laya's 28 encoder layers and two decision transformer layers from Rust. Validate the checkpoint and kernel bundle before loading, then leave hidden states on the GPU for the scorer.

Part of #14. Depends on #48 and the existing Hopper kernel build. Uses original RoPE and eager execution. Scorer, HTTP and CUDA Graph integration are separate steps.

Kept in draft until #48 merges. The diff against main currently includes its dependencies. Review this increment: 419 core/configuration lines for encoder execution.

Tests now live under the repository-root tests/ directory. Cargo target names and test coverage are unchanged.

Test Plan

cargo fmt --all --check
cargo clippy --workspace --locked --all-targets -- -D warnings
cargo test --workspace --locked
cargo build --workspace --release --locked

Compare with official Laya 0.3.20 on H800: 13 real request cases, including 16 distinct rows at 16×512. Check intermediate and final hidden states, reverse request order, and repeat each workspace without intermediate readbacks. Reproduction commands are in recipe/laya/README.md. The residency/workspace recipe covers the inherited resource checks.

System1-Omni Version / Commit: b7c9384. Encoder increment: 2f5ff4b → ff4d8fa; GPU run: 684470a. Later changes add pre-load rotary-size validation, device selection and test/documentation layout; model execution and kernels are unchanged.

Test Result

Current CPU CI: 45 tests passed; 9 checkpoint/GPU tests skipped. fmt, strict Clippy and release build passed. This documentation-only follow-up also passed local strict MkDocs and rendered-anchor checks. The historical GPU validation and benchmark below were not rerun; encoder and CUDA source are unchanged.

All 208 valid-token comparisons had zero measured error. Repeated runs produced identical output bytes. Candidate outputs were finite; empty-key padding was excluded from equality checks. These checks validate implementation parity.

CI for b7c9384: Rust CI, Docs build, benchmark harness tests passed.

H800 comparison, 2026-10-03: original RoPE in #49 vs Laya 0.3.20 eager fast path. Same GPU, checkpoint, token IDs, lengths and question types; BF16, concurrency 1, CUDA Graphs disabled. Scope: embedding + 28 encoder + 2 decision layers; scorer and HTTP are outside this benchmark.

Host p50 is 0.3–1.6% lower for short/long inputs and 0.4–0.5% higher at 16×512.

Each cell is run 1 / run 2; latencies are in ms. Order: upstream→Rust, then Rust→upstream. Each case/backend/run uses 20 warmup calls and 100 measured calls, after a feasibility pass. Host timing includes uploads and completion sync; req/s counts encoder calls. Rust includes validation/serialization and per-upload sync; upstream uses prebuilt CPU tensors and a final sync. Loading, compilation, warmup and readback are excluded.

30/30 final-hidden comparisons passed: nRMS=0, max_abs=0; existing thresholds unchanged. These are implementation-parity checks.

gh gist clone https://gist.github.com/linear3735/c777b0dc449676ebf1c26f0990a90b16 evidence
# Follow evidence/README.md to prepare source, checkpoint and kernel bundle.
python source/recipe/laya/native/compare_encoder.py "$LAYA_CHECKPOINT" cases.json target/release/examples/encoder_bench NEW_HOST_OUTPUT --timer host --warmup 20 --samples 100
# Switch LAYA_CUDA_LIBRARY to the event wrapper for a separate pass.
python source/recipe/laya/native/compare_encoder.py "$LAYA_CHECKPOINT" cases.json target/release/examples/encoder_bench NEW_EVENT_OUTPUT --timer cuda-event --warmup 20 --samples 100

Self-review

Before marking this PR ready for review or requesting maintainer review, complete
the self-review checklist.
Keep the PR in draft while this work is incomplete.
For agent assistance, use the optional precheck-pr skill.

• I have reviewed the full diff and addressed the issues I found.
• I have checked that the change follows the project's architecture and stays focused on the stated purpose.
• I have run the checks appropriate to this change and reported commands, results, and anything I could not verify above.
• I have checked that the PR description, documentation, and any accuracy or performance claims match the implementation and available evidence.