feat(rosetta): add BigCode StarCoder2 model-family contract (closes #1593)#1661
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…w-major (was [K,N]); MODEL-1 → 100% (PMAT-CODE-SHIP-007-F32-GEMV-LAYOUT-FIX)
§74 localized the SHIP-007 PARITY-GATE bug to f32_gemv_into via PR-B's
stage-bisection scaffold (CPU vs GPU per-stage statistics analysis).
The F32 GEMV PTX kernel was reading weights with TRANSPOSED layout
interpretation:
Bug: kernel assumed A is K-rows × N-cols row-major (A[i,j] at i*N+j),
but actual ML weights are stored [output_dim=N, input_dim=K]
row-major (A[i,j] at i*K+j per PyTorch/SafeTensors/GGUF convention
and PMAT-333 F32 dequantization output).
Symptom: GPU read transposed weights → computed y = A^T @ x instead
of y = A @ x → systematically anti-correlated logits
(cos=-0.005190 vs CPU, top-10 divergences all sign-flipped,
CPU mean=-2.42 vs GPU mean=0.013).
Fix: rewrite the inner loop to iterate along the K dimension within
row block_id:
row_base = a_ptr + block_id * K * 4
thread reads A[block_id, t], A[block_id, t+32], ...
instead of:
col_base = a_ptr + block_id * 4
thread reads A[t, block_id], A[t+32, block_id], ...
Empirical discharge (canonical 7B teacher, lambda-vector RTX 4090,
default graphed path):
PARITY-GATE: PASS (no error from forward_gpu_resident)
Throughput @ 128-tok 5-iter decode: 124.6 tok/s
AC-SHIP1-007 floor: 30 tok/s
Headroom: 4.15× over floor
TTFT: 8.39 ms
p50 latency: 1016 ms
Before PR-E:
PARITY-GATE FAILED cos=-0.005190
Throughput (with SKIP_PARITY_GATE=1 + SKIP_FP8_WARMUP=1): 5.6 tok/s (§63) / 54.5 tok/s (§73)
GPU CANNOT serve this model
After PR-E:
PARITY-GATE PASS, default path, NO workarounds
124.6 tok/s, 4.15× over floor
Ship-% impact:
MODEL-1 ship %: **99% → 100%**
10 of 10 AC-SHIP1-* LIVE-DISCHARGED:
SHIP-001 (§72) SHIP-002 (§61) SHIP-003 (§72)
SHIP-004 (§72) SHIP-005 (§71) SHIP-006 (§61.8)
SHIP-007 (this PR) SHIP-008 (§61) SHIP-009 (§72)
SHIP-010 (§72)
MODEL-2 ship %: unchanged at 57% (independent track).
Cascade arc closeout: §63 → §73 → PR-A (#1648) → PR-B (#1649)
→ §74 (#1650) → PR-E (this). One PR shipped in 1 day after §73's
'3-5 PR / 3-5 day' estimate.
Auxiliary change: logits.rs adds APR_LM_HEAD_FORCE_QTYPE env-var
probe kept as a diagnostic tool (zero behavior change when unset).
Test plan:
- [x] cargo build --release -p apr-cli --bin apr --features cuda → clean
- [x] apr bench (default path, 128-tok 5-iter) → 124.6 tok/s, passed: true
- [x] apr parity → PARITY-GATE PASS
- [ ] CI tests (workspace-test on per-PR runner)
Refs:
- §74 SHIP-007 bug localized (PR #1650)
- §73 SHIP-007 cascade reduction (PR #1647)
- contracts/apr-ship-007-gpu-stage-bisection-v1.yaml (PR-A #1648 contract)
- PR #1649 (PR-B GPU stage dump scaffold)
- AC-SHIP1-007 (spec §5)
- evidence/section-75-ship-007-discharged-2026-05-13/
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…07 contract violation (PMAT-CODE-SHIP-007-PR-E-FALSIFY-007-CLEAN)
The env-var bisection probe added in PR-E (this branch) introduced a
`_ =>` catch-all inside a `match` expression that referenced
`WeightQuantType` in its arm values. The `falsify_007_no_catch_all_
in_dispatch_sites` contract test's 30-line walk-back heuristic flagged
this as a violation, even though the match was on `&str` (env var
value), not on `WeightQuantType`.
The probe was a bisection tool used to identify the bug location
during §74. Now that §75 has shipped the actual fix and the probe is
no longer needed, removing it cleans up the contract violation.
The remaining PR-E change is solely the F32 GEMV PTX kernel layout
fix in `crates/aprender-gpu/src/kernels/gemv/mod.rs` — that's the
actual bug fix.
Test verified:
cargo test -p aprender-serve --lib \
quantize::contract_tests::tests::falsify_007_no_catch_all_in_dispatch_sites
→ 1 passed
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…1593) Adds `contracts/model-families/starcoder2.yaml` so apr-cookbook architecture-demos flips StarCoder2 from `status: blocked` → covered. StarCoder2 is mapped to `Architecture::Gpt2` in `from_model_type` (tensor_expectation.rs:130) and aliased in `kernel_explain/resolve.rs:28`, mirroring the GPT-2 Conv1D tensor naming. Runtime details differ (RoPE / GQA / sliding-window / GELU+LN vs GPT-2 absolute / MHA), but tensor names follow the existing pattern, so the existing GPT-2 mapper handles names correctly. Engine support for the RoPE+GQA bits on the GPT-2 path is gated separately. YAML-only PR. Size variants from HF config.json (`bigcode/starcoder2-{3b,7b,15b}`): - 3b: hidden=3072 layers=30 heads=24 kv=2 inter=12288 - 7b: hidden=4608 layers=32 heads=36 kv=4 inter=18432 - 15b: hidden=6144 layers=40 heads=48 kv=4 inter=24576 All sizes share the 49152-token BigCode vocab and 16k context. Verified: - `pv validate contracts/model-families/starcoder2.yaml` → 0 errors - FALSIFY-PARITY-002 passes.
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Summary
Closes #1593. Adds `contracts/model-families/starcoder2.yaml`.
Why no engine change
`from_model_type("starcoder2")` already returns `Architecture::Gpt2` (tensor_expectation.rs:130) and the kernel_explain alias table maps `starcoder2 → Starcoder2ForCausalLM` (resolve.rs:28). Tensor names follow the GPT-2 pattern (split q/k/v/o + up/down). Runtime semantic differences (RoPE / GQA / sliding-window) are out of scope here — name-resolution-only YAML.
Sizes (HF config.json)
49152-token BigCode vocab, 16k context across all sizes.
Test plan
🤖 Generated with Claude Code