feat(rosetta): OLMo + StableLM + GPTBigCode families (closes #1591, #1592, #1594)#1662
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noahgift merged 14 commits intoMay 13, 2026
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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>
…loses #1591, #1592, #1594) Three Llama-derivative / GPT-2-derivative families share an `Architecture` variant with their parent — none need a new variant or a custom tensor mapper. Engine change is a single match arm extension in `from_model_type`: - OLMo / OLMo-2 (allenai/OLMo*) → `Architecture::Llama` - StableLM (stabilityai/stablelm*) → `Architecture::Llama` - GPTBigCode (StarCoder1 / SantaCoder / tiny_starcoder_py) → `Architecture::Gpt2` OLMo and OLMo-2 share `LlamaForCausalLM` tensor naming. StableLM likewise — partial-RoPE and per-checkpoint norm variation are runtime concerns, not tensor-name concerns. GPTBigCode uses GPT-2 Conv1D layout with Multi-Query Attention (single shared K/V head); MQA semantics affect cache shape and inference dispatch but not tensor-name resolution, so the Gpt2 mapper handles names. Three YAMLs added: - `contracts/model-families/olmo.yaml` (1B / 7B / OLMo-2 7B / OLMo-2 13B) - `contracts/model-families/stablelm.yaml` (1.6B / 3B / Zephyr-3B) - `contracts/model-families/gpt_bigcode.yaml` (tiny / SantaCoder / StarCoder1 15.5B) `from_model_type` extended: - `"olmo" | "olmo2" | "stablelm" | "stablelm_epoch" | "stablelm_alpha"` → `Self::Llama` (joins existing smollm / granite / nemotron list) - `"gpt_bigcode" | "gpt-bigcode"` → `Self::Gpt2` (joins existing starcoder / starcoder2 / bigcode list) Verified: - `pv validate` clean on all three YAMLs - FALSIFY-PARITY-002 (`test_every_model_family_yaml_has_architecture`) passes
This was referenced May 14, 2026
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Summary
Closes #1591 (OLMo), #1592 (StableLM), #1594 (GPTBigCode) in a single PR. Three Llama- / GPT-2-derivative families share an existing `Architecture` variant with their parent — none need a new variant or a custom tensor mapper.
Engine change (single function)
`tensor_expectation.rs::from_model_type`:
```rust
// LLaMA derivatives (use LLaMA tensor naming)
"smollm" | "smollm2" | "granite" | "granite3" | "nemotron"
| "olmo" | "olmo2" | "stablelm"
| "stablelm_epoch" | "stablelm_alpha"
=> Some(Self::Llama),
// StarCoder + GPTBigCode reuse GPT-2 tensor naming
"starcoder" | "starcoder2" | "bigcode"
| "gpt_bigcode" | "gpt-bigcode"
=> Some(Self::Gpt2),
```
YAMLs
Rationale
Test plan
🤖 Generated with Claude Code