WS1 C6–C11 closeout evidence¶

Parent: #266 · Branch: feat/ws1-c6-c11-closeout-266

C9 green is assembly only. H20 C8 and both C10 backend profiles are green on fdf5bcc5165820abb506291a29370225306514ca. Full WS1 EXIT still requires the final-commit required GitHub GPU CI run plus parent A/B/Final comments.

What landed¶

ID	Code	CPU evidence
C6	`rl_engine/kernels/gtest/kv_consistency.py`, `scripts/check_decode_prefill.py`	`tests/test_kv_consistency.py`
C7	`StatefulKVCache` + same harness, `scripts/check_stateful_kv.py`	B1 writer/reader + generate-rescore; B2=`absent`
C9	`rl_engine/alignment/qwen3_dense.py`, `scripts/ws1_chain_fwd_bwd.py`	topology / official fingerprint / profile resolution
C10	`rl_engine/kernels/gtest/chain_gate.py`, `scripts/ws1_chain_gate.py`	report schema + bitwise `atol=0` rule
C11	`ci/run_ws1_chain_gate.sh`, `.github/workflows/ws1-chain-gpu.yml`	CPU schema jobs in `ci.yml`; GPU job is required

Private _DECODE_ATOL / _PADDING_ATOL were removed from tests/test_kv_cache_attention.py. Those checks now resolve C1 forward_accuracy / attention.

Local GPU evidence (not EXIT)¶

NVIDIA GeForce RTX 4070 Ti SUPER, CC 8.9, this branch:

Gate	Profile	Result
C6 `check_decode_prefill.py`	`cuda_bf16`	passed; 6/6 cells; attn max_abs = 0
C6 `check_decode_prefill.py`	`triton_cuda_bf16`	passed; 6/6 cells; attn max_abs = 0
C7 `check_stateful_kv.py`	`cuda_bf16`	B1 + generate-rescore passed; B2=`absent`
C7 `check_stateful_kv.py`	`triton_cuda_bf16`	B1 + generate-rescore passed; B2=`absent`

C9/C10/C11 full 36-layer + pinned weights were not run here (16 GB card). That execute is the remaining closeout step on H20.

Local pytest totals are command-scoped development evidence, not an EXIT criterion. Always quote the exact test command and commit with a pass count; do not cite a bare aggregate such as 220 passed as closeout evidence.

H20 technical execute¶

Environment: NVIDIA H20 (CC 9.0), driver 580.76.05, CUDA 12.8, PyTorch 2.8.0+cu128, Triton 3.4.0, pinned Qwen3-8B weight hash fc664a19c52c82b6f5ddb33d4fe2723181daeb93a344b16fee6369963e5a13a5.

Gate	Profile	Result
C8 four-judgment matrix	CUDA + Triton	green=176, N/A=16 (pack), red=0
C10 full model	`cuda_bf16`	passed, `first_drift=null`, clean SHA
C10 full model	`triton_cuda_bf16`	passed, `first_drift=null`, clean SHA

Each C10 profile passed 7/7 forward-invariance rows, 2394/2394 parameter-gradient invariance rows, 2/2 FP32 forward-accuracy rows, 798/798 FP32 gradient-accuracy rows, 8/8 accuracy aggregates, all three train/infer aggregates, BN train/infer parity, and 6/6 decode/prefill cases. The four Batch/Chunk cells execute separate real paths; chunked training runs per-layer chunked operations and verifies its logits against stateful chunked prefill before backward.

These local H20 results establish C10 technical readiness. They do not replace C11's required GitHub workflow success and workflow URL on the final commit.

H20 reproduction commands¶

Pinned Qwen3-8B snapshot (C2 revision + shard hashes):

export QWEN3_8B=/path/to/Qwen3-8B   # safetensors at revision b968826d9c46dd6066d109eabc6255188de91218
export KERNEL_ALIGN_FORCE_SM90=1

python scripts/prepare_ws1_weights.py --output "$QWEN3_8B" --verify-only

python scripts/check_decode_prefill.py --backend-profile cuda_bf16
python scripts/check_decode_prefill.py --backend-profile triton_cuda_bf16
python scripts/check_stateful_kv.py --backend-profile cuda_bf16
python scripts/check_stateful_kv.py --backend-profile triton_cuda_bf16

python scripts/ws1_chain_fwd_bwd.py --backend-profile cuda_bf16 --weights hf --weights-path "$QWEN3_8B"
python scripts/ws1_chain_fwd_bwd.py --backend-profile triton_cuda_bf16 --weights hf --weights-path "$QWEN3_8B"

python scripts/ws1_chain_gate.py --backend-profile cuda_bf16 --model qwen3-8b-dense --dtype bfloat16 --weights required --weights-path "$QWEN3_8B" --json
python scripts/ws1_chain_gate.py --backend-profile triton_cuda_bf16 --model qwen3-8b-dense --dtype bfloat16 --weights required --weights-path "$QWEN3_8B" --json

Bind the two C10 JSON files + git rev-parse HEAD + GPU/CC on the parent issue before closing #266.

Each accepted JSON must report backward_executed=true, train_infer_executed=true, accuracy_executed=true, gradient_accuracy_executed=true, a null first_drift, verified weight content hash, complete runtime observations for all nine required node kinds, backward runtime kernel identities for lm_head / rms_norm / det_gemm / embedding, gpu_name, representative case_ids, C8 evidence path, and git_dirty=false. Schema is ws1-c10-c11-v5. Generate the JSON only after committing the gate code; the CI wrapper rejects dirty-worktree evidence.

C10 compares tensor.grad after a real loss.backward() for every official Qwen3-8B Dense trainable leaf (gradient_scope=all_required_trainable_parameters, all_parameter_gradients=true): embedding, final norm, LM head, and all 36 layers of Q/K/V/O, QK-norm, RMSNorm, and MLP weights. Logprob accuracy vs the FP32 gold cell is judged only by max_abs_dlogp / approx_kl0 / clipfrac0. Full-model decode/prefill covers short, long, varlen, left/right padding, and B=1/N. Backward runtime records the kernel that actually ran (det_gemm / RMSNorm / embedding), not a class-attribute string.

Gradient snapshots are copied to CPU in their native dtype rather than retained as FP32 CUDA tensors, and their payloads are released after comparisons while report keys remain. The chain GPU job generates C8 outside the repository, then C11 loads c8_evidence_path and requires its commit to equal C10 git_sha with both worktrees clean. Packed forward and gradient accuracy must appear explicitly as BN/packed versus fp32_reference.

Not claimed¶

Multi-GPU / vime / real vLLM vs Megatron
C9 skeleton alone as EXIT
Production paged-KV (C7 B2 is explicitly absent)
C11/public EXIT until the required final-commit GitHub GPU workflow is green