# recipes/qwen3.8-27b/b200/attune-tp1-pp2.yaml # An OpenRecipes recipe; the fields are documented in schema/recipe.schema.yaml. schema_version: 1 model: Qwen3.8-27B gpu: type: B200 memory_gb: 180 count: 8 per_node: 8 instance: AWS p6-b200.48xlarge author: name: ATTUNE url: https://arxiv.org/abs/2610.17528 status: verified stack: framework: 'slime 0.3.0: THUDM/slime a897e1f plus 8 commits that do not change the training step' megatron_lm: commit 1dcf0daf (megatron-core 0.16.0rc0), with a backported upstream fix for the Qwen3.5 attention output gate at TP above the query-group count transformer_engine: 2.10.0 pytorch: 2.11.0 (CUDA 12.9 build) cuda: '12.9' nccl: 2.28.9 (CUDA 12.9 build; printed by the measured run) deepep: 1.2.1 python: 3.12.3 fla: 0.4.1 (flash-linear-attention) triton: 3.6.0 network: 8 GPUs per node, EFA, 3.2 Tb/s RDMA per node runtime_fixes: patches/fla_blackwell_guard.py, imported in every training process before FLA source: 'in-pod version check of the measured run''s training image (same digest) on 2026-10-18: installed TE, PyTorch, DeepEP, megatron-core, FLA and Triton, Megatron-LM git commit, Python; NCCL from the measured run''s log' workload: row_tokens: 8192 rows: SFT conversations global_batch: 128 precision: bf16 dataset: name: Tulu 3 SFT mixture source: allenai/tulu-3-sft-mixture revision: b14afda60f1bbebe55d5d2fa1e4df5042f97f8be license: ODC-By 1.0 rows_sha256: ee1486b28ce21df721ad61c2d3765c64a29eed0eb5680ede005bd6a321dc1a1e build: name: qwen38-tulu3-fixed8192-v1 tokenizer: Qwen3.8 (the Qwen3.8-27B tokenizer; tokenizer.json sha256 0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3) loss_mask: qwen3_5 rows: 6400 row_tokens: 8192 padding_tokens: 0 seed: 42 packing: source conversations in a seeded global permutation, concatenated with an EOS token between them and cut into consecutive 8,192-token rows; rows without supervised targets are skipped supervised_token_ratio: 0.642 parquet_sha256: a7b7b4db98e95152604a77bcf09b51502d2266ba04de98409cef6b97afd039c8 parquet_bytes: 76722714 builder: data/generate_fixed_sft.py (data/README.md) layout: label: TP1 PP2 DP4 summary: TP 1, PP 2 (34/30 layers), CP 1, DP 4; one 8,192-token row per microbatch; full recompute (uniform, 1 layer); precision-aware distributed optimizer; no offload; dense model, no experts highlights: 34/30 layer split, no offload tp: 1 pp: 2 vpp: 1 cp: 1 ep: 1 etp: 1 dp: 4 sequence_parallel: false layer_split: first: 34 last: 30 max_tokens_per_gpu: 8192 rows_per_microbatch: 1 recompute: granularity: full method: uniform num_layers: 1 offload: null dispatcher: null result: tokens_per_s_per_gpu: 3858.51 usd_per_million_tokens: 1.03 step_time_cv_percent: 0.30 mfu: 0.2637 mfu_note: null active_params: count: 25623920640 source: https://huggingface.co/Qwen/Qwen3.8-27B revision: 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0 measured: 2026-10-18 steps_per_run: 20 window: - 5 - 19 window_tokens: 15728640 runs: 1 profiler: false gpu_hours: 2.29 gpu_hours_note: GPUs x (scheduler admission to finish), including startup; the same run is the candidate arm of the convergence check correctness: result: pass summary: passed; 1 component test passed on this image reference: 'The layout of slime''s `scripts/run-qwen3.5-27B.sh` on one 8-GPU node: TP 4 with sequence parallelism, PP 2 (first stage 34 layers, last 30), CP 1 in place of its CP 4, full recompute and CPU optimizer offload, with this recipe''s optimizer settings.' configuration: pass component_tests: - test: precision-aware Adam on the GPU and CPU optimizer offload (fraction 1.0, overlapped copies), each against an FP64 reference shape: 'one Qwen3.5-397B-A17B MoE layer: hidden 4096, 512 experts, top-10, expert FFN 1024, EP 8, ETP 1, 4,096 tokens per rank' gpu: B200 date: 2026-10-14 result: pass detail: worst tensor (update) relative L2 error 5.12e-7 on the GPU and 7.99e-7 offloaded against a bound of 1.0e-3 after one step, and 4.99e-5 and 7.73e-5 against 1.1e-3 after three steps at the recipe's hyperparameters (bound 2 x the default GPU optimizer's error + 1e-3); reconstructed FP32 masters bitwise identical to an arm with explicit FP32 masters, the TE remainder self-test passes and the negative controls fail note: run on B200 on this recipe's training image; the optimizer step is elementwise, so the result holds for every model on the image convergence: steps: 20 result: pass correlation: 0.9999987 final_loss_difference: 2.9e-05 pointwise_failures: 0 note: 'candidate arm: the measured run; reference arm: a 20-step run of the reference recipe on the same image and rows; the parity probe off in both; both with patches/fla_blackwell_guard.py' rules: argument classes of ATTUNE's skill/scripts/correctness/rules/numerics_list.yaml (amazon-science/attune f2cb450), with CPU optimizer offload admitted by the B200 component test above; 20-step check by Megatron-Bridge validate_convergence with its pipeline thresholds env: CUDA_DEVICE_MAX_CONNECTIONS: '1' PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True TORCHINDUCTOR_COMPILE_THREADS: '1' NCCL_NVLS_ENABLE: $([ "$NVLINK_COUNT" -gt 0 ] && echo 1 || echo 0) patches: - patches/fla_blackwell_guard.py run: | export CUDA_DEVICE_MAX_CONNECTIONS=1 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True export TORCHINDUCTOR_COMPILE_THREADS=1 export NCCL_NVLS_ENABLE=$([ "$NVLINK_COUNT" -gt 0 ] && echo 1 || echo 0) python3 -m --async \ --actor-num-nodes 1 \ --actor-num-gpus-per-node 8 \ ${MODEL_ARGS[@]} \ --hf-checkpoint "$HF_CKPT" \ --ref-load "$TORCH_DIST" \ --rollout-function-path pretokenized_sft_rollout.generate_rollout \ --prompt-data "$SFT_DATA" \ --input-key messages \ --metadata-key metadata \ --num-rollout 20 \ --rollout-batch-size 128 \ --global-batch-size 128 \ --loss-type sft_loss \ --loss-mask-type qwen3_5 \ --calculate-per-token-loss \ --disable-compute-advantages-and-returns \ --debug-train-only \ --tensor-model-parallel-size 1 \ --pipeline-model-parallel-size 2 \ --decoder-last-pipeline-num-layers 30 \ --context-parallel-size 1 \ --expert-model-parallel-size 1 \ --expert-tensor-parallel-size 1 \ --sequence-parallel \ --recompute-granularity full \ --recompute-method uniform \ --recompute-num-layers 1 \ --use-dynamic-batch-size \ --max-tokens-per-gpu 8192 \ --optimizer adam --lr 1e-5 --lr-decay-style cosine --min-lr 1e-6 \ --lr-warmup-fraction 0.1 --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.98 \ --use-distributed-optimizer \ --use-precision-aware-optimizer \ --attention-dropout 0.0 --hidden-dropout 0.0 \ --accumulate-allreduce-grads-in-fp32 --attention-softmax-in-fp32 \ --attention-backend flash \ --distributed-timeout-minutes 30 \ --tensorboard-dir "$PROFILE_DIR" notes: | - Starting point: slime's Qwen3.5-27B script, `scripts/run-qwen3.5-27B.sh` with `scripts/models/qwen3.5-27B.sh` at slime 0.3.0. slime ships it as a 4-node GRPO script and publishes no tokens/s figure for it. Ported to this workload on one 8-GPU node with its layout (TP 4 with sequence parallel, PP 2 with 34/30 layers, CP 1 in place of its CP 4, full recompute, Adam on the CPU), it measured 1,411.1 tokens/s/GPU on 8 B200; ATTUNE tuned it to 3,858.5 (+173%). - Recompute stays on: the tuning runs without it ran out of memory on the first stage at TP 4 and TP 2 with PP 2 (162.7 and 165.8 GiB allocated of 178 GiB). - B200 runs of this model need `patches/fla_blackwell_guard.py`: with the image's Triton 3.6, FLA 0.4.1's autotuner picks gated-delta-net kernel configs that race on Blackwell and give a different, wrong result on every call, and the patch keeps those kernels on the configs FLA main uses there. Every training process imports it before FLA (patches/README.md) and prints a `[fla-guard]` line.