# recipes/qwen3-32b/h200/attune-tp4-pp2-mt16k.yaml # An OpenRecipes recipe; the fields are documented in schema/recipe.schema.yaml. schema_version: 1 model: Qwen3-32B gpu: type: H200 memory_gb: 141 count: 8 per_node: 8 instance: AWS p5en.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 network: 8 GPUs per node, EFA, 3.2 Tb/s RDMA per node source: 'in-pod version checks of the measured run''s training image, 2026-10-13 and 2026-10-18: installed TE, PyTorch, DeepEP and megatron-core, 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: 8f76b822a284adbe24faaf3cfe725e65cb5e94f98ba54aceade45b275345f983 build: name: qwen3-tulu3-fixed8192-v1 tokenizer: Qwen3-32B (revision 9216db5781bf21249d130ec9da846c4624c16137; tokenizer.json sha256 aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4; tokenizer_config.json sha256 d5d09f07b48c3086c508b30d1c9114bd1189145b74e982a265350c923acd8101) loss_mask: qwen3 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: 0.677 parquet_sha256: 7ea7f5dca51f6be280e2f7cd7ee786ac05f546570e5ac1b07236c920338241fe parquet_bytes: 88808580 builder: data/generate_fixed_sft.py with --loss-mask-type qwen3 (data/README.md) layout: label: TP4 PP2 summary: TP 4, PP 2 (32 layers per stage), CP 1, DP 1, sequence parallel; two 8,192-token rows per microbatch (max 16,384 tokens per GPU); no recompute; precision-aware distributed optimizer; no offload; dense model, no experts highlights: max 16,384 tokens/GPU, no recompute, no offload tp: 4 pp: 2 vpp: 1 cp: 1 ep: 1 etp: 1 dp: 1 sequence_parallel: true layer_split: null max_tokens_per_gpu: 16384 rows_per_microbatch: 2 recompute: null offload: null dispatcher: null result: tokens_per_s_per_gpu: 2200.16 usd_per_million_tokens: 1.0 step_time_cv_percent: 0.45 mfu: 0.4269 mfu_note: null active_params: count: 31983534080 source: https://huggingface.co/Qwen/Qwen3-32B revision: 9216db5781bf21249d130ec9da846c4624c16137 measured: 2026-10-18 steps_per_run: 20 window: - 5 - 19 window_tokens: 15728640 runs: 1 profiler: false gpu_hours: 3.02 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-qwen3-32B.sh` (one 8-GPU node): TP 8 with sequence parallelism, PP 1, full recompute (uniform, 1 layer), max 20,480 tokens/GPU and CPU optimizer offload with overlapped copies and the precision-aware optimizer. Model arguments, rows and schedule are the same in both recipes.' 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: H200 date: 2026-10-11 result: pass detail: worst tensor (update) relative L2 error 5.12e-7 on the GPU and 7.98e-7 offloaded against a bound of 1.0e-3 after one step, and 4.99e-5 and 7.72e-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 this recipe's training image (the same image configuration); the optimizer step is elementwise, so the result holds for every model on the image convergence: steps: 20 result: pass correlation: 0.9999998 final_loss_difference: 0.00012 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' rules: argument classes of ATTUNE's skill/scripts/correctness/rules/numerics_list.yaml (amazon-science/attune f2cb450); 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: [] 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 \ --calculate-per-token-loss \ --disable-compute-advantages-and-returns \ --debug-train-only \ --tensor-model-parallel-size 4 \ --sequence-parallel \ --pipeline-model-parallel-size 2 \ --context-parallel-size 1 \ --expert-model-parallel-size 1 \ --expert-tensor-parallel-size 1 \ --use-dynamic-batch-size \ --max-tokens-per-gpu 16384 \ --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 Qwen3-32B script, `scripts/run-qwen3-32B.sh` with `scripts/models/qwen3-32B.sh` at slime 0.3.0. slime ships it as a one-node GRPO script that shares the GPUs with its rollout engine and publishes no tokens/s figure for it. Ported to this workload with its layout (TP 8 with sequence parallel, PP 1, full recompute, max 20,480 tokens/GPU, Adam on the CPU) on 8 H200, it measured 1,327.8 tokens/s/GPU; ATTUNE tuned it to 2,200.2 (+66%). - No layer is recomputed, so activation memory grows with the tokens per GPU: the tuning runs at max 24,576 tokens per GPU, and at max 16,384 with PP 1 and DP 2, ran out of memory. Check the memory before raising `--max-tokens-per-gpu`.