# recipes/qwen3.5-122b-a10b/h200/attune-tp1-pp4-ep8.yaml # An OpenRecipes recipe; the fields are documented in schema/recipe.schema.yaml. schema_version: 1 model: Qwen3.5-122B-A10B gpu: type: H200 memory_gb: 141 count: 32 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' network: 8 GPUs per node, EFA, 3.2 Tb/s RDMA per node source: 'in-pod environment dump of the measured run''s training image (same digest), taken by the component tests on 2026-10-11: installed TE, PyTorch and DeepEP, Megatron-LM git commit; 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: 7a837641dcde9ebcac9d3ed5ed84ddd941fc9bfc173606cc701e9a3f51c307f1 build: name: qwen35-tulu3-fixed8192-v1 tokenizer: Qwen3.5 (the Qwen3.5-122B-A10B tokenizer; tokenizer.json sha256 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42) 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: 0.676 parquet_sha256: f215bdc6aba60d6d0dff4471eaef805765cb393e40b95762190535376491ab01 parquet_bytes: 76127393 builder: data/generate_fixed_sft.py (data/README.md) layout: label: TP1 PP4 EP8 DP8 summary: TP 1, PP 4, CP 1, EP 8, ETP 1, DP 8; one 8,192-token row per microbatch; full recompute (uniform, 1 layer); alltoall dispatcher with shared-expert overlap; load-balancing loss off; TE cross-entropy fusion; precision-aware distributed optimizer; no offload highlights: max 8,192 tokens/GPU, load-balancing loss off tp: 1 pp: 4 vpp: 1 cp: 1 ep: 8 etp: 1 dp: 8 sequence_parallel: false layer_split: null max_tokens_per_gpu: 8192 rows_per_microbatch: 1 recompute: granularity: full method: uniform num_layers: 1 offload: null dispatcher: alltoall result: tokens_per_s_per_gpu: 2366.73 usd_per_million_tokens: 0.93 step_time_cv_percent: 0.61 mfu: 0.1293 mfu_note: null active_params: count: 9008136192 source: https://huggingface.co/Qwen/Qwen3.5-122B-A10B revision: dc4d348443bc740c68e2d77492492c11606384d5 measured: 2026-10-11 steps_per_run: 14 window: - 4 - 13 window_tokens: 10485760 runs: 1 profiler: false gpu_hours: 8.2 gpu_hours_note: GPUs x (job start to end), including startup correctness: result: pass summary: passed, with the load-balancing loss off in both arms reference: Not published. configuration: pass component_tests: [] convergence: steps: 20 result: pass correlation: 0.999993 final_loss_difference: 0.000819 pointwise_failures: 0 note: both arms with the load-balancing loss off, same Tulu 3 rows and seed, on the measured run's training image; 0 of 16 scored points outside tolerance worst_step_tolerance_ratio: 0.026 rules: argument classes of ATTUNE's skill/scripts/correctness/rules/numerics_list.yaml; 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) # MODEL_ARGS: the model script's, with the load-balancing loss off python3 -m --async \ --actor-num-nodes 4 \ --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 14 \ --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 4 \ --context-parallel-size 1 \ --expert-model-parallel-size 8 \ --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 \ --moe-shared-expert-overlap \ --cross-entropy-loss-fusion --cross-entropy-fusion-impl te \ --distributed-timeout-minutes 30