# recipes/glm-4.7-flash/h200/attune-tp1-pp1-ep8.yaml # An OpenRecipes recipe; the fields are documented in schema/recipe.schema.yaml. schema_version: 1 model: GLM-4.7-Flash 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: miles 0.1.0 (radixark/miles commit 78527b91, a slime fork), no local changes megatron_lm: radixark/Megatron-LM commit 37f91617 (megatron-core 0.16.0rc0), no local changes transformer_engine: 2.17.0 pytorch: 2.11.0 (CUDA 13.0 build) cuda: '13.0' nccl: 2.28.9 (CUDA 13.0 build; printed by the measured run) deepep: 1.2.1 (installed; the alltoall dispatcher does not use it) python: 3.12.3 network: 8 GPUs per node, EFA (aws-ofi-nccl 1.20), 3.2 Tb/s RDMA per node source: 'version dump of the measured run''s training image (same digest), 2026-10-13: installed TE, PyTorch, DeepEP, megatron-core and miles, Megatron-LM and miles git commits (clean trees), 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: 79bfe4b7e20e66ec85c12caf4995ea9574bbf6d316772e4d69193a01efe01914 build: name: glm47flash-tulu3-fixed8192-v1 tokenizer: GLM-4.7-Flash (revision 7dd20894a642a0aa287e9827cb1a1f7f91386b67; tokenizer.json sha256 19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d; chat_template.jinja sha256 d63ad536c3c81880043e22ec7fd08db42b4d8fb7c89c7138bc562bfa25281375) loss_mask: glm5 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: 0.674 parquet_sha256: 33e50f503dee9999e27c16a9ae1781c822ad1acc012b4b52c2efccfd9c8bbcb4 parquet_bytes: 83603534 builder: data/generate_fixed_sft.py with patch_glm5_mask.py (data/README.md) layout: label: TP1 PP1 EP8 DP8 summary: TP 1, PP 1, CP 1, EP 8, ETP 1, DP 8; two 8,192-token rows per microbatch (THD, max 16,384 tokens/GPU); full recompute (uniform, 1 layer); distributed optimizer with the precision-aware optimizer; alltoall dispatcher; no offload highlights: max 16,384 tokens/GPU, no offload tp: 1 pp: 1 vpp: 1 cp: 1 ep: 8 etp: 1 dp: 8 sequence_parallel: false layer_split: null max_tokens_per_gpu: 16384 rows_per_microbatch: 2 recompute: granularity: full method: uniform num_layers: 1 offload: null dispatcher: alltoall result: tokens_per_s_per_gpu: 6178.46 usd_per_million_tokens: 0.36 step_time_cv_percent: 0.95 mfu: 0.1342 mfu_note: null active_params: count: 3579314176 source: https://huggingface.co/zai-org/GLM-4.7-Flash revision: 7dd20894a642a0aa287e9827cb1a1f7f91386b67 measured: 2026-10-15 steps_per_run: 20 window: - 5 - 19 window_tokens: 15728640 runs: 1 profiler: false gpu_hours: 1.34 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: 'miles'' layout for GLM-4.7-Flash (`scripts/run_glm47_flash.py`): TP 4 with sequence parallel, EP 8, full recompute (uniform, 1 layer), max 32,768 tokens/GPU and Adam on the CPU (`--optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer`), here on 8 GPUs (DP 2). 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: 2026-10-14 result: pass detail: worst tensor (dense 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 three negative controls fail note: run 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: 0.9779 final_loss_difference: 0.0032 pointwise_failures: 1 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 TILELANG_CACHE_DIR: $KERNEL_CACHE/tilelang TRITON_CACHE_DIR: $KERNEL_CACHE/triton TORCHINDUCTOR_CACHE_DIR: $KERNEL_CACHE/inductor FI_PROVIDER: efa FI_EFA_USE_DEVICE_RDMA: '1' patches: [] run: | export CUDA_DEVICE_MAX_CONNECTIONS=1 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True export TILELANG_CACHE_DIR="$KERNEL_CACHE/tilelang" export TRITON_CACHE_DIR="$KERNEL_CACHE/triton" export TORCHINDUCTOR_CACHE_DIR="$KERNEL_CACHE/inductor" export FI_PROVIDER=efa export FI_EFA_USE_DEVICE_RDMA=1 MODEL_ARGS_LINE="$(python3 miles/utils/external_utils/model_args_utils.py glm4.7-flash)" read -ra MODEL_ARGS <<< "$MODEL_ARGS_LINE" python3 train.py \ --actor-num-nodes 1 \ --actor-num-gpus-per-node 8 \ --num-gpus-per-node 8 \ "${MODEL_ARGS[@]}" \ --hf-checkpoint "$HF_CKPT" \ --ref-load "$TORCH_DIST" \ --loss-type sft_loss \ --calculate-per-token-loss \ --disable-compute-advantages-and-returns \ --debug-train-only \ --rollout-function-path pretokenized_sft_rollout.generate_rollout \ --prompt-data "$SFT_DATA" \ --input-key messages \ --metadata-key metadata \ --num-rollout 20 \ --rollout-batch-size 128 \ --global-batch-size 128 \ --tensor-model-parallel-size 1 \ --pipeline-model-parallel-size 1 \ --context-parallel-size 1 \ --expert-model-parallel-size 8 \ --expert-tensor-parallel-size 1 \ --recompute-granularity full \ --recompute-method uniform \ --recompute-num-layers 1 \ --use-dynamic-batch-size \ --max-tokens-per-gpu 16384 \ --use-precision-aware-optimizer \ --attention-backend flash \ --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 \ --attention-dropout 0.0 --hidden-dropout 0.0 \ --accumulate-allreduce-grads-in-fp32 --attention-softmax-in-fp32 \ --distributed-timeout-minutes 30 notes: | - Starting point: miles' GLM-4.7-Flash launcher, `scripts/run_glm47_flash.py` at miles 0.1.0 (model page https://miles.radixark.com/docs/models/glm/glm4-7-flash). miles publishes it as a GRPO recipe with no tokens/s figure. Ported to this workload with its layout (TP 4 with sequence parallel, EP 8, full recompute, max 32,768 tokens/GPU, Adam on the CPU) on 8 H200, it measured 3,142.9 tokens/s/GPU; ATTUNE tuned it to 6,178.5 (+97%). - The loss starts 1.0% above the reference recipe's (1.1580 against 1.1465 at step 0; Hugging Face transformers gives 1.1536 on the same 128 rows). In steps 0-3, where both recipes' gradient norms run at 16-276 before clipping to 1.0, it rises to 1.34 at step 3 against the reference's 0.97; from step 6 it stays within 1.2% of the reference and from step 12 within 0.5%.