# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe.
#
# GLM-5.2 uses GlmMoeDsaForCausalLM (DeepSeek Sparse Attention). sglang
# auto-selects the DSA attention backend for this architecture, so this recipe
# leaves `attention_backend` unset (empty) and lets the server pick DSA. MXFP4
# enables SGLANG_DSV4_FP4_EXPERTS in launch_mi355x.sh (driven by PRECISION),
# same as the DeepSeek-V4 FP4 path.
#
# NOTE: the checkpoint on disk is amd/GLM-5.1-MXFP4 (only GLM MXFP4 build
# currently mirrored on /it-share). model_path in nightly-configs.yaml points at
# it; the recipe naming tracks the model line we are wiring CI for.
#
# Consumed by:
#   * scripts/ci/slurm/process_result.py reads `resources` and
#     `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
#   * scripts/ci/slurm/launch_mi355x.sh reads `runtime` and `bench`.

resources:
  prefill_workers: 1
  decode_workers: 1

backend:
  sglang_config:
    prefill:
      tensor-parallel-size: 8
      expert-parallel-size: 1
      data-parallel-size: 1
    decode:
      tensor-parallel-size: 8
      expert-parallel-size: 1
      data-parallel-size: 1

# Model-specific docker env + sglang server args (generic launcher path; keeps
# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
# disabled to mirror the DeepSeek-V4 path.
model:
  env:
    SGLANG_USE_AITER: 1
  server_args:
    - --reasoning-parser
    - glm45
    - --tool-call-parser
    - glm45
    - --disable-shared-experts-fusion

runtime:
  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
  # attention_backend intentionally unset: DSA is auto-selected for
  # GlmMoeDsaForCausalLM.
  # RoCE HCAs MORI uses for cross-node KV transfer.
  ib_devices: rdma0,rdma1,rdma2,rdma3
  prefill_port: 30025
  decode_port: 30026
  prefill_bootstrap_port: 8998
  decode_bootstrap_port: 9001
  lb_port: 8000
  mem_fraction_static: 0.90
  page_size: 256
  max_running_requests: 256
  chunked_prefill_size: 8192
  swa_full_tokens_ratio: 0.1

bench:
  # bench_serving --max-concurrency sweep; one result JSON per concurrency.
  concurrencies: [1, 8, 16, 32, 64, 128, 256]
  num_prompts_factor: 4      # num-prompts = concurrency * factor
  random_range_ratio: 1.0

  # Correctness gate run through the PD path before the perf sweep
  # (full GSM8K, 8-shot, accuracy > 0.91). A regression here fails the nightly
  # even when throughput looks fine ("fast but wrong").
  accuracy:
    enabled: true
    num_shots: 8
    num_questions: 1319    # full GSM8K test set
    threshold: 0.91