# MI355X Kimi-K2.6 (MXFP4 experts, FP8 KV) 4-node 2P1D disaggregation recipe (MTP) — narrow-prefill EP8
# + wide-decode EP16 (mirrors the DSV4-Pro Oren config: wide EP only helps decode).
#
# Two prefill engines (EP8, one node each; the router fans requests across both) +
# one decode engine (EP16) spanning 2 nodes. Still one logical P/D pair per role
# group, 4 nodes total. nodes-per-engine = ceil(TP/8): prefill 8->1, decode 16->2,
# so the launcher emits cross-node --nnodes/--node-rank/--dist-init-addr for the
# decode engine only. Prefill EP8 keeps MoE all-to-all INTRA-node (XGMI); decode
# gets wide EP16 across nodes. KV (prefill TP8 -> decode TP16) is carried over mori.
#
# Kimi-specific bits vs the DSV4-Pro EP16 recipe: split attention backends
# (aiter prefill / triton decode), the Kimi model env + parsers, and the Kimi
# model path. Everything else (2P1D topology, mori a2a + KV, dist init) is shared.
#
# 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`, `bench`, `model`, `mtp`.

resources:
  prefill_workers: 2
  decode_workers: 1

backend:
  sglang_config:
    prefill:
      tensor-parallel-size: 8
      expert-parallel-size: 8
      data-parallel-size: 8
    decode:
      tensor-parallel-size: 16
      expert-parallel-size: 16
      data-parallel-size: 16

# Model-specific docker env + sglang server args (written verbatim via
# model_flags.sh). Each server arg + value is a SEPARATE list item.
model:
  env:
    SGLANG_USE_AITER: 1
    SGLANG_ROCM_FUSED_DECODE_MLA: 0
  server_args:
    - --model-loader-extra-config
    - '{"enable_multithread_load": true}'
    - --reasoning-parser
    - kimi_k2
    - --tool-call-parser
    - kimi_k2

runtime:
  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260715
  # Kimi uses split attention backends (aiter prefill / triton decode), not a
  # single --attention-backend.
  prefill_attention_backend: aiter
  decode_attention_backend: triton
  # RoCE HCAs (8/node) for mori MoE all-to-all AND the P->D KV transfer.
  ib_devices: rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
  # Wide-EP MoE all-to-all backend (cross-node expert dispatch/combine).
  moe_a2a_backend: mori
  # KV P->D transfer backend (mori for both a2a and KV on this cluster).
  kv_transfer_backend: mori
  # Cross-node torch-distributed NIC for the wide decode engine's dist init.
  dist_socket_ifname: eno0
  # rocm720 0715 image needs the ROCm-7.0.0-alpha path OFF (validated).
  rocm700a: 0
  prefill_port: 30025
  decode_port: 30026
  prefill_bootstrap_port: 8998
  decode_bootstrap_port: 9001
  lb_port: 8000
  # Base defaults; the wide_ep block overrides mem-fraction / max-req per role.
  mem_fraction_static: 0.90
  page_size: 256
  max_running_requests: 256
  chunked_prefill_size: 131072
  swa_full_tokens_ratio: 0.1
  # Per-role wide-EP tuning (starting point cloned from the validated DSV4-Pro
  # EP16 run; may need Kimi-specific retuning). Prefill and decode differ.
  wide_ep:
    kv_cache_dtype: fp8_e4m3
    prefill_mem_fraction_static: 0.8
    decode_mem_fraction_static: 0.85
    prefill_chunked_prefill_size: 131072
    prefill_max_running_requests: 1024
    decode_max_running_requests: 1024
    common_extra_flags: "--moe-dense-tp-size 1 --enable-dp-lm-head --decode-log-interval 100 --watchdog-timeout 3600 --load-balance-method round_robin"
    prefill_extra_flags: "--context-length 9217 --max-total-tokens 262144"
    decode_extra_flags: "--disable-cuda-graph"
    prefill_extra_env:
      MORI_MAX_DISPATCH_TOKENS_PREFILL: 8192
      MORI_MAX_DISPATCH_TOKENS_DECODE: 256
      SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK: 16384
    decode_extra_env:
      MORI_MAX_DISPATCH_TOKENS_DECODE: 64
      MORI_MOE_MAX_INPUT_TOKENS_DECODE: 332
      SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK: 128


# EAGLE3 speculative decoding with the external Kimi-K2.6 draft model (same
# draft + hyperparams as the validated EP8 1p1d-mtp.yaml). Decode stays eager
# (--disable-cuda-graph); MXFP4 experts run the aiter MoE path (no int4 route-1
# dense-compaction), so cuda-graph could be revisited once the leg is green.
mtp:
  enabled: true
  algorithm: EAGLE3
  num_steps: 3
  eagle_topk: 1
  num_draft_tokens: 4
  draft_model_path: /it-share/model_coverage/models--lightseekorg--kimi-k2.6-eagle3.1-mla

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 > 0.92). Mirrors the single-node Kimi-K2.6 eval threshold.
  accuracy:
    enabled: true
    num_shots: 8
    num_questions: 1319    # full GSM8K test set
    threshold: 0.92