# GLM-5.2 NF4 Lloyd-Experts-GPTQ — 4× DGX Spark (GB10, sm_121a), TP=4, DCP=4.
#
# Model: aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ
#   → https://huggingface.co/aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ
#
# Lloyd-codebook NF4 routed-expert overlay for GLM-5.2 (744B MoE, 256
# experts/layer, 8 activated/token, 78 layers, sparse-MLA). Expert weights
# quantized to 4-bit with a fitted Lloyd–Max codebook (−16.8% expert
# reconstruction error vs E2M1/MXFP4 baseline on the same GPTQ pipeline).
# Codes are indices into per-layer grids/L{n}.json — NOT the E2M1 table.
#
# LOADING via [composed_model]: the Lloyd repo is experts-only (no config,
# no tokenizer, no non-expert weights). The launcher builds a /tmp dir per
# node with symlinks into the HF cache and bind-mounts it at /root/model:
#   1. aidendle94/GLM-5.2-MXFP4-Experts-GPTQ (base checkpoint — config.json
#      with quant_method="hybrid_mxfp4_ct", hybrid-ct-*.safetensors non-expert
#      weights, tokenizer, mtp-draft) → all files symlinked.
#   2. aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ (Lloyd NF4 experts) →
#      L*.safetensors symlinked, OVERWRITING the MXFP4 E2M1 experts
#      (later sources win). Same filenames, same tensor names — the Lloyd
#      codes replace the E2M1 codes.
#   3. nf4_layers.json (generated) — tells the kernel which layers use the
#      nf4_e8m0_k32 decode path vs the default fp4_e8m0_k32 E2M1 bit-twiddle.
#
# The symlinks point to container-side paths (/root/.cache/huggingface/...)
# so they resolve inside the container where the HF cache is mounted. The
# _NF4_CODEBOOK is GLOBAL and hardcoded in the b12x binary (fitted as the
# mean of 71 per-layer Lloyd–Max tables) — the per-layer grids/L{n}.json
# files are reference artifacts, NOT loaded at runtime.
#
# Image: sparkrun-vllm-ds4-gb10:glm52
#   Built from docker-images/glm52/Dockerfile, which FROMs
#   aidendle94/sparkrun-vllm-ds4-gb10:production-hybrid-1.4 (bakes the
#   nf4_e8m0_k32 / nf4_packed kernel — inert without VLLM_NF4_LAYERS).
#
# PREREQUISITES:
#   - Both repos cached on every node via HF_HOME:
#       huggingface-cli download aidendle94/GLM-5.2-MXFP4-Experts-GPTQ
#       huggingface-cli download aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ
#     (MXFP4 base ~390 GB, Lloyd experts ~385 GB)
#   No manual overlay or JSON creation — [composed_model] handles everything.

engine = "vllm"
name = "GLM-5.2 NF4 Lloyd-Experts-GPTQ (vLLM)"
docker_image = "sparkrun-vllm-ds4-gb10:glm52"
model = "aidendle94/GLM-5.2-MXFP4-Experts-GPTQ"
required_nodes = 4
tensor_parallel_size = 4
pipeline_parallel_size = 1
distributed_backend = "mp"
shm_size = "10gb"
docker_image_dir = "glm52"

entrypoint = ["vllm", "serve"]

args = [
    # Model name
    "--served-model-name", "glm52",
    # General
    "--trust-remote-code",
    "--reasoning-parser", "glm45",
    "--tool-call-parser", "glm47",
    "--enable-auto-tool-choice",
    "--enable-prefix-caching",
    # DCP (Decode Context Parallelism)
    "--decode-context-parallel-size", "4",
    "--dcp-comm-backend", "ag_rs",
    # Engine backends
    "--attention-backend", "B12X_MLA_SPARSE",
    # HF overrides — 78-char index_topk_pattern (GLM-5.2 MLP/expert routing)
    "--hf-overrides", """
'{"index_topk_pattern":"FFFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSS"}'""",
    # KV cache — NVFP4 with per-token scaling (nvfp4_ds_mla)
    "--kv-cache-dtype", "nvfp4_ds_mla",
    "--kv-cache-memory-bytes", "5500000000",
    # Context & batch config
    "--max-model-len", "262144",
    "--max-num-batched-tokens", "2048",
    "--max-num-seqs", "3",
    "--gpu-memory-utilization", "0.89",
    # Speculative decode — MTP k=4 with adaptive depths
    # /root/model/mtp-draft is symlinked from the MXFP4 base checkpoint
    # (the composed dir includes it via source #1).
    #
    # NOTE: MTP + reasoning parser (glm45) + response_format (guided decoding)
    # requires the vLLM structured-output reasoning-boundary fix (upstream
    # PRs #44297 + #44993, forward-ported in docker-images/glm52/). Without
    # it, the grammar FSM fails to advance across the reasoning-end marker
    # (|◊|) under MTP, so the content channel is unconstrained and the model
    # emits prose/markdown-fence prefixes before JSON. The fix is baked into
    # the sparkrun-vllm-ds4-gb10:glm52 image.
    "--speculative-config", """
'{"model":"/root/model/mtp-draft","method":"mtp","num_speculative_tokens":4,"draft_attention_backend":"B12X_MLA_SPARSE","adaptive_speculative_tokens_window":32}'""",
    # Compilation — FULL_AND_PIECEWISE with all custom ops, 8 capture sizes
    "--compilation-config", """
'{"cudagraph_mode":"FULL_AND_PIECEWISE","custom_ops":["all"],"cudagraph_capture_sizes":[1,2,3,4,5,6,8]}'""",
    # Timeout safety net (cold boot: ranks finish minutes apart)
    "--distributed-timeout-seconds", "3600",
    "--cpu-distributed-timeout-seconds", "3600",
]

[composed_model]
mount = "/root/model"

[[composed_model.sources]]
repo = "aidendle94/GLM-5.2-MXFP4-Experts-GPTQ"

[[composed_model.sources]]
repo = "aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ"
glob = "L*.safetensors"
# Exclude the 5 layers with mis-packed experts (per model card known-defect
# workaround). These layers' Lloyd repo L{n}.safetensors contain 255 Lloyd-
# coded experts + 1 E2M1 expert — decoding as E2M1 (which nf4_layers.json
# does) would garbage-decode the 255 Lloyd experts. Instead, skip the Lloyd
# overlay for these files so the MXFP4 base's all-E2M1 L{n}.safetensors
# survives, and the excluded-from-NF4 E2M1 decode path matches their bytes.
exclude = [
    "L5.safetensors",
    "L8.safetensors",
    "L9.safetensors",
    "L32.safetensors",
    "L46.safetensors",
]

# NF4 layer list — all 75 MoE layers (3–77) EXCEPT the 5 layers with a
# known packing defect (per model card 2026-07-27). Layers 5, 8, 9, 32, 46
# each contain 1 expert whose bytes were packed with E2M1/MXFP4 nibbles
# (quantizer arm won per-expert, but decoding is per-layer). These layers
# are ALSO excluded from the Lloyd source above (exclude = [...]) so the
# MXFP4 base's all-E2M1 L{n}.safetensors survives — decoding as E2M1
# (which this list does) matches their bytes. Layer 10 had the same defect
# but was already caught + fixed upstream by the packing guard, so it
# stays in the NF4 set.
# Ref: https://huggingface.co/aidendle94/GLM-5.2-NF4-Lloyd-Experts-GPTQ
# ("Known defect: 5 experts are packed on the wrong grid")
[[composed_model.files]]
name = "nf4_layers.json"
content = '{"layers":[3,4,6,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77]}'

# Persistent JIT/torch-inductor cache (avoids ~20 min recompilation on cold boot).
# `alias = "jit_cache"` resolves via [mount_aliases] in config.toml; if absent,
# falls back to host "~/.cache/jit" (expanded to /home/<ssh_user>/.cache/jit).
# `env` injects -e XDG_CACHE_HOME=/root/.cache/jit so vLLM/torch write there.
[[extra_mounts]]
alias = "jit_cache"
host = "~/.cache/jit"
container = "/root/.cache/jit"
env = "XDG_CACHE_HOME"


[env]
# ── NF4 Lloyd expert overlay ──────────────────────────────────────────────
# Path to a JSON file listing which layers use the nf4_e8m0_k32 decode path
# (vs the default fp4_e8m0_k32 E2M1 bit-twiddle). Generated by [composed_model]
# at /root/model/nf4_layers.json — no placeholder needed (known container path).
VLLM_NF4_LAYERS = "/root/model/nf4_layers.json"

# ── b12x suite ────────────────────────────────────────────────────────────
VLLM_USE_V2_MODEL_RUNNER = "1"
VLLM_USE_B12X_MOE = "1"
VLLM_USE_B12X_SPARSE_INDEXER = "1"
VLLM_DCP_SHARD_DRAFT = "1"
VLLM_DCP_GLOBAL_TOPK = "1"
VLLM_DSV4_INDEXER_SP = "1"

# ── MLA CKV gather (sparse gather for decode) ─────────────────────────────
VLLM_B12X_MLA_CKV_GATHER = "1"
VLLM_B12X_MLA_CKV_GATHER_MAX_TOKENS = "262144"
VLLM_B12X_MLA_DECODE_SPARSE_GATHER = "1"
VLLM_B12X_MLA_DECODE_GATHER_V2 = "1"

# ── Speculative decode ────────────────────────────────────────────────────
VLLM_ADAPTIVE_SPEC_DEPTHS = "2,4"

# ── NVFP4 KV cache ────────────────────────────────────────────────────────
VLLM_NVFP4_MLA_PER_TOKEN_SCALE = "1"

# ── Timeout safety net ────────────────────────────────────────────────────
VLLM_ENGINE_READY_TIMEOUT_S = "3600"
VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS = "1800"
VLLM_ALLOW_LONG_MAX_MODEL_LEN = "1"

# ── MUST unset baked VLLM_PREFIX_CACHE_RETENTION_INTERVAL ─────────────────
# The image bakes a value that GLM-5.2's KV coordinator rejects.
# Empty string = unset at runtime.
VLLM_PREFIX_CACHE_RETENTION_INTERVAL = ""

# ── Architecture (aarch64/sm_121a DGX Spark) ──────────────────────────────
CUTE_DSL_ARCH = "sm_121a"
TORCH_CUDA_ARCH_LIST = "12.1a"

# ── JIT cache ──────────────────────────────────────────────────────────────
# Persisted via the [[extra_mounts]] jit_cache entry above: XDG_CACHE_HOME is
# injected from that mount, pointing at /root/.cache/jit (no hardcoded dupe).

# ── NCCL overrides ─────────────────────────────────────────────────────────
# Cluster config provides NCCL_IB_HCA, NCCL_IB_GID_INDEX, NCCL_SOCKET_IFNAME
# Launcher defaults: NCCL_IGNORE_CPU_AFFINITY=1, NCCL_NET=IB, NCCL_DEBUG=WARN
NCCL_IB_DISABLE = "0"             # enable IB (override launcher default)
NCCL_MAX_NCHANNELS = "4"
NCCL_MIN_NCHANNELS = "4"
NCCL_CUMEM_ENABLE = "0"

# ── Compute ────────────────────────────────────────────────────────────────
OMP_NUM_THREADS = "4"