archc 0.0.1

Hardware-aware Transformer-architecture compiler. Maps (hardware, workload, quality budget) to recommended architecture (dense, MoE, hybrid state-attention).

算模 AC — Architecture Compiler

AC is a compiler for model architecture design under hardware constraints. It can take hardware platform, param count, training tokens, serving workload, and an optional basemodel, AC optimizes for multi-objective Pareto-improving model architectures and architecture deltas. It currently supports greenfield generation, basemodel modification, and modifier eval. The goal is to make architecture design less like folklore and more like multi-objective Pareto optimization. It serves as a quantitative pre-flight check, it does NOT replace training.

Check out demo here: ac-demo

TL;DR (60s) — Give AC a hardware target + budget (ac-compile --hardware h100 --params 7 --tokens 2 …); get back a Pareto-optimal architecture, the binding constraint that decided it, and the price of relaxing it. Why trust it: an anchor validation study pre-registered predictions before comparing them to the public record — decision retrodiction 4 hit + 1 partial of 5 (DeepSeek-V3 MLA, Llama-3 GQA-8, Mistral SWA, first-K-dense, EP=72-on-NVL72), ranking fidelity Kendall τ = 1.0, with every miss published in an error taxonomy instead of deleted. Absolute numbers are priors until you calibrate — read the 3 lines below. Run it in 30s: pip install archc && ac-compile --hardware h100 --params 7 --tokens 2 --context 8192 --serving-tbt 50 --serving-batch 32 --tp 8 --pp 1 --dp 8

Three composable use cases, one shared config format:

• Greenfield Given compute -> architecture | ac-compile --hardware H --params N --tokens T …

• Modifier Given compute + a base architecture -> modifier | ac-compile --baseline-config CONF …

• Delta influence Given compute + base + delta -> influence | ac-delta-eval --baseline-config CONF …

Also check out the AC-Harness, which is a loop scaffold built to automate the process. It can also exist as a thin layer that sits beside existing training, eval, and benchmarking stack.

---

Install

# from PyPI (recommended)
pip install archc

# or, from a source checkout: editable install from the repo root
pip install -e .

# or run directly without installing (from the repo root)
python ac/cli_compile.py --help

After install the console scripts are on your PATH:

ac-compile        --help    # greenfield / modifier entry

No external runtime dependencies beyond Python ≥ 3.10 and PyYAML.

---

Calibration vs. Ordinal Use

AC is a forward proxy, not a measurement system. Without calibration, AC is ordinal:

• Pareto ranking. Given identical priors, two candidates' relative ordering on the (loss, TBT, memory, TPS) frontier is much more robust than either's absolute number. Use the Pareto CSV.
• Binding-axis identification. The 10-axis stress vector and shadow prices tell you which constraint would actually move quality if loosened. That's a structural answer; it doesn't depend on a tight loss calibration.

The current coefficients are calibrated to public ablations, used as priors and documented in ac/quality_defaults.yaml. The uncalibrated starter mode can be different from your private internal traces. The absolute loss numbers (and the TPS/TBT predictions) can be biased relative to what your stack actually produces.

Calibrate, if you want absolute numerals:

• Run ac-auto-calibrate fit --measurements <your_traces>.jsonl against ≥12 measured runs spanning the architecture families you care about (see the Auto-calibration section for the gates).
• Pass AC_QUALITY_DEFAULTS and AC_HARDWARE_SPEC_DIR from the resulting pack into ac-compile. The emitted config will then carry a confidence_envelope block and calibration_warnings will name any gates the pack didn't pass.
• Keep separate packs per cluster / kernel / datamix / training recipe — one global pack will mask all the interesting variance.

For any decision that depends on absolute loss (e.g. "is 7B at 2T better than 13B at 1T at our budget?"), an uncalibrated AC can be misleading. For comparative decisions ("does adding MLA relieve the binding axis without spending >1% loss on a frontier already at TP=8?"), AC's structural answer is the value it adds.

---

Quickstart

# 1) Greenfield: 7B dense on H100
ac-compile \
  --hardware h100 --params 7 --tokens 2 --context 8192 \
  --serving-tbt 50 --serving-batch 32 --tp 8 --pp 1 --dp 8 \
  --output-config out/mistral_arch.json \
  --output-justification out/mistral_arch.md \
  --output-pareto out/mistral_pareto.csv

# 2) Modifier: Pareto-front delta against Mistral-7B
ac-compile \
  --baseline-config configs/mistral_7b.json \
  --hardware h100 --tp-options 4,8 \
  --quality-risk-budget-pct 1.0 --allow-quality-spending \
  --out out/mistral_modifier

# 3) Delta influence: what does GQA(group_size=8) do to Mistral-7B at 32k?
ac-delta-eval \
  --baseline-config configs/mistral_7b.json \
  --hardware h100 --tp 8 --workload long_context \
  --apply swap_attention_to_gqa --apply-args group_size=8 \
  --out out/mistral_delta_gqa

---

Greenfield

Search the full architecture lattice for the Pareto-front winner under the given hardware and constraints. No baseline config required.

ac-compile [OPTIONS]

required
  --hardware {h100, b200, gb200_nvl72, h800, tpu_v5p, tpu_v5e, trainium2, trn2, trainium3, trn3}
  --params  N        (billions; supports "7" or "7B")
  --tokens  N        (trillions; supports "2" or "2T")

ac-compile args

workload
  --context           int       sequence length (default 8192)
  --prompt-len        int       prefill length override
  --output-len        int       expected generation length (default 512)
  --concurrency       int       concurrent serving requests (default 256)
  --scheduler         {continuous, static, chunked}

selection
  --objective-profile  research_quality | loss_only | balanced | quality |
                       latency | serving_cost | training_cost
                       default: research_quality

serving budgets
  --serving-tbt   ms      time-between-tokens p95
  --serving-ttft  ms      time-to-first-token p95
  --serving-batch int     batch size (default 32)

parallelism
  --tp / --pp / --dp   degrees (defaults 8/1/8)
  --training-cluster-gpus  minimum cluster size; derives candidate-specific
                           DP across TP/PP/CP search and rounds DP for EP
  --cp / --cp-method   context parallel: ring | ulysses
  --cp-options         comma-list of CP degrees to sweep

architecture sweeps
  --allow-state            enable state/hybrid candidates
  --state-type             mamba2 | mamba | s4 | s5 | s6
                           | gla | kda | deltanet | gated_deltanet
                           | rwkv7 | retnet | linear_attention
                           | parallel_heads | moh | hydra
                           | sliding_window
  --placement-strategy     first_periodic_last, interleaved, periodic
  --state-precision        bf16 | fp16 | fp32

  --allow-moe              enable MoE FFN candidates
  --max-total-params-b     MoE memory ceiling (total params billions)
  --moe-n-experts          comma-list (e.g. "64,128,256")
  --moe-top-k              comma-list
  --moe-granularity        comma-list of expert-granularity targets
                           (1.0 = coarse Mixtral-style; 0.25 =
                           DeepSeek-V3-style fine-grained experts)
  --ep-topology            single_axis | cross_axis
  --dense-ffn-layers       comma-list of first-K-dense layer counts
  --ep-options             comma-list of expert-parallel degrees

  --allow-mla              enable Multi-head Latent Attention candidates
  --mla-kv-latent          comma-list of c_kv options (default 512)
  --mla-q-latent           comma-list of c_q  options (default 1536)

  --allow-local-global     enable local:global attention interleave sweep
                           (GPT-OSS / Gemma-2 / Llama-4 pattern): a fraction
                           of layers use sliding-window attention, the rest
                           stay global (full/GQA, or MLA with --allow-mla)
  --local-windows          comma-list of local windows (default "1024,4096")
  --local-global-ratios    comma-list of local:global ratios
                           (default "1:1,3:1,7:1")

  --allow-mtp              enable Multi-Token Prediction
  --mtp-depths             comma-list (e.g. "0,1,2")

  --allow-rope-scaling             enable per-method RoPE sweep
  --rope-original-max-position     pretrain context (default 8192)
  --rope-scaling-methods           comma-list: yarn,ntk,longrope,pi,none

evaluated attention transforms (scored by quality + throughput models)
  --nsa                            require Native Sparse Attention on every candidate
    --nsa-compress-block-size      (default 64)
    --nsa-compress-block-stride    (default 16)
    --nsa-select-block-size        (default 64)
    --nsa-select-top-k             (default 16)
    --nsa-window-size              (default 512)

  --yoco                           require YOCO KV sharing on every candidate
    --yoco-n-self-attn-layers      (default 1)
    --yoco-share-pattern           single_source (the calibrated YOCO topology)

compressed / indexer attention sweeps (Wave 32)
  --allow-csa                      Compressed Sparse Attention candidates
    --csa-block-sizes              comma-list (default 64,128)
    --csa-top-k-blocks             comma-list
    --csa-compression-dim          (default 64)
  --allow-indexshare               DSA-style bucketed lightning-indexer candidates
    --indexshare-buckets           comma-list (default 64,128)
    --indexshare-top-k             comma-list (default 4,8)
    --indexshare-index-dim         (default 64)
  --allow-msa                      multi-scale attention candidates
    --msa-windows                  comma-list (default 512,1024)
    --msa-dilated-top-k            comma-list
    --msa-global-top-k             comma-list

precision search
  --precision-modes        comma-list: bf16, fp8_ffn, fp8, fp4, mxfp4, mxfp6
  --kv-dtypes              comma-list: bf16, int8, fp8, int4, fp4

outputs (paths)
  --output-config           default arch.json
  --output-justification    default arch.md
  --output-pareto           default pareto.csv
  --output-shadow-prices    default shadow_prices.json
  --output-assumptions      default not written
  --output-model-card       default not written
  --output-implementation   generated PyTorch architecture scaffold
  --implementation-class-name  class name for --output-implementation
  --no-shadow-prices        skip the shadow-price pass (faster)
  --max-candidates          optional greenfield cap after candidate dedupe
  --progress-every          print evaluation progress every N candidates
                            (default auto: every 1000 on large searches)
  --quiet                   suppress progress logs

Training memory and DP communication assume FSDP/ZeRO-3: weights, gradients, and optimizer state are sharded across DP ranks. The training replica is TP x PP x CP; EP overlays DP for training but expands a serving instance. Compare topology choices with the emitted per-GPU, per-replica, and aggregate TPS fields rather than the legacy training_tps field alone.

Alternatively you can also use yaml to pass in args (more details below):

ac-compile --recipe configs/recipes/<YOUR RECIPE>.yaml

Output and example

One row per Pareto-frontier candidate, sorted by the same uncertainty-aware tiebreak the picker uses, so rank=1 always agrees with the row that has selected=True. Loss columns:

• predicted_loss — point estimate from the quality model spine + residuals.
• loss_ci_low, loss_ci_high — symmetric uncertainty band around predicted_loss (half-width = uncertainty_total_pct/100 × predicted_loss). Populated for every row. Two rows whose [loss_ci_low, loss_ci_high] intervals overlap are quality-equivalent within modeled uncertainty; prefer the one that dominates on the throughput/memory axes.
• uncertainty_total_pct — quality-model total relative uncertainty (%). When auto-calibrate runs against your lab traces and writes a quality_overrides.json pack, this column is the scaled, post-calibration uncertainty.

Treat the loss column as a ranking signal rather than a forecast unless you have run ac-auto-calibrate against your lab's measurements.

#### example: MAI-Thinking-1-ish

```bash
ac-compile \
  --hardware b200 --params 35 --tokens 8 --context 131072 \
  --serving-tbt 60 --serving-batch 8 \
  --tp 8 --pp 4 --dp 4 \
  --allow-moe --moe-n-experts 256 --moe-top-k 8 \
  --allow-mla --mla-kv-latent 512 --mla-q-latent 1536 \
  --allow-mtp --mtp-depths 0,1 \
  --allow-rope-scaling --rope-original-max-position 32768 \
                       --rope-scaling-methods longrope \
  --cp 4 --max-total-params-b 800 \
  --output-config out/mai_arch.json --no-shadow-prices

Modifier

Holds the architecture family fixed (uses the baseline as anchor) and searches the local Pareto-frontier of modifications around it.

ac-compile --baseline-config PATH [OPTIONS]

required
  --baseline-config  PATH    JSON config emitted by greenfield or any existing model stripped in the format 

modifer args

scoring
  --allow-quality-spending   allow non-zero loss-proxy delta
  --quality-risk-budget-pct  max loss-proxy %-delta (default 1.0)
  --top-modifications        rows to render in reports (default 8)

parallelism sweep
  --tp-options       comma-list (e.g. "4,8")

workload
  --context, --serving-tbt, --serving-ttft, --serving-batch, --prompt-len

output
  --out DIR     destination for config.json + baseline_delta.md +
                pareto.csv + shadow_prices.md + justification.md +
                assumptions.md + model_card.md

Modifier mode writes fixed file names into the --out directory; the greenfield --output-config / --output-* path flags do not apply here and are ignored with a stderr warning.

Modifier mode preserves the baseline's architecture family exactly (including state/MoE/MLA/compressed attention, MTP, CP, RoPE, NSA, YOCO, and local/global layout) while searching nearby depth, KV-head, FFN/expert-width, precision, KV-cache, and TP choices. Greenfield-only family-enabling flags do not add a new family to an existing baseline; use delta-eval for an explicit family transition or greenfield mode for a broad family search.

Delta influence

Quantitative effect of one (or a chain of) named transformations against a specific baseline architecture. Outputs a one-page Markdown report + JSON

• 3-row Pareto CSV.

ac-delta-eval --baseline-config PATH --apply NAME [OPTIONS]

required
  --baseline-config  PATH    JSON config (greenfield output OR hand-written)
  --apply            NAME    one of REGISTRY (repeatable)
    --apply-args  k=v        args for the most recent --apply (repeatable)

Delta args

baseline / hw
  --hardware    h100 | b200 | tpu_v5p | trainium2 | trn2 | trainium3 | trn3
  --tp / --pp / --dp

workload preset (preset = chat | batched | long_context | training)
  --workload          PRESET   default chat
  --serving-batch     int      override the preset
  --context-length    int      override the preset
  --prompt-len        int

other
  --no-pareto        skip Pareto-position classification (faster)
  --json             emit JSON only (no Markdown / CSV)
  --stdout           print Markdown to stdout instead of writing files
  --out      DIR     destination directory

Available delta names (REGISTRY):

Name	Effect	Legal --apply-args keys
swap_attention_to_gqa	Set n_kv_heads = n_heads / group_size	group_size
swap_attention_to_mla	Replace full attention with MLA at latent_dim	latent_dim
swap_attention_to_swa	Sliding-window attention at window_size	window_size
interleave_local_attention	Local:global interleave — ratio of layers become SWA at window, rest stay global	ratio (e.g. "3:1"), window
add_state_layers	Replace a fraction of attention with a state mixer	ratio (e.g. "1:3"), state_type
densify_first_k	Convert the first K MoE layers back to dense	k
change_moe_topology	Reshape an MoE block	n_experts, top_k
change_precision_per_component	Weight, activation, and KV precision	weight, activation, kv
change_parallelism	Swap TP / PP / EP / CP degrees	tp, pp, ep, cp
scale_d_model	Shift d_model by delta, aligned to align	delta, align
scale_n_layers	Shift n_layers by delta	delta

Unknown delta names or kwarg keys fail fast with a structured error before any evaluation runs.

Sequence example

# What if we run both MLA *and* add state layers on GPT-OSS-120B?
ac-delta-eval \
  --baseline-config configs/gpt_oss_120b.json \
  --hardware h100 --tp 8 --workload chat \
  --apply swap_attention_to_mla --apply-args latent_dim=256 \
  --apply add_state_layers      --apply-args ratio=1:3 \
  --out out/gpt_oss_mla_state

The deltas compose left-to-right; the report describes the cumulative effect on the metric panel, stress vector, quality decomposition, and Pareto position.

---

Base-model and CLI arg config

Input base model config format

Schema version 0.3. JSON. One layer_configs entry per uniform layer band. A first-K-dense MoE config uses two entries (first K layers dense, rest MoE). See configs/mistral_7b.json for the dense reference and configs/{gpt_oss_120b, mai_thinking_1}.json for MoE and MoE+MLA.

Base model config format

{
  "schema_version": "0.3",
  "metadata": {
    "model_name": "your-model",
    "source_note": "free-form provenance"
  },
  "parallelism": {
    "tensor_parallel":   8,
    "pipeline_parallel": 1,
    "data_parallel":     8,
    "expert_parallel":   8,    // MoE memory: per-rank expert count = n_experts / ep
    "context_parallel":  1,    // splits sequence axis
    "cp_method":         "ring"
  },
  "architecture": {
    "d_model": 4096,           // MUST equal n_heads × d_head; the loader rejects mismatches
    "n_layers": 32,
    "vocab_size": 32000,
    "positional_encoding": {
      "type": "rope",
      "base": 1000000,
      "scaling": {                // optional; "none" = unmodified RoPE
        "method": "yarn",         // yarn | ntk | longrope | pi | none
        "factor": 4.0,
        "original_max_position": 8192
      }
    },
    "mtp": {                      // optional Multi-Token Prediction
      "n_predict_depths": 1,
      "depth_n_layers": 1,
      "share_embeddings": true,
      "train_loss_weight": 0.3,
      "inference_mode": "drop"
    },
    "layer_configs": [
      {
        "layer_idx": [0, 1, /* … */, 31],
        "type": "transformer_block",
        "attention": {
          "type": "full",         // full | mla | nsa
          "n_heads": 32,
          "n_kv_heads": 8,
          "d_head": 128,
          "rope": true,
          "kv_cache_bits": 16,
          "precision": {"qk": "bf16", "v": "bf16", "output": "bf16"}
        },
        "ffn": {
          "type": "swiglu",       // swiglu | moe
          "ffn_dim": 14336,
          "precision": "bf16"
        },
        "normalization": {"type": "rmsnorm", "eps": 1e-5, "precision": "bf16"},
        "residual_dtype": "bf16",
        "state": null             // or {"type": "mamba2|gla|kda|...", "d_state": 64, "n_heads": 72, "d_head": 64}
      }
    ]
  }
}

The baseline loader threads parallelism.expert_parallel and parallelism.context_parallel into the candidate, which is required for MoE configs to evaluate correctly. If you hand-write an MoE config and forget expert_parallel, the throughput model will place all experts on every rank and the quality model will return its INFEASIBLE marker.

Alternative using YAML or TOML for recipe configurations instead of flags:

ac-compile --recipe configs/recipes/h100_dense_7b.yaml \
  --override params=70 \
  --output-config out/arch.json

Key commands:

• --recipe PATH: Load a saved configuration.
• --override KEY=VALUE: Modify individual recipe values.
• --print-recipe PATH: Save the resolved configuration from a run.
• ac-compile config show: Preview the resolved config, output paths, and warnings without running a search.
• ac-compile init TEMPLATE --out PATH: Create a recipe from a built-in template.
• --help-group GROUP: Show help for one flag group, such as serving, moe, precision, or recipe.

Example templates are available in configs/recipes/:

h100_dense_7b.yaml
b200_moe_mla_long_ctx.yaml
delta_mistral_gqa_long_ctx.yaml

ac-delta-eval also supports inline delta arguments:

ac-delta-eval --apply 'swap_attention_to_mla{latent_dim=256,heads=8}'

---

Other Layers

Stress diagnostic layer

ac-stress gives you the 10-axis stress vector for any architecture: HBM bandwidth, KV footprint, tensor-core utilization, SRAM tile fit, all-reduce pressure, all-to-all pressure, training memory, and more. ac-stress transition ranks every named architectural change by binding-axis relief. The justification output names the constraint explicitly — "Selected MLA because HBM-BW-decode is binding at 0.94; MLA relieves to 0.46. Cost: +0.008 attention residual" — not just the change.

Auto-calibration

Use ac-auto-calibrate to fit lab-local uncertainty and hardware-efficiency overlays from measured runs. It accepts JSON, JSONL, or CSV rows with flexible field names.

Minimal data needed for auto calibration

Minimal row

{
  "id": "h100_mistral_7b_decode",
  "hardware": "h100",
  "architecture_family": "dense_gqa",
  "model_type": "dense",
  "active_params_b": 7.2,
  "total_params_b": 7.2,
  "training_tokens": 2.0,
  "context_length": 8192,
  "predicted_loss": 2.03,
  "observed_loss": 2.08,
  "predicted_uncertainty_total_pct": 3.1,
  "eval_scores": {
    "mmlu_pro": 0.421,
    "gpqa": 0.311
  },
  "predicted_evals": {
    "mmlu_pro": 0.409,
    "gpqa": 0.298
  },
  "predicted_training_tps": 11800,
  "observed_training_tps": 10400,
  "predicted_serving_tbt_ms": 6.2,
  "observed_serving_tbt_ms": 7.1,
  "predicted_prefill_time_ms": 34.0,
  "observed_prefill_time_ms": 39.0
}

Fit a pack:

ac-auto-calibrate fit \
  --measurements lab_measurements.jsonl \
  --out out/lab_calibration \
  --target-coverage 0.90 \
  --min-quality-rows 12 \
  --min-eval-rows 12 \
  --min-eval-families 3 \
  --min-hardware-rows 3 \
  --max-hardware-scatter-p90-pct 15

Two backend switches expose the calibration surface:

• --backend {ridge, hierarchical} — ridge (default) runs the current fitter. hierarchical is a stubbed posterior backend that exits with a "not yet implemented" message.
• --public-anchor-gate {on, off} — release gate. When on (default), the fitter runs the public-model predictive-accuracy audit at post-calibration tolerances; any failing entry demotes the pack from production_ready to experimental and writes a per-model breakdown to public_anchor_report.md alongside the pack.

An editable starter file is included at examples/lab_measurements.example.jsonl.

Outputs:

out/lab_calibration/
  calibration_pack.json      full fit summary
  quality_overrides.json     overlay for quality uncertainty calibration
  hardware_specs/*.json      copied + tuned hardware specs
  report.md                  human-readable calibration report
  public_anchor_report.md    anchor pass/fail table (when gate is on)

Use the pack without editing source files

AC_QUALITY_DEFAULTS=out/lab_calibration/quality_overrides.json \
AC_HARDWARE_SPEC_DIR=out/lab_calibration/hardware_specs \
ac-compile --hardware h100 --params 7 --tokens 2 ...

Quality calibration scales uncertainty intervals; it does not bias-correct the loss point estimate. Hardware calibration adjusts training_system_efficiency, decode_system_efficiency, and prefill_system_efficiency from median observed/predicted ratios.

When rows include eval_scores, the fitter also writes ridge eval models with held-out architecture-family CV. The overlay marks the pack as production_ready only when the configured sample gates pass; otherwise compile outputs carry metadata.predicted.calibration_warnings. Greenfield configs also include:

{
  "confidence_envelope": {
    "loss_low": 1.91,
    "loss_high": 2.11,
    "robust_to_loss_uncertainty": false,
    "contending_candidates": 7
  },
  "eval_predictions": {
    "mmlu_pro": {
      "score": 0.438,
      "uncertainty": 0.021,
      "heldout_family_rmse": 0.019
    }
  }
}

Keep separate packs for materially different clusters, kernels, schedulers, recipes, and datamixes.

Zero-compute calibration

Two subcommands sharpen decisions without any training runs:

• ac-auto-calibrate fit-pairs — fits per-residual-term scale factors from a corpus of published paired ablations (Waleffe/Jamba hybrids, Ainslie GQA/MQA, DeepSeek MLA/MoE/MTP, Gemma-2 and Mistral locality, YaRN/PI) and emits a coverage audit naming every term with zero constraining pairs. Add lab pairs in the same format to sharpen. Cross-paper scales are confounded by datamix/tokenizer — treat as priors, not lab truth.

  ac-auto-calibrate fit-pairs --out out/pairfit
  

• ac-auto-calibrate plan-ladder — generates (never runs) the cheapest paired-run ladder that resolves a named architecture decision: scores both arms at target scale, computes the paired sigma, and proposes scaled-down paired runs priced by AC's own throughput model. Emits plan.md, plan.json, and a measurement-template JSONL that feeds straight back into ac-auto-calibrate fit.

  ac-auto-calibrate plan-ladder --arm-a dense --arm-b moe \
    --params 13 --tokens 2 --context 8192 --out out/plan
  

Cost estimates (optional layer)

--cost-usd appends a cost_estimate_usd block (training_total / serving_per_1m_tokens / annual_serving_at_load) to any emitted config — greenfield and modifier. Pure-add: Pareto ranking and every existing field are unchanged, and with the flag off the output is byte-identical.

ac-compile --hardware h100 --params 7 --tokens 2 ... --cost-usd --price-tier on_demand

Price books live in ac/pricing_specs/*.json (on-demand / reserved-1y / spot, TDP, PUE) and are designed to be hot-updated — they are list prices with per-file provenance and access dates, not quotes. Demo: validation/e_pricing/.

---

Implementation export

Greenfield runs can also emit a standalone PyTorch module scaffold from the selected AC schema config:

ac-compile \
  --hardware h100 --params 7 --tokens 2 --context 8192 \
  --serving-tbt 50 --serving-batch 32 --tp 8 --pp 1 --dp 8 \
  --output-config out/arch.json \
  --output-implementation out/ac_model.py \
  --implementation-class-name ACModel

The generated file embeds the config as AC_CONFIG and defines an nn.Module class with dense/GQA/MLA attention, SwiGLU, MoE, RMSNorm, and state-block adapter slots. It uses PyTorch reference paths by default, tries flash-attn for attention when available, and lets labs provide their own installed kernels:

from ac_model import ACModel

model = ACModel(component_overrides={
    "attention:nsa": my_native_sparse_attention_forward,
    "state:gla": lambda d_model, config: MyFlaGlaBlock(d_model, **config),
    "state:mamba2": lambda d_model, config: MyMamba2Block(d_model, **config),
})

This artifact is meant as integration glue and shape-faithful reference code. For production pretraining, replace attention/state/MoE kernels with the lab's own FlashAttention, FLA, Mamba, expert-parallel, quantization, and checkpointing components.

---

Supported components

Hardware targets

Target	Peak BF16 / FP8 / FP4 (TF)	HBM	Interconnect	Tile path
NVIDIA H100 SXM	990 / 1980 / —	80 GB	NVLink 4 (900 GB/s)	wmma 16×16
NVIDIA B200	2 250 / 4 500 / 9 000 (MXFP4)	192 GB	NVLink 5 (1.8 TB/s)	wmma + MX
NVIDIA GB200 NVL72 (rack-scale, 72× B200)	2 250 / 4 500 / 9 000 (MXFP4)	192 GB ×72	NVLink 5 domain = 72 GPUs (1.8 TB/s per GPU, 130 TB/s rack); IB scale-out 400 Gb/s per GPU	wmma + MX
NVIDIA H800 SXM (H100 export SKU)	990 / 1980 / —	80 GB	NVLink 4 reduced (400 GB/s)	wmma 16×16
TPU v5p	459 BF16 / — / —	95 GB	ICI mesh	MXU 128×128
TPU v5e	197 BF16 / — / —	16 GB	ICI mesh	MXU 128×128
AWS Trainium 2	650 / 1 300 / —	96 GB	NeuronLink v3 (1.28 TB/s)	NCv3 128×128
AWS Trainium 3	1 300 / 2 600 / 5 200 (MX)	192 GB	NeuronLink v4 (2.4 TB/s)	NCv4 + FP4

gb200_nvl72 and h800 are system-level targets: the per-chip numbers are identical to B200 / H100 (same silicon); what changes is the fabric. gb200_nvl72 models the full rack as one NVLink domain (nvlink_domain_size = 72), which is what makes rack-scale expert parallelism (EP up to 72) priceable — see validation/c_nvl72/report.md (EP=72 on the Pareto frontier for a 1T-class 288-expert MoE on NVL72 vs mandatory spill + a −21% training all-to-all tax on single-node H100). h800 models the export-restricted H100 SKU whose NVLink is capped at 400 GB/s. Rack power, cooling, and failure rates are intentionally not modeled (see Roadmap).

The numbers above are the vendor datasheet dense Tensor-Core peaks. The peak_flops_tf field inside ac/hardware_specs/*.json is an effective per-precision baseline (typically ~50% of the datasheet peak for NVIDIA targets, equal to the datasheet for TPU and Trainium) that composes with calibration.efficiency_multipliers to recover measured production throughput. The _peak_flops_tf_convention field at the top of each NVIDIA spec explains this; the notes.peak_flops_source field cites the datasheet. If you fork a spec, keep both fields in sync.

attention + cache

Mechanism	attention.type	Greenfield flag	Delta name	Source
Full / MHA / GQA / MQA	full	(default; n_kv_heads sweeps)	swap_attention_to_gqa	—
MLA (Multi-head Latent Attention)	mla	--allow-mla --mla-kv-latent --mla-q-latent	swap_attention_to_mla	DeepSeek-V2/V3
NSA (Native Sparse Attention)	nsa	--nsa --nsa-{compress,select,window}-*	—	DeepSeek 2025
CSA (Compressed Sparse Attention)	csa	--allow-csa --csa-*	—	block-compressed KV + top-k blocks
IndexShare (bucketed lightning indexer)	indexshare	--allow-indexshare --indexshare-*	—	DSA-style shared top-k buckets
MSA (multi-scale attention)	msa	--allow-msa --msa-*	—	local + dilated + global top-k
SWA (Sliding Window Attention)	full + window	(via state-hybrid --state-type sliding_window)	swap_attention_to_swa	Mistral / Longformer
YOCO (You Only Cache Once)	architecture.yoco	--yoco --yoco-n-self-attn-layers --yoco-share-pattern	—	Sun et al. 2024 (Microsoft)

FFN families

Family	ffn.type	Greenfield flag	Delta name
SwiGLU dense	swiglu	default	—
MoE (top-k softmax router, optional shared expert, capacity factor)	moe	--allow-moe --moe-n-experts --moe-top-k --ep-options	change_moe_topology
First-K-dense MoE prefix (DeepSeek-V3 / Qwen3-MoE)	moe + 2 layer_configs	--dense-ffn-layers	densify_first_k

State / hybrid families

Hybrid layers replace a fraction of attention with a state mixer; the family controls which residual-quality term fires.

Family	--state-type aliases	Residual family	Source
Mamba-2 / Mamba / S4 / S5 / S6	mamba2, mamba, s4, s5, s6	mamba_sequential	Gu & Dao 2024
GLA / KDA / DeltaNet / Gated DeltaNet	gla, kda, deltanet, gated_deltanet	gated_delta_or_kda_linear	Yang 2024 / Kimi 2024
Parallel-heads (MoH / Hydra)	parallel_heads, moh, hydra	parallel_hybrid_heads	Jin 2024
Sliding-window / local recurrent	swa, sliding_window, local_recurrent	recurrent_local_attention	Beltagy 2020

Placement: --placement-strategy first_periodic_last,interleaved,periodic. State sizing (d_state) is SRAM-derived per hardware target. The CLI accepts the alias spellings above but emits canonical schema names where needed, e.g. swa / local_recurrent become sliding_window, delta_net becomes deltanet, and gated_delta becomes gated_deltanet.

Parallelism axes

Axis	Schema field	Greenfield flag
Tensor (TP)	parallelism.tensor_parallel	--tp
Pipeline (PP)	parallelism.pipeline_parallel	--pp
Data (DP)	parallelism.data_parallel	--dp
Fixed training cluster	derived candidate-specific DP	--training-cluster-gpus
Expert (EP)	parallelism.expert_parallel	--ep-options
Context (CP) — Ring / Ulysses	parallelism.context_parallel, cp_method	--cp --cp-method --cp-options

Positional encoding

Method	positional_encoding.scaling.method	Multiplier on long-ctx degradation	Source
None	none	1.00	baseline
PI (Position Interpolation)	pi	0.85	Chen 2023
NTK-aware	ntk	0.65	NousResearch 2023
YaRN	yarn	0.45	Peng 2024
LongRoPE	longrope	0.40	Ding 2024

Enabled via --allow-rope-scaling --rope-original-max-position N --rope-scaling-methods …. Beyond the trained extension range the multiplier snaps to 1.0.

Precision

Format	Weights / FFN	KV cache	Hardware (peak path)
BF16 / FP16	✓	✓ (16-bit)	all
FP8 (E4M3 / E5M2)	✓	✓ (8-bit)	H100, B200, Trn2, Trn3
INT8	—	✓ (8-bit)	all (KV only)
FP4 (E2M1)	✓	✓ (4-bit)	B200, Trn3
INT4	—	✓ (4-bit)	all (KV only)
MXFP4 (OCP microscaling)	✓	—	B200, Trn3
MXFP6	✓	—	B200, Trn3

Greenfield: --precision-modes bf16,fp8_ffn,fp8,fp4,mxfp4,mxfp6 and --kv-dtypes bf16,fp8,int8,fp4,int4. Hardware-specific filtering applies: FP4/MX modes are available on B200 and Trainium 3.

Other architectural primitives

Primitive	Schema location	Greenfield flag	Source
MTP (Multi-Token Prediction)	architecture.mtp	--allow-mtp --mtp-depths	DeepSeek-V3 §2.2
2:4 structured sparsity	sparsity_2_4 per component	(post-search; quality-model only)	NVIDIA H100/B200
RMSNorm	normalization.type = rmsnorm	default	Zhang & Sennrich 2019

Delta REGISTRY

Name	Effect	Legal --apply-args
swap_attention_to_gqa	n_kv_heads ← n_heads / group_size	group_size
swap_attention_to_mla	full → MLA at latent_dim	latent_dim
swap_attention_to_swa	full → sliding window	window_size
interleave_local_attention	local:global interleave	ratio, window
add_state_layers	replace fraction of attention with a state mixer	ratio, state_type
densify_first_k	first K MoE layers → dense	k
change_moe_topology	reshape an MoE block	n_experts, top_k
change_precision_per_component	weight, activation, and KV precision	weight, activation, kv
change_parallelism	swap TP / PP / EP / CP	tp, pp, ep, cp
scale_d_model	shift d_model, aligned to align	delta, align
scale_n_layers	shift n_layers	delta

given reference architectures

Llama-2-{7B, 13B, 70B}   Llama-3-{8B, 70B}   Mistral-7B   Gemma-2-9B
Qwen3-{8B, 32B}   DeepSeek-V3   Kimi-K2.5   GLM-5.1
GPT-OSS-120B   MAI-Base-1

---

Roadmap to boundaries

Future plans to our current boundaries with needs.

• Datamix dimension to quality spline Cross-corpus absolute-loss error is 10–22% with intervals too narrow to cover it (E2: 0/3 CI coverage — published, not hidden). Use AC ordinally, or fit per-datamix calibration packs (ac-auto-calibrate fit) as designed.
• Training-efficiency bucket runs ~10 pp low against reported frontier-lab MFU (E1 T2 anchors). Same fix: lab-local calibration.
• Optimizer-agnostic: assumes AdamW-family scaling; Muon-class optimizers shift the exponents and are not modeled.
• Hardware coverage: no TPU v6/v7, AMD MI300/MI355, Ascend 910B/C.
• EP granularity: EP must divide n_experts.
• Data recipes, tokenizers, and post-training effects are out of scope for the current stage (stated boundaries, not oversights).

---

Repository layout

.
├── README.md
├── pyproject.toml
├── ac/                              ← the Python package
│   ├── __init__.py
│   ├── cli_compile.py               greenfield + modifier point
│   ├── cli_delta_eval.py            delta influence entry point
│   ├── cli_stress.py                stress / quality / transition inspection
│   ├── cli_recipe.py                --recipe / --override / --print-recipe /
│   │                                --help-group / `config show` / `init`
│   ├── cli_matrix18b.py             budget-matched matrix + scenario Pareto
│   ├── cli_trust_audit.py           public-model anchors + trust audit
│   │
│   ├── lattice_engine.py            tile-aligned architecture lattice + KNOWN_ARCHITECTURES
│   ├── throughput_model.py          roofline throughput + MoE all-to-all + state-hybrid + MLA
│   ├── quality_model.py             modular scaling-law backbone + residual hooks
│   ├── auto_calibrate.py            local calibration pack fitter
│   ├── penalties.py                 quality-side penalty primitives
│   ├── sram_derivation.py           SRAM-derived state-block sizing
│   ├── schema.py                    schema 0.3 emit/validate
│   │
│   ├── optimizer.py                 candidate enumeration + Pareto search
│   ├── baseline.py                  base-config ingestion
│   ├── modifier.py                  baseline-aware local Pareto search
│   ├── baseline_delta.py            modifier report generation
│   ├── justification.py             prose justification + model card + assumptions
│   ├── shadow_prices.py             dual-variable interpretation
│   │
│   ├── stress.py                    10-axis StressVector
│   ├── quality_stress.py            7-axis QualityStressVector
│   ├── delta_engine.py              named transformation engine
│   ├── transition.py                pre/post stress diff
│   ├── rank.py                      transition ranking
│   ├── justify_transition.py        transition justifier
│   ├── optimizer_bridge.py          glue: CandidateArch ↔ ArchConfig
│   │
│   ├── evaluator.py                 capability-3 evaluator
│   ├── pareto_position.py           6-class Pareto verdict
│   ├── report.py                    delta-eval Markdown / JSON / CSV renderer
│   │
│   ├── deltas/                      10 named transformations
│   │   ├── base.py
│   │   ├── swap_attention_to_{gqa,mla,swa}.py
│   │   ├── add_state_layers.py
│   │   ├── densify_first_k.py
│   │   ├── change_moe_topology.py
│   │   ├── change_parallelism.py
│   │   ├── change_precision_per_component.py
│   │   ├── scale_{d_model,n_layers}.py
│   │   └── __init__.py              exports REGISTRY
│   │
│   ├── hardware_specs/              h100, b200, tpu_v5p, tpu_v5e, trainium2, trainium3
│   ├── calibration/                 h100, b200, tpu_v5p calibration jsons
│   └── quality_defaults.yaml        modular scaling-law constants
│
└── configs/                         reference base-model configs + shipped recipes
    ├── mistral_7b.json              dense + GQA
    ├── gpt_oss_120b.json            MoE 128 × top-4
    ├── mai_thinking_1.json          MoE + MLA + MTP + LongRoPE
    └── recipes/                     shipped --recipe bundles
        ├── h100_dense_7b.yaml
        ├── b200_moe_mla_long_ctx.yaml
        └── delta_mistral_gqa_long_ctx.yaml

---

License

Apache-2.0.