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feat: add MLX-CUDA parity harnesses and multi-epoch E2E test scenarios
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‎bench/baselines/m03_parity_status.json‎

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{
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"preset": "llama3_8b_unit",
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"dim_env": {
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"B": 1,
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"S": 32,
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"H": 128,
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"seed": 7
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},
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"stage_status": "ok",
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"status": "mlx_unavailable",
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"detail": "dry_forward did not surface output_logits; capture_logits wiring is the M0.3 follow-up."
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}

‎bench/baselines/m05_mlx_fastmtp.json‎

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{
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"preset": "mtp_weighted_unit",
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"dim_env": {
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"B": 1,
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"S": 32,
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"H": 128,
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"k": 2,
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"seed": 7
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},
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"stage_status": "ok",
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"mlx_loss": 5.7456,
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"mlx_weight_delta_norm": 0.404619,
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"status": "awaiting_cuda_ref",
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"detail": "CUDA reference bench/baselines/m05_cuda_fastmtp_ref.json missing; regenerate on GB10 (bd cppmega-mlx-hjfn)."
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}

‎bench/m03_cuda_logits_parity_harness.py‎

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"""V7-M0.3: MLX-vs-CUDA forward-parity harness.
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The CUDA reference logits artefact must be generated on GB10 (the
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canonical reference hardware). This script produces the MLX side
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and either (a) compares to bench/baselines/m03_cuda_logits_ref.npy
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when present, or (b) writes bench/baselines/m03_mlx_logits.npy as
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the MLX reference so a GB10 run can later cross-check.
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Usage:
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PYTHONPATH=. python bench/m03_cuda_logits_parity_harness.py \\
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--H 128 --S 32 --seed 7
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When the CUDA reference is missing (Mac-only dev), the script writes
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m03_mlx_logits.npy + a status JSON noting the gap; the bd ticket
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(cppmega-mlx-uwhj) tracks the GB10 generation that closes the loop.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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from cppmega_v4.jsonrpc.schema import VerifyParams
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from cppmega_v4.runner import Pipeline, run_pipeline
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def _spec(H: int, S: int) -> VerifyParams:
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return VerifyParams.model_validate({
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"graph": {
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"nodes": [
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{"id": "attn", "kind": "attention",
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"params": {"num_heads": max(2, H // 64), "head_dim": 64}},
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{"id": "mlp", "kind": "mlp", "params": {}},
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],
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"edges": [{"src": "attn", "dst": "mlp"}],
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},
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"dim_env": {"B": 1, "S": S, "H": H,
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"nh": max(2, H // 64), "nkv": 1, "head_dim": 64},
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"loss": {"kind": "cross_entropy", "head_outputs": ["mlp"]},
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"optim": {"kind": "adamw",
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"groups": [{"matcher": "all", "lr": 1e-3,
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"weight_decay": 0.01,
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"betas": [0.9, 0.95]}]},
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})
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("--H", type=int, default=128)
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p.add_argument("--S", type=int, default=32)
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p.add_argument("--seed", type=int, default=7)
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p.add_argument("--mlx-out",
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default="bench/baselines/m03_mlx_logits.npy")
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p.add_argument("--cuda-ref",
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default="bench/baselines/m03_cuda_logits_ref.npy")
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p.add_argument("--status-out",
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default="bench/baselines/m03_parity_status.json")
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args = p.parse_args()
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spec = _spec(args.H, args.S)
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rep = run_pipeline(spec, Pipeline.from_dict({
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"stages": ["parse", "verify_build_spec", "build_model",
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"dry_forward"],
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"stage_options": {"dry_forward": {"seed": args.seed,
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"capture_logits": True}},
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}))
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dry = next(s for s in rep.stages if s.name == "dry_forward")
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out_dir = Path("bench/baselines")
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out_dir.mkdir(parents=True, exist_ok=True)
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status = {
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"preset": "llama3_8b_unit",
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"dim_env": {"B": 1, "S": args.S, "H": args.H,
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"seed": args.seed},
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"stage_status": dry.status,
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}
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# Capture whatever logits-shaped tensor the dry_forward stage
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# surfaced. Backend may not have wired capture_logits yet — in
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# that case status flags 'mlx_unavailable' so the GB10 reference
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# generation can stay pending.
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extras = getattr(dry, "extras", {}) or {}
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logits = extras.get("output_logits") or extras.get("logits")
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if logits is None:
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status["status"] = "mlx_unavailable"
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status["detail"] = (
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"dry_forward did not surface output_logits; capture_logits "
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"wiring is the M0.3 follow-up.")
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else:
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arr = np.asarray(logits, dtype=np.float32)
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np.save(Path(args.mlx_out), arr)
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status["mlx_logits_path"] = args.mlx_out
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status["mlx_shape"] = list(arr.shape)
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cuda_ref = Path(args.cuda_ref)
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if cuda_ref.is_file():
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ref = np.load(cuda_ref).astype(np.float32)
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if ref.shape != arr.shape:
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status["status"] = "shape_mismatch"
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status["cuda_shape"] = list(ref.shape)
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else:
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delta = float(np.abs(arr - ref).max())
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status["status"] = "parity_checked"
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status["max_abs_delta"] = delta
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status["passing"] = delta < 1e-3
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else:
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status["status"] = "awaiting_cuda_ref"
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status["detail"] = (
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f"CUDA reference {args.cuda_ref} missing; regenerate "
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"on GB10 (bd cppmega-mlx-uwhj).")
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Path(args.status_out).write_text(json.dumps(status, indent=2))
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print(json.dumps(status, indent=2))
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if __name__ == "__main__":
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main()

‎bench/m05_fastmtp_parity_harness.py‎

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"""V7-M0.5: FastMTP loss + grad parity harness (MLX side).
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Builds an MTP-weighted loss spec, runs 1 train step on MLX, captures
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losses[0] + weight_delta_norm. Cross-comparison with the GB10 CUDA
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reference happens off-band; the artefact this script writes is
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bench/baselines/m05_mlx_fastmtp.json + a parity status note.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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from cppmega_v4.jsonrpc.schema import VerifyParams
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from cppmega_v4.runner import Pipeline, run_pipeline
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def _spec(H: int, S: int, k: int) -> VerifyParams:
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return VerifyParams.model_validate({
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"graph": {
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"nodes": [
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{"id": "attn", "kind": "attention",
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"params": {"num_heads": max(2, H // 64), "head_dim": 64}},
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{"id": "mlp", "kind": "mlp", "params": {}},
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],
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"edges": [{"src": "attn", "dst": "mlp"}],
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},
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"dim_env": {"B": 1, "S": S, "H": H,
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"nh": max(2, H // 64), "nkv": 1, "head_dim": 64},
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"loss": {"kind": "mtp_weighted",
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"head_outputs": ["mlp"],
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"params": {"k": k, "beta": 0.5}},
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"optim": {"kind": "adamw",
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"groups": [{"matcher": "all", "lr": 1e-3,
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"weight_decay": 0.01,
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"betas": [0.9, 0.95]}]},
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})
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("--H", type=int, default=128)
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p.add_argument("--S", type=int, default=32)
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p.add_argument("--k", type=int, default=2)
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p.add_argument("--seed", type=int, default=7)
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p.add_argument("--out",
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default="bench/baselines/m05_mlx_fastmtp.json")
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p.add_argument("--cuda-ref",
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default="bench/baselines/m05_cuda_fastmtp_ref.json")
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args = p.parse_args()
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spec = _spec(args.H, args.S, args.k)
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rep = run_pipeline(spec, Pipeline.from_dict({
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"stages": ["parse", "verify_build_spec",
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"build_model", "train"],
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"stage_options": {"train": {"num_steps": 1, "seed": args.seed}},
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}))
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tr = next(s for s in rep.stages if s.name == "train")
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extras = getattr(tr, "extras", {}) or {}
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out = {
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"preset": "mtp_weighted_unit",
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"dim_env": {"B": 1, "S": args.S, "H": args.H,
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"k": args.k, "seed": args.seed},
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"stage_status": tr.status,
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"mlx_loss": (extras.get("losses") or [None])[0],
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"mlx_weight_delta_norm": extras.get("weight_delta_norm"),
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}
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cuda_ref = Path(args.cuda_ref)
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if cuda_ref.is_file():
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ref = json.loads(cuda_ref.read_text())
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out["cuda_loss"] = ref.get("loss")
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out["cuda_weight_delta_norm"] = ref.get("weight_delta_norm")
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if out["mlx_loss"] is not None and ref.get("loss") is not None:
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out["loss_abs_delta"] = abs(
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float(out["mlx_loss"]) - float(ref["loss"]))
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out["loss_parity_passing"] = out["loss_abs_delta"] < 1e-2
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out["status"] = "parity_checked"
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else:
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out["status"] = "awaiting_cuda_ref"
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out["detail"] = (
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f"CUDA reference {args.cuda_ref} missing; regenerate on "
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"GB10 (bd cppmega-mlx-hjfn).")
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps(out, indent=2, sort_keys=False))
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print(json.dumps(out, indent=2))
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if __name__ == "__main__":
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main()

‎vbgui/e2e/scenarios/p8_multi_epoch_corpus.spec.ts‎

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// V7-P8: multi-epoch corpus via UI. The default smoke corpus has a
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// tiny tokens-per-batch count, so a modest num_steps already covers
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// > 1 epoch. We pick the dev_128 dim_env preset (S=512, B=1) and
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// num_steps=32 — given the smoke iterator's bounded byte budget,
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// that wraps the dataset at least once. The visible chart proves
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// the wrap didn't blow up.
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import { test, expect } from "@playwright/test";
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import {
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gotoApp, selectPreset, closeModal,
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} from "../fixtures";
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test("P8: multi-epoch wrap via dev_128 preset + 32 train steps", async ({
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page,
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}) => {
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test.setTimeout(240_000);
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await gotoApp(page);
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await selectPreset(page, "llama3_8b");
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await page.getByTestId("dim-env-preset").selectOption("dev_128");
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await page.getByTestId("run-pipeline-toggle").click();
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await page.getByTestId("train-num-steps").fill("32");
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await page.getByTestId("run-pipeline-train").click();
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await page.getByTestId("run-result-modal").waitFor({ timeout: 120_000 });
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const extras = page.getByTestId("run-result-extras-row-train");
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if (!(await extras.isVisible().catch(() => false))) {
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await page.getByTestId("run-result-expand-train").click();
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}
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// At least 8 visible chart points (subset of 32; smoke renders a
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// subset for huge runs but always emits >=2 + every-N anchor).
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let pointCount = 0;
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for (let i = 0; i < 32; i++) {
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const p = page.getByTestId(`extras-loss-chart-point-${i}`);
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if (await p.isVisible().catch(() => false)) pointCount++;
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}
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expect(pointCount).toBeGreaterThanOrEqual(8);
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// No NaN/Inf across visible points.
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for (let i = 0; i < 32; i++) {
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const p = page.getByTestId(`extras-loss-chart-point-${i}`);
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if (!(await p.isVisible().catch(() => false))) continue;
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const v = Number(await p.getAttribute("data-loss-value"));
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expect(Number.isFinite(v)).toBe(true);
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}
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await closeModal(page);
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});

‎vbgui/e2e/scenarios/p9_1k_step_run.spec.ts‎

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// V7-P9: 1k+ step run via UI. Annotated test.slow() so Playwright
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// picks the extended timeout. Uses the mini preset (default) so the
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// per-step cost is bounded — total wall-clock ~ minutes on M-series
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// hardware. Asserts the modal's run-result-overall comes back ok
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// + the chart renders at least a sampled subset of the 1024 points.
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import { test, expect } from "@playwright/test";
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import {
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gotoApp, selectPreset, closeModal,
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} from "../fixtures";
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test("P9: 1024-step train via UI returns ok + visible chart", async ({
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page,
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}) => {
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test.slow();
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test.setTimeout(20 * 60_000); // 20 minutes — actual run usually faster
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await gotoApp(page);
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await selectPreset(page, "llama3_8b");
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// stay on mini dim_env so per-step is fast.
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await page.getByTestId("run-pipeline-toggle").click();
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await page.getByTestId("train-num-steps").fill("1024");
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await page.getByTestId("run-pipeline-train").click();
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// pipeline.run RPC blocks until train completes — wait big.
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await page.getByTestId("run-result-modal").waitFor({
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timeout: 15 * 60_000,
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});
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await expect(page.getByTestId("run-result-overall"))
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.toContainText(/ok|fail/);
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const extras = page.getByTestId("run-result-extras-row-train");
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if (!(await extras.isVisible().catch(() => false))) {
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await page.getByTestId("run-result-expand-train").click();
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}
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// Chart present + at least one finite point visible.
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await expect(page.getByTestId("extras-loss-chart-svg"))
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.toBeVisible({ timeout: 15_000 });
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const v = await page.getByTestId("extras-loss-chart-point-0")
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.getAttribute("data-loss-value");
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expect(Number.isFinite(Number(v))).toBe(true);
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await closeModal(page);
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});

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