#!/usr/bin/env python3
"""
TRACE study workload driver.

Runs one labelled workload window on this node: distributed training, or one
of the deliberately similar non-training workloads used as negatives for the
likelihood-ratio calibration.

  - Dependencies: Python 3 standard library plus PyTorch. Nothing else.
  - Network: torch.distributed traffic between the study nodes listed in
    nodes.conf only (rendezvous on MASTER_ADDR:MASTER_PORT, then NCCL or
    Gloo). No other connections. Single-node workloads open no sockets.
  - Reads: nothing outside this bundle. Training data is generated
    synthetically on the GPU; no dataset is required on the node.
  - Writes: checkpoints and generated files under --scratch (rotated, capped),
    and one ground-truth JSON per run under --out/ground_truth/.

Invoked by orchestrator.py; can also be run by hand for testing:
  python3 workloads.py --kind burn --duration 30 --out ./out --scratch ./scratch
"""
import argparse
import hashlib
import json
import math
import os
import random
import shutil
import signal
import socket
import struct
import sys
import time
from datetime import datetime, timezone, timedelta

import torch
import torch.nn as nn
import torch.nn.functional as F

STOP = False

# workload kinds that use torch.distributed across nodes
DIST_KINDS = {"train", "grad_eval", "fabric_bench", "tp_infer", "hpc"}


def log(msg):
    sys.stderr.write(f"{datetime.now(timezone.utc).isoformat()} workload {msg}\n")
    sys.stderr.flush()


def on_term(sig, frm):
    global STOP
    STOP = True


class Deadline:
    def __init__(self, seconds):
        self.t_end = time.time() + seconds

    def expired(self):
        return STOP or time.time() >= self.t_end

    def remaining(self):
        return max(0.0, self.t_end - time.time())


# ---------------------------------------------------------------- distributed
def dist_env():
    return int(os.environ.get("WORLD_SIZE", "1")), int(os.environ.get("RANK", "0"))


def init_dist():
    world, rank = dist_env()
    if world <= 1:
        return world, rank
    import torch.distributed as dist

    backend = "nccl" if torch.cuda.is_available() else "gloo"
    dist.init_process_group(
        backend=backend, world_size=world, rank=rank,
        timeout=timedelta(seconds=1800),
    )
    log(f"dist ready: backend={backend} rank={rank} world={world}")
    return world, rank


def cleanup_dist():
    import torch.distributed as dist

    if dist.is_available() and dist.is_initialized():
        dist.destroy_process_group()


def pick_device():
    if torch.cuda.is_available():
        return torch.device("cuda:0")
    return torch.device("cpu")


def autocast_ctx(device):
    if device.type == "cuda" and torch.cuda.is_bf16_supported():
        return torch.autocast("cuda", dtype=torch.bfloat16)
    import contextlib

    return contextlib.nullcontext()


def sync(device):
    if device.type == "cuda":
        torch.cuda.synchronize()


def coll_flags(device, world, stop, gap=False):
    """Agree on loop decisions across ranks so no rank ever exits a loop
    while a peer is still waiting in a collective. MAX-reduce of two flags:
    stop fires if ANY rank wants to stop; gap carries rank 0's decision."""
    if world <= 1:
        return stop, gap
    import torch.distributed as dist

    t = torch.tensor(
        [1.0 if stop else 0.0, 1.0 if gap else 0.0],
        device=device if device.type == "cuda" else "cpu",
    )
    dist.all_reduce(t, op=dist.ReduceOp.MAX)
    return bool(t[0] > 0), bool(t[1] > 0)


# ---------------------------------------------------------------- model
class Attention(nn.Module):
    def __init__(self, dim, heads):
        super().__init__()
        self.heads = heads
        self.qkv = nn.Linear(dim, 3 * dim, bias=False)
        self.proj = nn.Linear(dim, dim, bias=False)

    def forward(self, x):
        b, t, d = x.shape
        q, k, v = self.qkv(x).chunk(3, dim=-1)
        q = q.view(b, t, self.heads, -1).transpose(1, 2)
        k = k.view(b, t, self.heads, -1).transpose(1, 2)
        v = v.view(b, t, self.heads, -1).transpose(1, 2)
        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
        y = y.transpose(1, 2).reshape(b, t, d)
        return self.proj(y)


class Block(nn.Module):
    def __init__(self, dim, heads):
        super().__init__()
        self.ln1 = nn.LayerNorm(dim)
        self.attn = Attention(dim, heads)
        self.ln2 = nn.LayerNorm(dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, 4 * dim, bias=False),
            nn.GELU(),
            nn.Linear(4 * dim, dim, bias=False),
        )

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.mlp(self.ln2(x))
        return x


class GPT(nn.Module):
    def __init__(self, vocab, dim, layers, heads, seq):
        super().__init__()
        self.seq = seq
        self.tok = nn.Embedding(vocab, dim)
        self.pos = nn.Embedding(seq, dim)
        self.blocks = nn.ModuleList(Block(dim, heads) for _ in range(layers))
        self.ln_f = nn.LayerNorm(dim)
        self.head = nn.Linear(dim, vocab, bias=False)

    def forward(self, idx, targets=None):
        b, t = idx.shape
        pos = torch.arange(t, device=idx.device)
        x = self.tok(idx) + self.pos(pos)
        for blk in self.blocks:
            x = blk(x)
        logits = self.head(self.ln_f(x))
        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)), targets.reshape(-1)
            )
        return logits, loss


def build_model(p, device):
    torch.manual_seed(p["seed"])
    m = GPT(p["vocab"], p["dim"], p["layers"], p["heads"], p["seq"]).to(device)
    n = sum(q.numel() for q in m.parameters())
    log(f"model built: {n/1e6:.1f}M params")
    return m, n


def synth_batch(p, batch, device):
    # synthetic token stream; the study measures compute/communication
    # structure, not model quality, so random tokens are sufficient
    x = torch.randint(0, p["vocab"], (batch, p["seq"]), device=device)
    y = torch.roll(x, -1, dims=1)
    return x, y


# ---------------------------------------------------------------- checkpoint
class Checkpointer:
    """Writes checkpoints under scratch, keeps the newest `keep`, logs sizes.

    mode "normal":       one torch.save file, the standard pattern.
    mode "small_writes": the E1 evasion — the same state split into many
                         small files with a generic directory name.
    """

    def __init__(self, scratch, run_id, mode, keep, rank):
        self.dir = os.path.join(scratch, f"ckpt_{run_id}_rank{rank}")
        os.makedirs(self.dir, exist_ok=True)
        self.mode = mode
        self.keep = keep
        self.events = []

    def save(self, model, opt, step):
        t0 = time.time()
        state = {
            "step": step,
            "model": model.state_dict(),
            "opt": opt.state_dict() if opt is not None else None,
        }
        if self.mode == "small_writes":
            d = os.path.join(self.dir, f"data_export_{step:08d}")
            os.makedirs(d, exist_ok=True)
            i, buf, bufsz = 0, {}, 0
            for k, v in state["model"].items():
                buf[k] = v
                bufsz += v.numel() * v.element_size()
                if bufsz >= 8 * 2**20:
                    self._save_part(d, i, buf)
                    i, buf, bufsz = i + 1, {}, 0
            if buf:
                self._save_part(d, i, buf)
            path = d
        else:
            path = os.path.join(self.dir, f"step_{step:08d}.pt")
            with open(path, "wb") as f:
                torch.save(state, f)
                f.flush()
                os.fsync(f.fileno())
        nbytes = self._du(path)
        self._rotate()
        ev = {"t": time.time(), "step": step, "bytes": nbytes,
              "secs": round(time.time() - t0, 3)}
        self.events.append(ev)
        log(f"checkpoint step={step} {nbytes/2**20:.0f} MiB in {ev['secs']}s")
        return path

    def latest(self):
        entries = sorted(os.listdir(self.dir))
        return os.path.join(self.dir, entries[-1]) if entries else None

    def _save_part(self, d, i, buf):
        p = os.path.join(d, f"part_{i:05d}.bin")
        with open(p, "wb") as f:
            torch.save(buf, f)
            f.flush()
            os.fsync(f.fileno())

    def _du(self, path):
        if os.path.isfile(path):
            return os.path.getsize(path)
        return sum(
            os.path.getsize(os.path.join(r, f))
            for r, _, fs in os.walk(path) for f in fs
        )

    def _rotate(self):
        entries = sorted(os.listdir(self.dir))
        for e in entries[: max(0, len(entries) - self.keep)]:
            p = os.path.join(self.dir, e)
            shutil.rmtree(p) if os.path.isdir(p) else os.remove(p)


# ---------------------------------------------------------------- train
TRAIN_DEFAULTS = {
    "seed": 1234, "vocab": 32768, "dim": 1024, "layers": 24, "heads": 16,
    "seq": 1024, "micro_batch": 8, "accum": 8, "lr": 3e-4,
    "strategy": "ddp",           # ddp | fsdp | paced
    "ckpt_interval_s": 900, "ckpt_mode": "normal", "ckpt_keep": 2,
    "ckpt_include_optimizer": True,
    "preallocate_optimizer_state": False,
    "update_weights": True,
    "fragment_run_s": 0, "fragment_gap_s": 0,   # E3: run/gap chunking
    "pace_gap_ms": 40, "pace_chunk_mb": 24,     # E2: paced fabric traffic
}

GRAD_EVAL_DEFAULTS = dict(
    TRAIN_DEFAULTS,
    strategy="ddp",
    update_weights=False,
    ckpt_include_optimizer=False,
)


def parameter_probe(model, max_values=4096):
    """Hash a fixed, non-reversible sample of parameters for ground truth.

    The positional embedding is preferred because every training step touches
    it. The probe establishes whether this controlled fixture changed weights;
    it is direct runner ground truth, not an infrastructure observable.
    """
    named = list(model.named_parameters())
    chosen = next(
        ((name, value) for name, value in named if name.endswith("pos.weight")),
        named[0] if named else (None, None),
    )
    name, value = chosen
    if value is None:
        return None
    sample = value.detach().reshape(-1)[:max_values].float().cpu().tolist()
    digest = hashlib.sha256()
    for item in sample:
        digest.update(struct.pack("<f", float(item)))
    return {"parameter": name, "sample_values": len(sample),
            "sha256": digest.hexdigest()}


def flat_chunks(params, chunk_mb):
    """Group parameters into roughly chunk_mb-sized lists for allreduce."""
    groups, cur, sz = [], [], 0
    limit = chunk_mb * 2**20
    for p in params:
        cur.append(p)
        sz += p.numel() * p.element_size()
        if sz >= limit:
            groups.append(cur)
            cur, sz = [], 0
    if cur:
        groups.append(cur)
    return groups


def preallocate_adam_state(opt):
    """Materialize Adam state without changing a parameter.

    AdamW normally allocates its two moment tensors on the first optimizer
    step. The paired identifiability experiment uses this on both arms so the
    update and no-update workloads have the same steady-state GPU allocation.
    """
    saved = []
    for group in opt.param_groups:
        saved.append((group, group["lr"], group.get("weight_decay", 0.0)))
        group["lr"] = 0.0
        group["weight_decay"] = 0.0
        for parameter in group["params"]:
            parameter.grad = torch.zeros_like(parameter)
    opt.step()
    for state in opt.state.values():
        step = state.get("step")
        if torch.is_tensor(step):
            step.zero_()
        elif step is not None:
            state["step"] = 0
    for group, lr, weight_decay in saved:
        group["lr"] = lr
        group["weight_decay"] = weight_decay
    opt.zero_grad(set_to_none=True)


def run_train(args, p, dl):
    import torch.distributed as dist

    world, rank = init_dist()
    device = pick_device()
    model, nparams = build_model(p, device)
    strategy = p["strategy"]
    update_weights = bool(p["update_weights"])
    probe_before = parameter_probe(model) if strategy != "fsdp" else None
    # The model seed is shared so every DDP rank starts from identical
    # weights; the data seed is rank-specific, as in real data parallelism.
    data_seed = int(p["seed"]) + 100003 * rank
    torch.manual_seed(data_seed)

    if strategy == "fsdp" and world > 1 and device.type == "cuda":
        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
        from torch.distributed.fsdp.wrap import ModuleWrapPolicy

        model = FSDP(model, auto_wrap_policy=ModuleWrapPolicy({Block}))
    elif strategy == "fsdp":
        log("fsdp needs cuda+multi-rank; falling back to ddp")
        strategy = "ddp"
    if strategy == "ddp" and world > 1:
        model = nn.parallel.DistributedDataParallel(
            model, device_ids=[0] if device.type == "cuda" else None
        )

    opt = torch.optim.AdamW(model.parameters(), lr=p["lr"])
    if p["preallocate_optimizer_state"]:
        preallocate_adam_state(opt)
        log("preallocated AdamW state without changing parameters")
    ck = Checkpointer(args.scratch, args.run_id, p["ckpt_mode"],
                      p["ckpt_keep"], rank)
    write_ckpt = (strategy == "fsdp") or rank == 0  # fsdp saves shards per rank
    # save the unwrapped module so a resume can load with matching keys
    ckpt_model = model.module if (strategy == "ddp" and world > 1) else model
    ckpt_opt = opt if p["ckpt_include_optimizer"] else None
    acc = None
    if strategy == "paced":
        chunk_groups = flat_chunks(list(model.parameters()), p["pace_chunk_mb"])

    def microbatch(sync_grads):
        x, y = synth_batch(p, p["micro_batch"], device)
        ctx = None
        if strategy == "ddp" and world > 1 and not sync_grads:
            ctx = model.no_sync()
            ctx.__enter__()
        with autocast_ctx(device):
            _, loss = model(x, y)
        (loss / p["accum"]).backward()
        if ctx is not None:
            ctx.__exit__(None, None, None)
        return loss

    def paced_reduce():
        # E2: spread gradient traffic into jittered, chunked allreduces so the
        # fabric shows no clean step-boundary burst
        for grp in chunk_groups:
            flat = torch.cat([q.grad.reshape(-1) for q in grp])
            dist.all_reduce(flat)
            flat /= world
            off = 0
            for q in grp:
                q.grad.copy_(flat[off:off + q.numel()].view_as(q.grad))
                off += q.numel()
            time.sleep(random.expovariate(1000.0 / max(1, p["pace_gap_ms"])))

    # comms fraction probe: a few compute-only steps vs full steps
    comm_frac = None
    if strategy == "ddp" and world > 1:
        sync(device); t0 = time.time()
        for _ in range(2):
            for _ in range(p["accum"]):
                microbatch(sync_grads=False)
            opt.zero_grad(set_to_none=True)
        sync(device); t_compute = (time.time() - t0) / 2

    steps, optimizer_steps, t_step_sum, last_ckpt = 0, 0, 0.0, time.time()
    step_events = []
    losses = []
    frag_next_gap = (time.time() + p["fragment_run_s"]
                     if p["fragment_run_s"] > 0 else None)
    loop_start = time.time()
    while True:
        # loop decisions must be identical on every rank (see coll_flags)
        gap_due = (rank == 0 and frag_next_gap is not None
                   and time.time() >= frag_next_gap)
        stop, gap = coll_flags(device, world, dl.expired(), gap_due)
        if stop:
            break
        if gap:
            # E3: pause, then resume from checkpoint, as a fragmented run would
            if write_ckpt:
                ck.save(ckpt_model, ckpt_opt, steps)
            if world > 1:
                dist.barrier()
            log(f"fragment gap {p['fragment_gap_s']}s")
            time.sleep(min(p["fragment_gap_s"], dl.remaining()))
            latest = ck.latest()
            if latest and os.path.isfile(latest):
                state = torch.load(latest, map_location=device,
                                   weights_only=False)
                ckpt_model.load_state_dict(state["model"])
                if state.get("opt") is not None:
                    opt.load_state_dict(state["opt"])
                log("resumed from checkpoint")
            frag_next_gap = time.time() + p["fragment_run_s"]
            if world > 1:
                dist.barrier()
        t0 = time.time()
        for i in range(p["accum"]):
            last = i == p["accum"] - 1
            loss = microbatch(sync_grads=last)
            if strategy == "paced" and world > 1:
                # re-reducing the accumulated grad each microbatch is
                # mathematically a no-op on already-averaged terms, so the
                # result stays correct while the traffic loses its step burst
                paced_reduce()
        if update_weights:
            opt.step()
            optimizer_steps += 1
        opt.zero_grad(set_to_none=True)
        sync(device)
        steps += 1
        step_duration = time.time() - t0
        t_step_sum += step_duration
        step_events.append([
            round(time.time(), 6), round(step_duration, 6)
        ])
        losses.append(float(loss.detach()))
        if steps % 10 == 0:
            log(f"step {steps} loss {losses[-1]:.3f} "
                f"({t_step_sum/steps:.2f}s/step)")
        if (p["ckpt_mode"] != "off" and write_ckpt
                and time.time() - last_ckpt >= p["ckpt_interval_s"]):
            ck.save(ckpt_model, ckpt_opt, steps)
            last_ckpt = time.time()
    loop_end = time.time()
    if strategy == "ddp" and world > 1 and steps > 0:
        t_full = t_step_sum / steps
        comm_frac = max(0.0, round(1.0 - t_compute / t_full, 3))

    tokens = steps * world * p["accum"] * p["micro_batch"] * p["seq"]
    equivalent_6nd = 6.0 * nparams * tokens
    probe_model = model.module if (strategy == "ddp" and world > 1) else model
    probe_after = parameter_probe(probe_model) if strategy != "fsdp" else None
    weights_changed = (
        probe_before is not None and probe_after is not None
        and probe_before["sha256"] != probe_after["sha256"]
    )
    return {
        "param_count": nparams, "steps": steps, "tokens_global": tokens,
        "model_seed": int(p["seed"]), "data_seed": data_seed,
        "loop_start_epoch_s": loop_start,
        "loop_end_epoch_s": loop_end,
        "step_events_end_epoch_s_duration_s": step_events,
        "ddp_gradient_syncs": (
            steps if strategy == "ddp" and world > 1 else None
        ),
        "optimizer_steps": optimizer_steps,
        "purpose_ground_truth": (
            "parameter_update" if update_weights else "gradient_evaluation_no_update"
        ),
        "flop_6nd_estimate": equivalent_6nd if update_weights else None,
        "work_6nd_equivalent": equivalent_6nd,
        "step_time_s_mean": round(t_step_sum / max(1, steps), 3),
        "comm_fraction_est": comm_frac,
        "checkpoint_includes_optimizer": bool(p["ckpt_include_optimizer"]),
        "optimizer_state_preallocated": bool(p["preallocate_optimizer_state"]),
        "parameter_probe_before": probe_before,
        "parameter_probe_after": probe_after,
        "parameter_probe_changed": weights_changed,
        "loss_first": losses[0] if losses else None,
        "loss_last": losses[-1] if losses else None,
        "checkpoints": ck.events,
    }


# ---------------------------------------------------------------- negatives
FB_DEFAULTS = {"sizes_mb": [1, 4, 16, 64, 256], "iters_per_size": 20}


def run_fabric_bench(args, p, dl):
    """N1: nccl-tests-style collective sweep. Regular, training-free fabric."""
    import torch.distributed as dist

    world, rank = init_dist()
    device = pick_device()
    if world <= 1:
        log("fabric_bench needs >=2 ranks; nothing to benchmark on one node")
        while not dl.expired():
            time.sleep(min(5, dl.remaining()))
        return {"skipped": "needs >=2 ranks"}
    stats = {"rounds": 0, "bytes_allreduce": 0, "bytes_allgather": 0}
    stop = False
    while not stop:
        for mb in p["sizes_mb"]:
            n = mb * 2**20 // 4
            t = torch.ones(n, device=device)
            for _ in range(p["iters_per_size"]):
                dist.all_reduce(t)
                stats["bytes_allreduce"] += n * 4
            gather = [torch.empty_like(t) for _ in range(world)]
            for _ in range(max(1, p["iters_per_size"] // 4)):
                dist.all_gather(gather, t)
                stats["bytes_allgather"] += n * 4 * world
            sync(device)
            stop, _ = coll_flags(device, world, dl.expired())
            if stop:
                break
        stats["rounds"] += 1
    return stats


TP_DEFAULTS = {
    "seed": 1234, "vocab": 32768, "dim": 1024, "layers": 24, "heads": 16,
    "seq": 512, "rate_hz": 4.0, "max_batch": 16,
}


class TPMlp(nn.Module):
    """Megatron-style MLP shard: column-parallel then row-parallel + allreduce."""

    def __init__(self, dim, world):
        super().__init__()
        self.fc1 = nn.Linear(dim, 4 * dim // world, bias=False)
        self.fc2 = nn.Linear(4 * dim // world, dim, bias=False)

    def forward(self, x):
        import torch.distributed as dist

        y = self.fc2(F.gelu(self.fc1(x)))
        if dist.is_initialized() and dist.get_world_size() > 1:
            dist.all_reduce(y)
        return y


def run_tp_infer(args, p, dl):
    """N2: tensor-parallel inference. Heavy per-layer collectives, no updates."""
    world, rank = init_dist()
    device = pick_device()
    model, _ = build_model(p, device)
    for blk in model.blocks:
        blk.mlp = TPMlp(p["dim"], max(1, world)).to(device)
    model.eval()
    # every rank must draw the same batch size or the allreduce shapes differ,
    # so the request stream comes from a shared-seed rng (a TP group serves
    # the same request on all ranks anyway)
    rng = random.Random(p["seed"])
    stats = {"requests": 0, "tokens": 0}
    with torch.no_grad():
        while True:
            stop, _ = coll_flags(device, world, dl.expired())
            if stop:
                break
            batch = rng.randint(1, p["max_batch"])
            x, _ = synth_batch(p, batch, device)
            with autocast_ctx(device):
                model(x)
            sync(device)
            stats["requests"] += batch
            stats["tokens"] += batch * p["seq"]
            time.sleep(rng.expovariate(p["rate_hz"]))
    return stats


HPC_DEFAULTS = {"grid": 8192, "halo_every": 1, "residual_every": 200}


def run_hpc(args, p, dl):
    """N5: Jacobi stencil with halo exchange, the classic HPC/MPI pattern."""
    import torch.distributed as dist

    world, rank = init_dist()
    device = pick_device()
    n = p["grid"]
    rows = max(4, n // max(1, world))
    grid = torch.rand(rows + 2, n, device=device)
    stats = {"iters": 0, "halo_exchanges": 0, "residual_allreduces": 0}
    stop = False
    while not stop:
        # a fixed block of iterations between collective stop checks keeps
        # every rank's collective sequence identical
        for _ in range(p["residual_every"]):
            if world > 1:
                reqs = []
                if rank > 0:
                    reqs.append(dist.isend(grid[1].contiguous(), rank - 1))
                    reqs.append(dist.irecv(grid[0], rank - 1))
                if rank < world - 1:
                    reqs.append(dist.isend(grid[rows].contiguous(), rank + 1))
                    reqs.append(dist.irecv(grid[rows + 1], rank + 1))
                for r in reqs:
                    r.wait()
                stats["halo_exchanges"] += 1
            inner = grid[1:rows + 1]
            new = 0.25 * (
                grid[0:rows] + grid[2:rows + 2]
                + torch.roll(inner, 1, dims=1) + torch.roll(inner, -1, dims=1)
            )
            grid[1:rows + 1] = new
            stats["iters"] += 1
        res = (new - inner).abs().sum()
        if world > 1:
            dist.all_reduce(res)
            stats["residual_allreduces"] += 1
        sync(device)
        stop, _ = coll_flags(device, world, dl.expired())
    return stats


INFER_DEFAULTS = {
    "seed": 1234, "vocab": 32768, "dim": 1024, "layers": 24, "heads": 16,
    "seq": 1024, "batch": 32,
}


def run_batch_infer(args, p, dl):
    """N3: continuous large-batch inference. High activity, no fabric."""
    device = pick_device()
    model, _ = build_model(p, device)
    model.eval()
    stats = {"batches": 0, "tokens": 0}
    with torch.no_grad():
        while not dl.expired():
            x, _ = synth_batch(p, p["batch"], device)
            with autocast_ctx(device):
                model(x)
            sync(device)
            stats["batches"] += 1
            stats["tokens"] += p["batch"] * p["seq"]
    return stats


SERVE_DEFAULTS = {
    "seed": 1234, "vocab": 32768, "dim": 1024, "layers": 24, "heads": 16,
    "seq": 256, "base_rate_hz": 4.0, "rate_swing": 0.6, "max_batch": 4,
}


def run_serving(args, p, dl):
    """S1: request-driven serving. Poisson arrivals, slowly varying rate."""
    device = pick_device()
    model, _ = build_model(p, device)
    model.eval()
    t_start = time.time()
    total = dl.remaining() + 1
    stats = {"requests": 0, "tokens": 0}
    with torch.no_grad():
        while not dl.expired():
            phase = 2 * math.pi * (time.time() - t_start) / total
            rate = p["base_rate_hz"] * (1 + p["rate_swing"] * math.sin(phase))
            time.sleep(random.expovariate(max(0.2, rate)))
            batch = random.randint(1, p["max_batch"])
            seq = random.randint(p["seq"] // 4, p["seq"])
            x = torch.randint(0, p["vocab"], (batch, seq), device=device)
            with autocast_ctx(device):
                model(x)
            sync(device)
            stats["requests"] += batch
            stats["tokens"] += batch * seq
    return stats


GEN_DEFAULTS = {
    "seed": 1234, "vocab": 32768, "dim": 1024, "layers": 24, "heads": 16,
    "seq": 512, "batch": 32, "gen_tokens": 128, "cap_gb": 20,
}


def run_datagen(args, p, dl):
    """N6: synthetic-data generation. High activity plus steady writes."""
    device = pick_device()
    model, _ = build_model(p, device)
    model.eval()
    outdir = os.path.join(args.scratch, f"gen_{args.run_id}")
    os.makedirs(outdir, exist_ok=True)
    stats = {"tokens_generated": 0, "bytes_written": 0, "files": 0}
    fileno = 0
    with torch.no_grad():
        while not dl.expired():
            x = torch.randint(0, p["vocab"], (p["batch"], 8), device=device)
            for _ in range(p["gen_tokens"]):
                with autocast_ctx(device):
                    logits, _ = model(x[:, -p["seq"]:])
                nxt = torch.multinomial(
                    F.softmax(logits[:, -1].float(), dim=-1), 1
                )
                x = torch.cat([x, nxt], dim=1)
                if dl.expired():
                    break
            path = os.path.join(outdir, f"gen_{fileno:06d}.pt")
            with open(path, "wb") as f:
                torch.save(x.to(torch.int16).cpu(), f)
                f.flush()
                os.fsync(f.fileno())
            fileno += 1
            stats["tokens_generated"] += x.numel()
            stats["bytes_written"] += os.path.getsize(path)
            stats["files"] += 1
            files = sorted(os.listdir(outdir))
            while sum(os.path.getsize(os.path.join(outdir, q)) for q in files) \
                    > p["cap_gb"] * 2**30:
                os.remove(os.path.join(outdir, files.pop(0)))
    return stats


IO_DEFAULTS = {
    "burst_gb": 4, "files_per_burst": 8, "interval_s": 300,
    "initial_delay_s": 0, "cap_gb": 40,
}


def run_io_burst(args, p, dl):
    """N7: checkpoint-shaped write bursts with idle GPUs."""
    outdir = os.path.join(args.scratch, f"io_{args.run_id}")
    os.makedirs(outdir, exist_ok=True)
    stats = {"bursts": 0, "bytes_written": 0}
    burst = 0
    block = os.urandom(4 * 2**20)
    if p["initial_delay_s"] > 0:
        time.sleep(min(p["initial_delay_s"], dl.remaining()))
    while not dl.expired():
        per_file = int(p["burst_gb"] * 2**30 / p["files_per_burst"])
        for i in range(p["files_per_burst"]):
            path = os.path.join(outdir, f"burst_{burst:04d}_{i:02d}.bin")
            written = 0
            with open(path, "wb") as f:
                while written < per_file:
                    f.write(block)
                    written += len(block)
                f.flush()
                os.fsync(f.fileno())
            stats["bytes_written"] += written
            if dl.expired():
                break
        stats["bursts"] += 1
        burst += 1
        dirs = sorted(set(q.split("_")[1] for q in os.listdir(outdir)))
        while len(dirs) * p["burst_gb"] > p["cap_gb"]:
            old = dirs.pop(0)
            for q in list(os.listdir(outdir)):
                if q.startswith(f"burst_{old}_"):
                    os.remove(os.path.join(outdir, q))
        time.sleep(min(p["interval_s"], dl.remaining()))
    return stats


BURN_DEFAULTS = {"size": 8192}


def run_burn(args, p, dl):
    """N8: gpu-burn equivalent. Max activity, no fabric, no writes."""
    device = pick_device()
    n = p["size"] if device.type == "cuda" else 512
    a = torch.randn(n, n, device=device)
    b = torch.randn(n, n, device=device)
    stats = {"matmuls": 0, "flop_estimate": 0.0}
    while not dl.expired():
        with autocast_ctx(device):
            c = a @ b
        sync(device)
        a.copy_(c / (c.norm() + 1e-6) * n)  # keep values bounded, reuse output
        stats["matmuls"] += 1
        stats["flop_estimate"] += 2.0 * n ** 3
    return stats


def run_idle(args, p, dl):
    """N4: allocated and idle. Baselines the site's own background chatter."""
    while not dl.expired():
        time.sleep(min(5, dl.remaining()))
    return {"idled": True}


KINDS = {
    "train": (run_train, TRAIN_DEFAULTS),
    "grad_eval": (run_train, GRAD_EVAL_DEFAULTS),
    "fabric_bench": (run_fabric_bench, FB_DEFAULTS),
    "tp_infer": (run_tp_infer, TP_DEFAULTS),
    "hpc": (run_hpc, HPC_DEFAULTS),
    "batch_infer": (run_batch_infer, INFER_DEFAULTS),
    "serving": (run_serving, SERVE_DEFAULTS),
    "datagen": (run_datagen, GEN_DEFAULTS),
    "io_burst": (run_io_burst, IO_DEFAULTS),
    "burn": (run_burn, BURN_DEFAULTS),
    "idle": (run_idle, {}),
}


def main():
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--kind", required=True, choices=sorted(KINDS))
    ap.add_argument("--duration", type=float, required=True)
    ap.add_argument("--out", required=True, help="ground-truth output dir")
    ap.add_argument("--scratch", required=True, help="checkpoint/data scratch")
    ap.add_argument("--run-id", default="manual")
    ap.add_argument("--params", default="{}", help="JSON overrides")
    args = ap.parse_args()

    signal.signal(signal.SIGTERM, on_term)
    fn, defaults = KINDS[args.kind]
    p = dict(defaults)
    p.update(json.loads(args.params))
    os.makedirs(args.scratch, exist_ok=True)
    gt_dir = os.path.join(args.out, "ground_truth")
    os.makedirs(gt_dir, exist_ok=True)

    world, rank = dist_env()
    log(f"start kind={args.kind} run={args.run_id} rank={rank}/{world} "
        f"duration={args.duration:.0f}s")
    t0 = time.time()
    err = None
    try:
        stats = fn(args, p, Deadline(args.duration))
    except Exception as e:
        err, stats = f"{type(e).__name__}: {e}", {}
        log(f"ERROR {err}")
    finally:
        try:
            cleanup_dist()
        except Exception:
            pass

    record = {
        "run_id": args.run_id, "kind": args.kind, "params": p,
        "rank": rank, "world": world, "node": socket.gethostname(),
        "t_start_utc": datetime.fromtimestamp(t0, timezone.utc).isoformat(),
        "t_end_utc": datetime.now(timezone.utc).isoformat(),
        "wall_s": round(time.time() - t0, 1),
        "device": (torch.cuda.get_device_name(0)
                   if torch.cuda.is_available() else "cpu"),
        "torch": torch.__version__,
        "error": err, "stats": stats,
    }
    path = os.path.join(gt_dir, f"{args.run_id}_rank{rank}.json")
    with open(path, "w") as f:
        json.dump(record, f, indent=2)
    log(f"done kind={args.kind}; ground truth -> {path}")
    sys.exit(1 if err else 0)


if __name__ == "__main__":
    main()