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e1acc15 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | """Solo synthetic qualification of fused Markov/q/graph semantics (no weights)."""
from __future__ import annotations
import argparse
import fcntl
import hashlib
import json
import os
import resource
import time
from collections.abc import Callable
from pathlib import Path
from types import SimpleNamespace
from typing import Any, cast
from unittest.mock import patch
import torch
from ...worker import sampled_fused
from ...worker.sampled import MASS, Top32
from ...worker.sampled_fused import FusedContext
from ...worker.tp2 import MarkovShard
UUID = "GPU-bc62df2a-59cf-9828-7d43-558d1355bab7"
def digest(path: Path) -> str:
return hashlib.sha256(path.read_bytes()).hexdigest()
def model_fixture() -> Any:
dev = torch.device("cuda:0")
vocab, rank_dim = 129280, 256
w1 = (torch.randn(vocab, rank_dim, device=dev) / 8).bfloat16()
slots = torch.full((vocab,), -1, dtype=torch.int64, device=dev)
slots[:4] = torch.arange(4, device=dev)
ranks: list[Any] = []
for rank, (lo, hi) in enumerate(((0, 64768), (64768, vocab))):
mb = MarkovShard.__new__(MarkovShard)
mb.w1 = w1
mb.w2 = (torch.randn(hi - lo, rank_dim, device=dev) / 8).bfloat16()
mb.slot_of_token = slots
mb.cache = (torch.randn(4, hi - lo, device=dev) / 4).bfloat16()
ranks.append(SimpleNamespace(r=rank, v0=lo, v1=hi, markov=mb, dev=dev))
return SimpleNamespace(ALL=(0, 1), devs=[dev, dev], ranks=ranks, emulated=True)
def qualify(model: Any, batch: int, depth: int, mask: torch.Tensor) -> dict[str, Any]:
dev = model.devs[0]
ctx = FusedContext(model, batch, depth)
keys = torch.tensor([[4104 + j, 1, 32768] for j in range(batch)], device=dev, dtype=torch.int64)
ctx.update(keys, mask)
logits = {r: torch.randn(batch, depth, rk.v1 - rk.v0, device=dev).bfloat16()
for r, rk in enumerate(model.ranks)}
output = {r: torch.zeros(batch, depth, device=dev, dtype=torch.int64) for r in model.ALL}
sampler = Top32(batch, 129280, dev)
checks = 0
# Cached, uncached and mixed rows; sequential positions use
# the actually selected previous token, exercising conditional Markov q.
for initial in (0, 7):
anchor = torch.tensor([initial if j % 2 == 0 else 2 for j in range(batch)], device=dev)
for pos in range(depth):
previous = {r: anchor if pos == 0 else output[r][:, pos - 1] for r in model.ALL}
ctx.select(model.ALL, logits, previous, output, pos)
support, q = ctx.result()
parts = []
for r in model.ALL:
mb = model.ranks[r].markov
prev = previous[r].contiguous()
parts.append((logits[r][:, pos] + mb.bias(prev, mb.embed(prev))).bfloat16())
full = torch.cat(parts, -1)
poskeys = keys.clone()
poskeys[:, 2] += pos
token, ref_ids, ref_q = sampler(full, poskeys, torch.full((batch,), .7, device=dev))
expected = torch.where(mask, token.long(), full.argmax(-1))
torch.testing.assert_close(output[0][:, pos], expected, rtol=0, atol=0)
torch.testing.assert_close(output[1][:, pos], expected, rtol=0, atol=0)
torch.testing.assert_close(support[:, pos], ref_ids, rtol=0, atol=0)
torch.testing.assert_close(q[:, pos], ref_q * mask[:, None], rtol=0, atol=0)
if not torch.equal((q[:, pos].double() * MASS).sum(-1), mask.double() * MASS):
raise AssertionError("dyadic mass differs")
checks += batch
# Capture the complete sequential selector. Replaying reads fresh RNG
# keys and masks; candidate retirement counters must reset each invocation.
anchor = torch.zeros(batch, device=dev, dtype=torch.int64)
def run() -> None:
for pos in range(depth):
previous = {r: anchor if pos == 0 else output[r][:, pos - 1] for r in model.ALL}
ctx.select(model.ALL, logits, previous, output, pos)
for _ in range(2):
run()
torch.cuda.synchronize()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
run()
times: list[float] = []
event_factory = cast(Callable[..., torch.cuda.Event], torch.cuda.Event)
start, end = event_factory(enable_timing=True), event_factory(enable_timing=True)
for rnd in range(100):
ctx.state[0]["keys"][:, 1].fill_(rnd)
start.record()
graph.replay()
end.record()
end.synchronize()
ctx.result()
times.append(start.elapsed_time(end))
# Fixed RNG keys imply exact graph/eager equality, including sparse q.
before = (output[0].clone(), ctx.state[0]["ids"].clone(), ctx.state[0]["q"].clone())
run()
for actual, expected in zip((output[0], ctx.state[0]["ids"], ctx.state[0]["q"]), before, strict=True):
torch.testing.assert_close(actual, expected, rtol=0, atol=0)
times.sort()
return {"batch": batch, "depth": depth, "sampled_rows": int(mask.sum().item()),
"exact_position_checks": checks, "graph_replays": 100,
"selector_median_ms": times[50], "selector_p95_ms": times[95],
"scope": "MEASURED two logical TP2 shards on solo; selector including Markov, excludes backbone/pair"}
def compile_peer_branches(model: Any) -> None:
"""Compile both real-rank specializations without executing peer traffic.
This catches JIT/assembly errors; it does not qualify physical-pair numerics,
visibility, timeout or graph interoperability.
"""
# JIT warmup is a dynamic compiler API absent from the narrow launch stub.
kernel: Any = sampled_fused._kernel
class CompileOnly:
def warmup(self, *args: Any, **kwargs: Any) -> Any:
return kernel.warmup(*args, **kwargs)
def __getitem__(self, grid: Any) -> Any:
def compile_call(*args: Any, **kwargs: Any) -> Any:
return kernel.warmup(*args, grid=grid, **kwargs)
return compile_call
model.emulated = False
try:
ctx = FusedContext(model, 1, 5)
logits = {r: torch.zeros(1, 5, rk.v1 - rk.v0, device=rk.dev, dtype=torch.bfloat16)
for r, rk in enumerate(model.ranks)}
previous = {r: torch.zeros(1, device=rk.dev, dtype=torch.int64) for r, rk in enumerate(model.ranks)}
outputs = {r: torch.zeros(1, 5, device=rk.dev, dtype=torch.int64) for r, rk in enumerate(model.ranks)}
with patch.object(sampled_fused, "_kernel", CompileOnly()):
ctx.select((0, 1), logits, previous, outputs, 0)
finally:
model.emulated = True
def edge_cases(model: Any) -> dict[str, bool]:
dev = model.devs[0]
ctx = FusedContext(model, 2, 5)
keys = torch.tensor([[4104, 7, 100], [4105, 8, 101]], device=dev, dtype=torch.int64)
mask = torch.tensor([True, False], device=dev)
ctx.update(keys, mask)
logits = {r: torch.zeros(2, 5, rk.v1 - rk.v0, device=dev, dtype=torch.bfloat16)
for r, rk in enumerate(model.ranks)}
previous = {r: torch.zeros(2, device=dev, dtype=torch.int64) for r in model.ALL}
output = {r: torch.zeros(2, 5, device=dev, dtype=torch.int64) for r in model.ALL}
# Force exact global ties, including the shard boundary. Signed zero must
# not move a larger ID ahead of a smaller ID.
saved = [rk.markov.cache[0].clone() for rk in model.ranks]
try:
for rk in model.ranks:
rk.markov.cache[0].zero_()
logits[0][:, 0, 1] = -0.
ctx.select(model.ALL, logits, previous, output, 0)
ids, q = ctx.result()
expected_ids = torch.arange(32, device=dev, dtype=torch.int32).expand(2, 32)
torch.testing.assert_close(ids[:, 0], expected_ids, rtol=0, atol=0)
assert output[0][1, 0].item() == 0
torch.testing.assert_close(q[0, 0], torch.full((32,), 1 / 32, device=dev), rtol=0, atol=0)
assert torch.count_nonzero(q[1, 0]).item() == 0
for bad in (float("nan"), float("inf")):
logits[1][0, 0, 3] = bad
ctx.select(model.ALL, logits, previous, output, 0)
for pos in range(1, 5):
continuation = {r: output[r][:, pos - 1] for r in model.ALL}
ctx.select(model.ALL, logits, continuation, output, pos)
try:
ctx.result()
except RuntimeError:
pass
else:
raise AssertionError("invalid fused logit accepted")
for rank in model.ALL:
logits[rank][:, 0].fill_(float("-inf"))
ctx.select(model.ALL, logits, previous, output, 0)
try:
ctx.result()
except RuntimeError:
pass
else:
raise AssertionError("all-masked fused logits accepted")
finally:
for rk, cache in zip(model.ranks, saved, strict=True):
rk.markov.cache[0].copy_(cache)
return {"lower_id_ties": True, "signed_zero": True, "mixed_t0_no_draw": True,
"invalid_intermediate_safe_continuation": True,
"nan_inf_all_masked_refused": True}
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--out", type=Path, required=True)
args = parser.parse_args()
if os.environ.get("CUDA_VISIBLE_DEVICES") != UUID:
raise RuntimeError("PP4 assigned solo UUID required")
fd = int(os.environ["PP4_LOCK_FD"])
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
if str(torch.cuda.get_device_properties(0).uuid).removeprefix("GPU-") != UUID.removeprefix("GPU-"):
raise RuntimeError("wrong physical GPU")
torch.set_num_threads(8)
torch.set_num_interop_threads(1)
torch.manual_seed(4104)
rows: list[dict[str, Any]] = []
root = Path(__file__).resolve().parents[2]
result: dict[str, Any] = {"schema": "pp4.fused-qualification.v1", "complete": False,
"classification": "MEASURED synthetic solo", "uuid": UUID,
"torch": torch.__version__, "rows": rows,
"source_sha256": digest(Path(__file__)),
"fused_sha256": digest(root / "worker/sampled_fused.py")}
started = time.monotonic()
model = model_fixture()
compile_peer_branches(model)
result["peer_specializations_compile_only"] = True
result["edges"] = edge_cases(model)
for batch in range(1, 5):
for depth in (4, 5):
for mixed in (False, True):
mask = torch.tensor([j % 2 == 0 if mixed else True for j in range(batch)], device=model.devs[0])
row = qualify(model, batch, depth, mask)
rows.append(row)
result["maxrss_kib"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
result["gpu_peak_bytes"] = torch.cuda.max_memory_allocated()
args.out.write_text(json.dumps(result, indent=2) + "\n")
print(json.dumps(row), flush=True)
result["elapsed_s"] = time.monotonic() - started
result["complete"] = True
args.out.write_text(json.dumps(result, indent=2) + "\n")
if __name__ == "__main__":
main()
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