File size: 11,840 Bytes
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 | """PP3 solo test of the actual prepared image standard-rejection kernels.
No vLLM engine, model, Spark access or service changes. Five predeclared toy
targets, 20000 requests each; chi-square family alpha 1e-5 (df=3). Tests full
target mass outside q, disjoint/equal proposals, no-q invalidation and T0.
"""
from __future__ import annotations
import argparse
import hashlib
import importlib
import importlib.util
import json
import math
import os
import resource
import sys
import types
from pathlib import Path
from typing import Any
import torch
import triton
import triton.language as tl
from triton.language.extra.cuda import libdevice
from offload.dspark.worker.sampled import Top32, _finish
from offload.dspark.worker.tools.pp3_probe import UUID, timing
def load(name: str, path: Path) -> Any:
spec = importlib.util.spec_from_file_location(name, path)
if spec is None or spec.loader is None:
raise ImportError(str(path))
module = importlib.util.module_from_spec(spec)
sys.modules[name] = module
spec.loader.exec_module(module)
return module
def modules(source: Path, overlay: Path) -> Any:
# Package-only shims avoid importing a GPU engine or its platform plugins.
# All math functions (Gumbel, Philox, verifier) are the real pinned sources.
names = ('vllm', 'vllm.v1', 'vllm.v1.worker', 'vllm.v1.worker.gpu',
'vllm.v1.worker.gpu.sample', 'vllm.v1.worker.gpu.spec_decode',
'vllm.v1.watermarking', 'vllm.v1.watermarking.prfs')
for name in names:
package = types.ModuleType(name)
package.__path__ = []
sys.modules[name] = package
shim = types.ModuleType('vllm.triton_utils')
for key, value in {'triton': triton, 'tl': tl, 'tldevice': libdevice, 'HAS_TRITON': True}.items():
setattr(shim, key, value)
sys.modules[shim.__name__] = shim
for suffix in ('watermarking.prfs.base', 'watermarking.prfs.philox',
'worker.gpu.sample.gumbel', 'worker.gpu.sample.watermark'):
load('vllm.v1.'+suffix, source / ('vllm/v1/'+suffix.replace('.', '/')+'.py'))
return load('vllm.v1.worker.gpu.spec_decode.rejection_sampler_utils',
overlay / 'vllm/v1/worker/gpu/spec_decode/rejection_sampler_utils.py')
def main() -> None:
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--source', type=Path, required=True)
p.add_argument('--overlay', type=Path, required=True)
p.add_argument('--out', type=Path, required=True)
a = p.parse_args()
if os.environ.get('CUDA_VISIBLE_DEVICES') != UUID:
raise RuntimeError('solo UUID required')
import fcntl
fcntl.flock(int(os.environ['PP3_LOCK_FD']), fcntl.LOCK_EX | fcntl.LOCK_NB)
if str(torch.cuda.get_device_properties(0).uuid).removeprefix('GPU-') != UUID.removeprefix('GPU-'):
raise RuntimeError('CUDA UUID mismatch')
torch.set_num_threads(8)
torch.set_num_interop_threads(1)
rs = modules(a.source, a.overlay)
dev = torch.device('cuda:0')
n, vocab = 20000, 64
cases = ('full_overlap', 'sparse_tail', 'disjoint', 'equal', 'invalidated')
results: list[dict[str, object]] = []
receipt: dict[str, object] = {'schema': 'pp3.gpu-qualification.v1', 'complete': False,
'classification': 'MEASURED solo SM86; actual prepared image kernels, engine not loaded',
'cases': results, 'trials_per_case': n, 'family_alpha': 1e-5,
'torch': torch.__version__, 'triton': triton.__version__, 'uuid': UUID,
'source_sha256': hashlib.sha256(Path(__file__).read_bytes()).hexdigest()}
receipt['dependency_sha256'] = {str(path.relative_to(root)): hashlib.sha256(path.read_bytes()).hexdigest()
for root in (a.source, a.overlay) for path in sorted(root.rglob('*.py'))}
producer_path = importlib.import_module('offload.dspark.worker.sampled').__file__
if producer_path is None:
raise RuntimeError('producer source path missing')
receipt['producer_sha256'] = hashlib.sha256(Path(producer_path).read_bytes()).hexdigest()
def save() -> None:
receipt['maxrss_kib'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
receipt['gpu_peak_bytes'] = torch.cuda.max_memory_allocated()
a.out.write_text(json.dumps(receipt, indent=2)+'\n')
save()
for case_id, case in enumerate(cases):
weights = torch.tensor([.125, .25, .25, .375], device=dev)
pids = torch.tensor([40, 41, 42, 43], device=dev)
qids = pids if case != 'disjoint' else torch.tensor([0, 1, 2, 3], device=dev)
qw = weights if case == 'equal' else torch.tensor([.5, .125, .125, .25], device=dev)
if case == 'sparse_tail':
qw = torch.tensor([.5, .5, 0., 0.], device=dev)
logits = torch.full((1, vocab), -torch.inf, device=dev)
logits[0, qids] = qw.log() * .7
sampler = Top32(1, vocab, dev)
keys = torch.tensor([[4103000+case_id, 1, 32768]], dtype=torch.int64, device=dev)
sampler(logits, keys, torch.tensor([.7], device=dev))
# Reuse selected candidates, varying the actual producer's Philox key.
candidates = sampler.candidates.expand(n, -1).contiguous()
flags = torch.zeros(n, sampler.tiles, dtype=torch.int32, device=dev)
keys = torch.stack((torch.arange(n, device=dev, dtype=torch.int64)+4103000+case_id*100000,
torch.full((n,), 1, device=dev), torch.full((n,), 32768, device=dev)), 1)
token = torch.empty(n, dtype=torch.int32, device=dev)
support = torch.empty(n, 32, dtype=torch.int32, device=dev)
q = torch.empty(n, 32, device=dev)
error = torch.empty(n, dtype=torch.int32, device=dev)
_finish[(n,)](candidates, keys, torch.full((n,), .7, device=dev), token, support, q,
error, flags, candidates.shape[1], triton.next_power_of_2(candidates.shape[1]), num_warps=4)
if error.any().item():
raise AssertionError('producer error')
dense = torch.zeros(n, 1, vocab, device=dev)
dense[:, 0].scatter_(1, support.long(), q)
target = torch.full((n, 2, vocab), -torch.inf, device=dev)
target[:, :, pids] = weights.log()
proposal = torch.stack((torch.zeros_like(token), token), 1).flatten()
if case == 'invalidated':
proposal[1::2] = -1
dense.zero_()
cu = torch.arange(0, n*2+1, 2, dtype=torch.int32, device=dev)
mapping = torch.arange(n, dtype=torch.int32, device=dev)
expanded = mapping.repeat_interleave(2)
localpos = torch.tensor([0, 1], device=dev, dtype=torch.int32).repeat(n)
positions = (localpos+32768).long()
seeds = torch.arange(n, device=dev, dtype=torch.int64)+9900000+case_id*100000
temp = torch.ones(n, device=dev)
os.environ['VLLM_DSPARK_SAMPLED_TOP32'] = '1'
emitted, lengths = rs.rejection_sample(target.flatten(0, 1), dense, proposal,
cu, positions, mapping, expanded, localpos, temp, seeds, 1)
counts = torch.bincount(emitted[:, 0], minlength=vocab)
outside = int(counts.sum().item()-counts[pids].sum().item())
if outside:
raise AssertionError('output escaped target support')
observed = counts[pids].cpu().tolist()
expected = [n*x for x in (.125, .25, .25, .375)]
chi = sum((float(o)-e)**2/e for o, e in zip(observed, expected, strict=True))
x = math.sqrt(chi/2)
pvalue = math.erfc(x) + math.sqrt(2*chi/math.pi)*math.exp(-chi/2)
if pvalue <= 1e-5/len(cases):
raise AssertionError(f'{case}: chi-square rejected ({chi}, p={pvalue})')
row: dict[str, object] = {'case': case, 'counts': observed, 'chi_square_df3': chi,
'p_value': pvalue, 'accepted': int((lengths > 1).sum().item())}
results.append(row)
save()
print(json.dumps(row), flush=True)
# Same target, same greedy requests/seeds: actual image old/new paths
# must agree byte-for-byte despite q being present in the new path.
temp.zero_()
os.environ['VLLM_DSPARK_SAMPLED_TOP32'] = '0'
old, old_lengths = rs.rejection_sample(target.flatten(0, 1), None, proposal,
cu, positions, mapping, expanded, localpos, temp, seeds, 1)
os.environ['VLLM_DSPARK_SAMPLED_TOP32'] = '1'
new, new_lengths = rs.rejection_sample(target.flatten(0, 1), dense, proposal,
cu, positions, mapping, expanded, localpos, temp, seeds, 1)
torch.testing.assert_close(old_lengths, new_lengths, rtol=0, atol=0)
# Image allocates sampled with torch.empty; positions >=num_sampled
# are undefined storage, not emitted tokens. Compare every defined byte.
valid = torch.arange(old.shape[1], device=dev)[None, :] < old_lengths[:, None]
torch.testing.assert_close(old[valid], new[valid], rtol=0, atol=0)
# Deterministic ties, signed zero, bad logits/temperatures, and reordering.
sampler = Top32(4, 64, dev)
x = torch.zeros(4, 64, device=dev)
x[:, ::2] = -0.
keys = torch.tensor([[4103+j, 12, 32768] for j in range(4)], device=dev)
tau = torch.full((4,), .7, device=dev)
tok, support, q = sampler(x, keys, tau)
first = tok.clone()
torch.testing.assert_close(support, torch.arange(32,device=dev,dtype=torch.int32).expand(4,-1),rtol=0,atol=0)
torch.testing.assert_close(q, torch.full((4,32),1/32,device=dev),rtol=0,atol=0)
tok, _, _ = sampler(x.flip(0), keys.flip(0), tau)
torch.testing.assert_close(tok, first.flip(0),rtol=0,atol=0)
x[0] = -torch.inf
x[1, 50] = torch.nan
x[2, 50] = torch.inf
tau[3] = 0.
tok, _, q = sampler(x, keys, tau)
assert sampler.error.tolist() == [1]*4 and tok.tolist() == [-1]*4 and not q.any().item()
receipt['producer_edges'] = 'PASS ties/signed-zero/reordering/NaN/+inf/all-masked/tau0'
# Full-vocabulary dense-p verification cost. No target model or processors.
b, depth, v = 1, 5, 129280
torch.manual_seed(4103)
target = torch.randn(depth+1, v, device=dev)
dense = torch.zeros(b, depth, v, device=dev)
dense[:, :, :32] = 1/32
proposal = torch.zeros(depth+1, dtype=torch.int32, device=dev)
cu = torch.tensor([0, depth+1], dtype=torch.int32, device=dev)
mapping = torch.zeros(1, dtype=torch.int32, device=dev)
expanded = torch.zeros(depth+1, dtype=torch.int32, device=dev)
localpos = torch.arange(depth+1, dtype=torch.int32, device=dev)
positions = localpos.long()+32768
temp, seeds = torch.ones(1, device=dev), torch.tensor([9900000], device=dev)
def run(q: torch.Tensor | None = dense) -> object:
return rs.rejection_sample(target, q, proposal, cu, positions, mapping, expanded,
localpos, temp, seeds, depth)
os.environ['VLLM_DSPARK_SAMPLED_TOP32'] = '1'
receipt['dense_p_verifier'] = timing(run)
os.environ['VLLM_DSPARK_SAMPLED_TOP32'] = '0'
receipt['greedy_q_verifier'] = timing(lambda: run(None))
# Sparse support IDs + q staged once in each direction; no network claim.
payload = torch.zeros(320, dtype=torch.int32, device=dev)
host = torch.empty_like(payload, device='cpu', pin_memory=True)
receipt['sparse1280_d2h'] = timing(lambda: host.copy_(payload, non_blocking=True))
receipt['sparse1280_h2d'] = timing(lambda: payload.copy_(host, non_blocking=True))
dense = torch.empty(1,5,v,device=dev)
support = torch.arange(32,device=dev).expand(1,5,-1)
q = torch.full((1,5,32),1/32,device=dev)
def expand_q() -> None:
dense.zero_()
dense.scatter_(2,support,q)
receipt['dense_q_expansion'] = timing(expand_q)
receipt['greedy_identity_requests'] = n*len(cases)
receipt['complete'] = True
save()
if __name__ == '__main__':
main()
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