236 lines
12 KiB
Python
236 lines
12 KiB
Python
"""Opt-in, frozen N64 space-time GPU trajectory; no convergence certification."""
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import argparse
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import hashlib
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import json
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import os
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from pathlib import Path
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import resource
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import subprocess
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import sys
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import time
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import numpy as np
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ROOT = Path(__file__).resolve().parents[3]
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sys.path.insert(0, str(ROOT/'src'))
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sys.path.insert(0, str(Path(__file__).parents[1]/'taylor_green'))
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from CelerisLab.config import LBMConfig
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import oracle as tg
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import spacetime_gpu as gpu
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ref = gpu.ref
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CPU_SHA = '180eef90a1c3f43113fe18cc54981ee4723cfc0cc2724334907ff188022fa770'
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UNIFORM_SHA = '01e7415ae39456f2de7f0c3d331afb971c49d19353b43038650e32e80737f1e7'
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CUDA_SHA = 'c47869688f7ac2ec93411c0af233887fe8b77405838299e4ae52c8963fd97fe4'
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TABLE_SHA = 'f2e62038dc620c5a10bb61d51a88be2afd80488f2f9f4a10c2677cfbe11009cf'
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STEPS = (256, 512, 768, 1024)
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TIMES = (.12, .24, .36, .48)
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FACTORS = dict(velocity_l2=.8, pressure_l2=1., energy_relative=1.)
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LIMITS = dict(velocity_l2=.05, pressure_l2=.15, energy_relative=.05, mass_relative=1e-5)
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HEALTH = ('boundary_pre_nonfinite', 'boundary_pre_nonpositive', 'boundary_stored_nonfinite',
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'boundary_stored_nonpositive', 'physical_population_nonfinite', 'physical_population_nonpositive')
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def sha(path):
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return hashlib.sha256(Path(path).read_bytes()).hexdigest()
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def load_references(cpu_path, uniform_path):
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for path, expected in ((cpu_path, CPU_SHA), (uniform_path, UNIFORM_SHA)):
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if sha(path) != expected:
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raise ValueError(f'pinned reference receipt differs: {path}')
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cpu, uniform = [json.loads(Path(p).read_text()) for p in (cpu_path, uniform_path)]
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assert cpu['completed'] and cpu['experimental_pass'] and not cpu['source']['dirty']
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assert cpu['source']['commit'] == '07879bc52bfef8d95503146c55d3823c8b6f8d0a'
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assert [r['mode'] for r in cpu['runs']] == ['constant_ivp', 'spacetime']
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assert all(tuple(row['tstar'] for row in r['rows']) == TIMES for r in cpu['runs'])
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assert uniform['completed'] and not uniform['source']['dirty']
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assert uniform['source']['revision'] == '9805383f9c50d867e8555471baf5dbda8333a652'
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assert (uniform['n'], uniform['depth'], uniform['viscosity']) == (64, 1, .0192)
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assert uniform['limits'] == tg.LIMITS == LIMITS
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return cpu, uniform
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def total(state, maps):
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arrays = [a.astype(np.float64) for a in (state.coarse_f, state.fine_f)]
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return ref.cpu.MOMENTS.T @ ref.directional(ref.cpu.State(*arrays), maps)
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def warm_and_reset(run, initial, check_cap):
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"""Time 32 synchronized intervals, then restore exact uploaded arrival F."""
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run.initialize(initial)
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before = run.download()
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started = time.monotonic()
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for _ in range(32):
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check_cap()
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run.step(audit=False)
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seconds = time.monotonic()-started
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warm_health = run.health_counts.tolist()
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run.initialize(initial)
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after = run.download()
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for a, b in zip((before.coarse_f, before.fine_f), (after.coarse_f, after.fine_f)):
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np.testing.assert_array_equal(a, b)
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if run.step_count != 0 or after.tick != 0 or run.health_counts.any():
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raise AssertionError('warm-up reset did not restore tick/counters')
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return after, dict(intervals=32, seconds=seconds, estimated_1024_seconds=32*seconds,
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health_counts=warm_health, reset_verified=True)
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def assess(rows, cpu, uniform, health_counts):
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if tuple(row['tstar'] for row in rows) != TIMES:
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raise ValueError('four frozen sample times required')
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worst = {key: max(row[key] for row in rows) for key in LIMITS}
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campaign = {key: all(np.isfinite(row[key]) and 0 <= row[key] <= limit for row in rows)
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for key, limit in LIMITS.items()}
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campaign['population_health'] = not any(health_counts)
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campaign['sampled_positive_finite'] = all(
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np.isfinite(row[key]) and row[key] > 0 for row in rows
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for key in ('minimum_population', 'minimum_boundary_pre', 'minimum_boundary_stored'))
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baseline, candidate = cpu['runs']
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thresholds = {key: factor*baseline['worst'][key] for key, factor in FACTORS.items()}
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experimental = {key: all(np.isfinite(row[key]) and row[key] >= 0 for row in rows)
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and worst[key] <= threshold for key, threshold in thresholds.items()}
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differences = []
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for actual, expected in zip(rows, candidate['rows']):
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delta = {}
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for key, value in expected.items():
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d = np.asarray(actual[key])-np.asarray(value)
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delta[key] = dict(cpu=value, signed=d.tolist(), absolute=np.abs(d).tolist())
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differences.append(dict(tstar=actual['tstar'], fields=delta))
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historical_worst = max(row['pressure_l2'] for row in uniform['rows'] if row['tstar'] > 0)
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historical_limit = max(2*historical_worst, .02)
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assert historical_limit == .024506347612699403
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return dict(worst=worst, campaign_checks=campaign, campaign_pass=all(campaign.values()),
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experimental_thresholds=thresholds, experimental_checks=experimental,
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experimental_pass=all(experimental.values()),
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gpu_minus_cpu=dict(status='descriptive_only_no_equivalence_tolerance', rows=differences),
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historical_pressure_gate=dict(status='historical_shifted_FP32_uniform_vs_unshifted_FP32_candidate',
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matched_storage=False, uniform_worst=historical_worst, threshold=historical_limit,
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candidate_worst=worst['pressure_l2'], passed=worst['pressure_l2'] <= historical_limit))
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--gpu-admitted', action='store_true')
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parser.add_argument('--cpu-receipt', type=Path, required=True)
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parser.add_argument('--uniform-receipt', type=Path, required=True)
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parser.add_argument('--output', type=Path, required=True)
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args = parser.parse_args()
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if not args.gpu_admitted or not os.environ.get('CUDA_VISIBLE_DEVICES'):
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parser.error('explicit --gpu-admitted and CUDA_VISIBLE_DEVICES required')
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if args.output.exists():
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parser.error('output exists; preserve evidence and choose a new path')
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cpu, uniform = load_references(args.cpu_receipt, args.uniform_receipt)
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gpu.check_sources()
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if sha(Path(gpu.__file__).with_suffix('.cu')) != CUDA_SHA:
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raise RuntimeError('reviewed CUDA source changed')
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source = dict(commit=subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=ROOT, text=True).strip(),
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dirty=bool(subprocess.check_output(['git', 'status', '--porcelain'], cwd=ROOT, text=True)),
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hashes={p.name: sha(p) for p in (Path(__file__), Path(gpu.__file__), Path(gpu.__file__).with_suffix('.cu'),
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Path(__file__).with_name('test_spacetime_gpu.py'))},
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reference_hashes=gpu.PINS, numpy=np.__version__, checkout=str(ROOT))
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if source['dirty']:
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raise RuntimeError('clean committed checkout required')
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started = time.monotonic()
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report = dict(scope='N64 experimental space-time GPU; no grid-convergence qualification',
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completed=False, passed=False, source=source, rows=[], health_counts=[0]*6, health_labels=HEALTH,
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setup=dict(n=64, bounds=[8,8,56,48], re_L=100, U=.03, viscosity=.0192, tau=[.5576,.6152],
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phase='arrival_f', storage='unshifted_FP32', collision='SRT', interval_count=1024,
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initialization='same physical FP64 equilibrium as CPU, rounded to FP32 on upload',
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cpu_storage='FP64', sample_steps=STEPS, sample_times=TIMES),
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references=dict(cpu_receipt_sha256=CPU_SHA, cpu_source=cpu['source'],
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cpu_archive_drive_id='197F9nwDOGf_YROK1a46YpVvNrSldnH_w',
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cpu_archive_sha256='850ddce3f5203ef38e76e5153e718a9b92e137d1fc388ba7cd12f5d47eab83b4',
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uniform_receipt_sha256=UNIFORM_SHA, uniform_source=uniform['source'],
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uniform_storage='historical_shifted_FP32'),
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frozen_screen=dict(campaign_limits=LIMITS, experimental_factors=FACTORS,
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gpu_cpu_equivalence_tolerance=None, historical_pressure_threshold=.024506347612699403),
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boundary_budget_status='not_measured', max_directional_residual=None,
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minima_scope='initial and four sampled physical arrival states; boundary minima at four samples',
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limits=dict(seconds=180., peak_rss_mib=512.), cuda_visible_devices=os.environ['CUDA_VISIBLE_DEVICES'])
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run = context = None
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phase = 'setup'
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def save():
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report['resources'] = dict(seconds=time.monotonic()-started,
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peak_rss_mib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,
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device_allocations_bytes=None if run is None else run.device_bytes,
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device_memory_scope='owner allocations; excludes compiler, driver and context')
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if run is not None:
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report['health_counts'] = run.health_counts.tolist()
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report['completed_intervals'] = run.step_count
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ref.write_receipt(args.output, report)
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def check_cap():
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if time.monotonic()-started > 180 or resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024 > 512:
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raise RuntimeError('frozen resource cap exceeded')
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save()
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try:
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from CelerisLab.cuda.context import CudaContext
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context = CudaContext(0)
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config = LBMConfig(nx=64, ny=64, viscosity=.0192, velocity=0., ddf_shifting=False)
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run = gpu.SpaceTimeGPU(config, (8,8,56,48), context=context)
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report.update(device=context.name, sm_arch=context.sm_arch, build_metadata=run.build_metadata,
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table_sha256=gpu.table_hash(run.tables))
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if report['table_sha256'] != TABLE_SHA:
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raise RuntimeError('frozen coordinate tables changed')
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initial = ref.cpu.State(tg.initial((64,64),0,64,.0192), tg.initial((128,128),1,64,.0192))
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phase = 'warm_reset'
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initial, report['warmup'] = warm_and_reset(run, initial, check_cap)
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maps = run.plan.maps
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masks = (maps.coarse_active, maps.fine_active)
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arrays = (initial.coarse_f, initial.fine_f)
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mass0 = tg.metrics(arrays, masks, 0, 64, .0192)['mass']
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moment0 = total(initial, maps)
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report['initial'] = dict(mass=mass0, momentum=moment0[1:].tolist(),
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minimum_population=min(float(a[:,m].min()) for a,m in zip(arrays,masks)))
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save()
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check_cap()
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phase = 'trajectory'
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trajectory_started = time.monotonic()
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for step in range(1,1025):
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check_cap()
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run.step(audit=False)
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if step in STEPS:
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state = run.download()
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if state.tick != 2*step:
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raise AssertionError('sample is not synchronized arrival state')
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arrays = (state.coarse_f, state.fine_f)
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row = tg.metrics(arrays, masks, step, 64, .0192, mass0)
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row.update(step=step, momentum_change=(total(state,maps)-moment0)[1:].tolist(),
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mass_change=row['mass']-mass0,
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minimum_population=min(float(a[:,m].min()) for a,m in zip(arrays,masks)))
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with context.activate():
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row['minimum_boundary_pre'] = float(run.read(run.pre,(len(run.plan.groups),4),np.float64).min())
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row['minimum_boundary_stored'] = float(run.read(run.h,(len(run.plan.groups),4)).min())
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if not all(np.isfinite(value).all() for value in row.values()):
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report['failed_sample'] = {key: repr(value) for key,value in row.items()}
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raise FloatingPointError('nonfinite sample diagnostic')
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report['rows'].append(row)
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save()
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print(json.dumps(row, allow_nan=False), flush=True)
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report['trajectory_seconds'] = time.monotonic()-trajectory_started
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check_cap()
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report.update(assess(report['rows'], cpu, uniform, run.health_counts))
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report['completed'] = True
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report['passed'] = report['campaign_pass'] and report['experimental_pass']
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except Exception as exc:
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report['failure'] = dict(phase=phase, error=repr(exc))
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raise
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finally:
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# Preserve the primary failure and every completed sample even if cleanup fails.
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for owner in (run, context):
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if owner is not None:
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try:
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owner.close()
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except Exception as exc:
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report.setdefault('cleanup_errors', []).append(repr(exc))
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report['passed'] = False
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save()
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return 0 if report['passed'] else 1
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if __name__ == '__main__':
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raise SystemExit(main())
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