158 lines
9.6 KiB
Python
158 lines
9.6 KiB
Python
"""Summarize native Windows d4r CPU stages and optional serializing HIP events.
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CPU completion time is not GPU execution time. HIP occupancy API predictions
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are not hardware counters. No bandwidth / WMMA utilization is inferred here.
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"""
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import argparse
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import collections
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import json
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import pathlib
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import re
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import statistics
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def stats(values):
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values = sorted(values)
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def quantile(q):
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index = (len(values) - 1) * q
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lower = int(index)
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return values[lower] + (values[min(lower + 1, len(values) - 1)] - values[lower]) * (index - lower)
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return dict(samples=len(values), total=sum(values), mean=statistics.mean(values),
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median=statistics.median(values), p95=quantile(.95), maximum=values[-1])
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def metadata(directory):
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result = {}
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if directory is None:
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return result
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for path in sorted(directory.glob('*.txt')):
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text = path.read_text(encoding='utf-8-sig', errors='replace')
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for block in re.split(r'(?m)^ - \.args:', text)[1:]:
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fields = dict(re.findall(r'(?m)^ \.(\w+):\s*([^\r\n]+)', block))
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if 'name' in fields:
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result[fields['name'].strip()] = {key: int(fields[key]) for key in (
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'wavefront_size', 'vgpr_count', 'sgpr_count', 'vgpr_spill_count', 'sgpr_spill_count',
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'group_segment_fixed_size', 'private_segment_fixed_size') if key in fields}
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return result
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def report(directory, metadata_directory=None):
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kernel_metadata = metadata(metadata_directory)
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stages = collections.defaultdict(list)
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commands = collections.defaultdict(list)
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kernels = collections.defaultdict(list)
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hook_threads = collections.defaultdict(list)
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apis = collections.defaultdict(list)
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replay = collections.defaultdict(lambda: collections.defaultdict(dict))
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for path in sorted(directory.glob('*.log')):
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data = path.read_bytes()
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text = data.decode('utf-16' if data.startswith((b'\xff\xfe', b'\xfe\xff')) else 'utf-8-sig', errors='replace')
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for name, variant, pair, value in re.findall(
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r'D4R_REPLAY_PROFILE kernel=(\S+) variant=(control|candidate) pair=([0-9]+) gpu_ms=([0-9.]+)', text):
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replay[name][int(pair)][variant] = float(value)
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for name, value in re.findall(r'D4R_STAGE name=(\S+) cpu_ms=([0-9.]+)', text):
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stages[name].append(float(value))
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for kind, fields in re.findall(r'(?m)^D4R_COMMAND_(RECORD|SUBMIT) ([^\r\n]+)', text):
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for name, value in re.findall(r'(\w+_ms)=([0-9.]+)', fields):
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number = float(value)
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if name != 'interval_ms' or number > 0:
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commands[kind.lower() + '.' + name].append(number)
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for line in text.splitlines():
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if line.startswith('D4R_CUDA_API_PROFILE '):
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fields = dict(re.findall(r'(\w+)=(\S+)', line))
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if all(key in fields for key in ('api', 'calls', 'total_ms', 'max_ms', 'period_ms', 'thread')):
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apis[(fields['thread'], fields['api'])].append(fields)
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if line.startswith('D4R_COMMAND_HOOK_SAMPLE '):
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fields = dict(re.findall(r'(\w+)=(\S+)', line))
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if all(name in fields for name in ('thread', 'period_ms', 'calls', 'samples', 'marked',
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'access_sum_ms', 'capture_sum_ms', 'driver_sum_ms')):
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hook_threads[fields['thread']].append(fields)
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if not line.startswith('D4R_KERNEL_PROFILE '):
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continue
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fields = dict(re.findall(r'(\w+)=("[^"]+"|\S+)', line))
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kernels[(fields['kernel'].strip('"'), fields['backend'], fields['phase'])].append(fields)
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result = dict(cpuStages={name: stats(values) for name, values in stages.items()},
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commandStages={name: stats(values) for name, values in commands.items()},
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commandHookSamples=[],
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cudaApi=[],
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replayPairs=[],
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kernels=[], notes=[
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'CPU stage times include waits and host work; they are not isolated GPU timings.',
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'CUDA API profiles measure host call duration without GPU events or added synchronization; they still include existing waits.',
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'Command record intervals are between NGX recording calls on one thread, not Present/FPS.',
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'Hook estimates use random one-in-64 samples; driver timing covers generated forwarding methods only.',
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'Hook access includes lock waits; summing threads does not measure serial frame latency or CPU execution time.',
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'HIP-event profiling synchronizes every sampled launch and changes scheduling.',
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'Occupancy is an API prediction, not a measured hardware counter.',
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'Bandwidth and WMMA utilization require additional supported hardware tooling.'])
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for name, pairs in replay.items():
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complete = [row for row in pairs.values() if set(row) == {'control', 'candidate'}]
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if complete:
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result['replayPairs'].append(dict(kernel=name,
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controlGpuMs=stats([row['control'] for row in complete]),
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candidateGpuMs=stats([row['candidate'] for row in complete]),
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candidateToControl=stats([row['candidate'] / row['control'] for row in complete if row['control'] > 0])))
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for (thread, api), rows in apis.items():
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calls = sum(int(row['calls']) for row in rows)
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total = sum(float(row['total_ms']) for row in rows)
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result['cudaApi'].append(dict(thread=thread, api=api, calls=calls, totalMs=total,
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meanMs=total / calls, maxMs=max(float(row['max_ms']) for row in rows),
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periodMs=sum(float(row['period_ms']) for row in rows)))
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result['cudaApi'].sort(key=lambda row: row['totalMs'], reverse=True)
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for thread, rows in hook_threads.items():
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sums = {name: sum(float(row[name]) for row in rows) for name in
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('period_ms', 'calls', 'samples', 'marked', 'access_sum_ms', 'capture_sum_ms', 'driver_sum_ms')}
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if not sums['samples'] or not sums['period_ms']:
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continue
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scale = sums['calls'] / sums['samples']
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estimate = scale * (sums['access_sum_ms'] + sums['capture_sum_ms'])
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result['commandHookSamples'].append(dict(thread=int(thread), periods=len(rows), **sums,
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accessMeanUs=1000 * sums['access_sum_ms'] / sums['samples'],
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captureMeanUsPerSample=1000 * sums['capture_sum_ms'] / sums['samples'],
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estimatedAccessCaptureMs=estimate, estimatedAccessCaptureMsPerSecond=1000 * estimate / sums['period_ms']))
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for (name, backend, phase), rows in kernels.items():
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item = dict(kernel=name, backend=backend, phase=phase,
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gpuMs=stats([float(row['gpu_ms']) for row in rows]),
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enqueueMs=stats([float(row['enqueue_ms']) for row in rows]),
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completionMs=stats([float(row['completion_ms']) for row in rows]),
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configurations=[dict(config) for config in sorted({tuple((key, row[key]) for key in
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('grid', 'block', 'static_lds_bytes', 'dynamic_lds_bytes', 'regs', 'private_bytes',
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'max_threads', 'predicted_blocks_per_multiprocessor')) for row in rows})])
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result['kernels'].append(item)
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if backend == 'native':
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item['elfMetadata'] = kernel_metadata.get(name + ('_prep' if phase == 'prep' else ''), {})
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result['kernels'].sort(key=lambda row: row['gpuMs']['total'], reverse=True)
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return result
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('directory', type=pathlib.Path)
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parser.add_argument('--output', type=pathlib.Path)
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parser.add_argument('--metadata-directory', type=pathlib.Path, help='llvm-readobj --notes output for native code objects')
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args = parser.parse_args()
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result = report(args.directory, args.metadata_directory)
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if not any(result[key] for key in ('cpuStages', 'kernels', 'commandStages', 'commandHookSamples', 'replayPairs', 'cudaApi')):
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parser.error('no complete stage or HIP-event records found')
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if args.output:
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args.output.write_text(json.dumps(result, indent=2) + '\n', encoding='utf-8')
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for name, row in result['cpuStages'].items():
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print(f"CPU {name}: n={row['samples']} mean={row['mean']:.3f} ms median={row['median']:.3f} ms p95={row['p95']:.3f} ms")
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for name, row in result['commandStages'].items():
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print(f"COMMAND {name}: n={row['samples']} mean={row['mean']:.3f} ms median={row['median']:.3f} ms p95={row['p95']:.3f} ms")
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for row in result['cudaApi'][:20]:
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print(f"CUDA API {row['api']} {row['thread']}: n={row['calls']} mean={row['meanMs']:.6f} ms total={row['totalMs']:.3f} ms max={row['maxMs']:.3f} ms")
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for row in result['commandHookSamples']:
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print(f"HOOK thread={row['thread']} samples={int(row['samples'])} access={row['accessMeanUs']:.3f} us "
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f"capture={row['captureMeanUsPerSample']:.3f} us estimated_cpu={row['estimatedAccessCaptureMsPerSecond']:.3f} ms/s")
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for row in result['replayPairs']:
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print(f"PAIRED {row['kernel']}: n={row['controlGpuMs']['samples']} control_median={row['controlGpuMs']['median']:.6f} ms "
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f"candidate_median={row['candidateGpuMs']['median']:.6f} ms ratio_median={row['candidateToControl']['median']:.6f}")
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for row in result['kernels']:
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times = row['gpuMs']
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print(f"GPU {row['kernel']} {row['backend']} {row['phase']}: n={times['samples']} mean={times['mean']:.6f} ms total={times['total']:.3f} ms")
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if __name__ == '__main__':
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main()
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