93 lines
4.6 KiB
Python
93 lines
4.6 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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kernels = collections.defaultdict(list)
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for path in sorted(directory.glob('*.log')):
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text = path.read_text(encoding='utf-8-sig', errors='replace')
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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 line in text.splitlines():
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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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kernels=[], notes=[
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'CPU stage times include waits and host work; they are not isolated GPU timings.',
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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, 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 result['cpuStages'] and not result['kernels']:
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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 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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