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5dc80b3 | 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 | #!/usr/bin/env python3
"""
CLI entry point for HRM SRAM/DRAM Memory Tiering Benchmarks.
Usage:
# Compare tiered vs baseline HRM
python run_benchmark.py --mode compare --batch-sizes 1,8,32 --seq-lens 64,128
# Benchmark tiered model only
python run_benchmark.py --mode tiered --warmup 5 --iterations 50 --output results.json
# Generate plots
python run_benchmark.py --mode compare --plot --output-dir benchmark_results/
# Quick smoke test
python run_benchmark.py --mode tiered --warmup 1 --iterations 3 --batch-sizes 2 --seq-lens 16
"""
import argparse
import json
import os
import sys
from dataclasses import asdict
# Add project root to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from benchmark import (
benchmark_tiered_model,
benchmark_baseline_model,
compare_models,
print_results_table,
generate_plots,
BenchmarkResult,
)
def parse_int_list(s: str):
return [int(x.strip()) for x in s.split(',')]
def main():
parser = argparse.ArgumentParser(
description='HRM SRAM/DRAM Memory Tiering Benchmark Suite (Triton)',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=__doc__,
)
parser.add_argument(
'--mode', choices=['tiered', 'baseline', 'compare'],
default='compare',
help='Benchmark mode: tiered-only, baseline-only, or comparison (default: compare)',
)
parser.add_argument(
'--batch-sizes', type=str, default='1,8,32',
help='Comma-separated batch sizes to benchmark (default: 1,8,32)',
)
parser.add_argument(
'--seq-lens', type=str, default='64,128',
help='Comma-separated sequence lengths (default: 64,128)',
)
parser.add_argument(
'--hidden-size', type=int, default=512,
help='Model hidden size (default: 512)',
)
parser.add_argument(
'--num-heads', type=int, default=8,
help='Number of attention heads (default: 8)',
)
parser.add_argument(
'--H-cycles', type=int, default=2,
help='H-level recurrence cycles (default: 2)',
)
parser.add_argument(
'--L-cycles', type=int, default=2,
help='L-level recurrence cycles (default: 2)',
)
parser.add_argument(
'--H-layers', type=int, default=4,
help='H-level transformer layers (default: 4)',
)
parser.add_argument(
'--L-layers', type=int, default=4,
help='L-level transformer layers (default: 4)',
)
parser.add_argument(
'--warmup', type=int, default=5,
help='Warmup iterations (default: 5)',
)
parser.add_argument(
'--iterations', type=int, default=20,
help='Benchmark iterations (default: 20)',
)
parser.add_argument(
'--output', type=str, default=None,
help='Output JSON file path for results',
)
parser.add_argument(
'--output-dir', type=str, default='benchmark_results',
help='Directory for output files and plots (default: benchmark_results/)',
)
parser.add_argument(
'--plot', action='store_true',
help='Generate comparison plots (requires matplotlib)',
)
parser.add_argument(
'--device', type=str, default=None,
help='Device: cuda, cpu, or cuda:N (default: auto-detect)',
)
args = parser.parse_args()
batch_sizes = parse_int_list(args.batch_sizes)
seq_lens = parse_int_list(args.seq_lens)
# Device setup
import torch
if args.device:
device = torch.device(args.device)
else:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"\n{'='*60}")
print(f" HRM SRAM/DRAM Memory Tiering Benchmark")
print(f" Device: {device}")
print(f" Mode: {args.mode}")
print(f" Batch sizes: {batch_sizes}")
print(f" Sequence lengths: {seq_lens}")
print(f" Hidden size: {args.hidden_size}")
print(f" H/L cycles: {args.H_cycles}/{args.L_cycles}")
print(f" H/L layers: {args.H_layers}/{args.L_layers}")
print(f" Warmup: {args.warmup}, Iterations: {args.iterations}")
print(f"{'='*60}")
if args.mode == 'compare':
results = compare_models(
batch_sizes=batch_sizes,
seq_lens=seq_lens,
hidden_size=args.hidden_size,
warmup=args.warmup,
iterations=args.iterations,
device=device,
)
if args.plot:
generate_plots(results, output_dir=args.output_dir)
# Save results
output_path = args.output or os.path.join(args.output_dir, 'results.json')
os.makedirs(os.path.dirname(output_path) or '.', exist_ok=True)
with open(output_path, 'w') as f:
json.dump(results, f, indent=2, default=str)
print(f"\n Results saved: {output_path}")
elif args.mode == 'tiered':
all_results = []
for bs in batch_sizes:
for sl in seq_lens:
print(f"\n Benchmarking tiered model: bs={bs}, seq={sl}")
r = benchmark_tiered_model(
batch_size=bs, seq_len=sl,
hidden_size=args.hidden_size,
num_heads=args.num_heads,
H_cycles=args.H_cycles, L_cycles=args.L_cycles,
H_layers=args.H_layers, L_layers=args.L_layers,
warmup=args.warmup, iterations=args.iterations,
device=device,
)
all_results.append(r)
print_results_table(all_results)
if args.output:
with open(args.output, 'w') as f:
json.dump([asdict(r) for r in all_results], f, indent=2, default=str)
print(f" Results saved: {args.output}")
elif args.mode == 'baseline':
all_results = []
for bs in batch_sizes:
for sl in seq_lens:
print(f"\n Benchmarking baseline model: bs={bs}, seq={sl}")
r = benchmark_baseline_model(
batch_size=bs, seq_len=sl,
hidden_size=args.hidden_size,
num_heads=args.num_heads,
H_cycles=args.H_cycles, L_cycles=args.L_cycles,
H_layers=args.H_layers, L_layers=args.L_layers,
warmup=args.warmup, iterations=args.iterations,
device=device,
)
all_results.append(r)
print_results_table(all_results)
if args.output:
with open(args.output, 'w') as f:
json.dump([asdict(r) for r in all_results], f, indent=2, default=str)
print(f" Results saved: {args.output}")
print("\n Done!\n")
if __name__ == '__main__':
main()
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