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a84640a | 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 | #!/usr/bin/env python3
"""Mobile performance profiler for the Hermes model (pre-conversion).
Measures the PyTorch reference model's throughput and memory at batch size 1
(the mobile serving target) so you can compare presets and sequence lengths
*before* spending time on LiteRT conversion. Numbers here are an upper bound on
device behaviour — the INT4 ``.litertlm`` graph will differ — but the relative
ordering between presets/lengths is a useful proxy.
Reported per sequence length:
* **Prefill tokens/sec** — throughput of the single forward pass over the prompt.
* **Decode tokens/sec** — throughput of incremental single-token generation
(KV-cache reuse), the metric users feel during streaming.
* **Time-to-first-token (ms)** — prefill latency for the prompt.
* **Peak RSS (MB)** — process resident memory via ``psutil``.
* **Peak VRAM (MB)** — ``torch.cuda.max_memory_allocated`` when on GPU.
Example::
python scripts/benchmark.py --preset hermes-270m \
--seq-lens 64 128 256 512 1024 --runs 5
Prints a markdown table and writes ``benchmark_results.json``.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from typing import Dict, List, Optional
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch # noqa: E402
from hermes.config import HermesConfig, get_config # noqa: E402
from hermes.inference import HermesInference # noqa: E402
from hermes.model import build_model # noqa: E402
try:
import psutil # noqa: E402
_HAS_PSUTIL = True
except ImportError: # pragma: no cover - psutil is a declared dependency
_HAS_PSUTIL = False
def _peak_rss_mb() -> Optional[float]:
"""Current process resident set size in MB, or None if psutil is absent."""
if not _HAS_PSUTIL:
return None
return psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
def _reset_vram(device: torch.device) -> None:
if device.type == "cuda":
torch.cuda.reset_peak_memory_stats(device)
torch.cuda.synchronize(device)
def _peak_vram_mb(device: torch.device) -> Optional[float]:
if device.type == "cuda":
return torch.cuda.max_memory_allocated(device) / (1024 * 1024)
return None
@torch.no_grad()
def benchmark_seq_len(
engine: HermesInference,
seq_len: int,
runs: int,
decode_tokens: int,
device: torch.device,
) -> Dict[str, float]:
"""Time prefill + decode for one sequence length, averaged over ``runs``."""
model = engine.model
vocab = engine.config.vocab_size
_reset_vram(device)
prefill_times: List[float] = []
decode_times: List[float] = []
for _ in range(runs):
prompt_ids = torch.randint(0, vocab, (1, seq_len), device=device)
# --- Prefill: single forward over the whole prompt. ---
caches = [None] * len(model.layers)
if device.type == "cuda":
torch.cuda.synchronize(device)
t0 = time.perf_counter()
logits, caches = engine._forward_with_cache(prompt_ids, caches, start_pos=0)
if device.type == "cuda":
torch.cuda.synchronize(device)
prefill_times.append(time.perf_counter() - t0)
# --- Decode: incremental single-token steps reusing the KV cache. ---
pos = seq_len
next_id = logits.argmax(dim=-1, keepdim=True)
if device.type == "cuda":
torch.cuda.synchronize(device)
t0 = time.perf_counter()
for _ in range(decode_tokens):
step_logits, caches = engine._forward_with_cache(next_id, caches, start_pos=pos)
next_id = step_logits.argmax(dim=-1, keepdim=True)
pos += 1
if pos >= engine.config.max_seq_len:
break
if device.type == "cuda":
torch.cuda.synchronize(device)
decode_times.append(time.perf_counter() - t0)
avg_prefill = sum(prefill_times) / len(prefill_times)
avg_decode = sum(decode_times) / len(decode_times)
decoded = min(decode_tokens, max(1, engine.config.max_seq_len - seq_len))
return {
"seq_len": seq_len,
"ttft_ms": avg_prefill * 1000.0,
"prefill_tok_per_s": seq_len / avg_prefill if avg_prefill > 0 else 0.0,
"decode_tok_per_s": decoded / avg_decode if avg_decode > 0 else 0.0,
"peak_rss_mb": _peak_rss_mb() or 0.0,
"peak_vram_mb": _peak_vram_mb(device) or 0.0,
}
class _NullTokenizer:
"""Placeholder tokenizer — benchmark only needs the model, not real text."""
def encode(self, text: str) -> List[int]:
return [1]
def decode(self, ids: List[int]) -> str:
return ""
def render_table(rows: List[Dict[str, float]]) -> str:
"""Format benchmark rows as a markdown table."""
header = (
"| seq_len | TTFT (ms) | prefill tok/s | decode tok/s | "
"peak RSS (MB) | peak VRAM (MB) |"
)
sep = "|---|---|---|---|---|---|"
lines = [header, sep]
for r in rows:
lines.append(
f"| {int(r['seq_len'])} | {r['ttft_ms']:.1f} | "
f"{r['prefill_tok_per_s']:.1f} | {r['decode_tok_per_s']:.1f} | "
f"{r['peak_rss_mb']:.1f} | {r['peak_vram_mb']:.1f} |"
)
return "\n".join(lines)
def run(args: argparse.Namespace) -> int:
device = torch.device(args.device)
config: HermesConfig = get_config(args.preset)
model = build_model(config)
engine = HermesInference(model, _NullTokenizer(), device=device, preset_name=args.preset)
print(engine)
rows: List[Dict[str, float]] = []
for seq_len in args.seq_lens:
if seq_len >= config.max_seq_len:
print(f"Skipping seq_len={seq_len} (>= max_seq_len={config.max_seq_len}).")
continue
print(f"Benchmarking seq_len={seq_len} ...")
rows.append(
benchmark_seq_len(engine, seq_len, args.runs, args.decode_tokens, device)
)
table = render_table(rows)
print("\n" + table + "\n")
results = {
"preset": args.preset,
"device": args.device,
"runs": args.runs,
"decode_tokens": args.decode_tokens,
"param_count": sum(p.numel() for p in model.parameters()),
"psutil_available": _HAS_PSUTIL,
"rows": rows,
}
with open(args.output, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2)
print(f"Saved {args.output}")
return 0
def parse_args(argv=None) -> argparse.Namespace:
p = argparse.ArgumentParser(description="Benchmark Hermes model speed/memory.")
p.add_argument("--preset", default="hermes-270m", choices=["hermes-1b", "hermes-500m", "hermes-270m"])
p.add_argument("--seq-lens", type=int, nargs="+", default=[64, 128, 256, 512, 1024])
p.add_argument("--runs", type=int, default=5, help="Repeats per sequence length.")
p.add_argument("--decode-tokens", type=int, default=32, help="Tokens to time during decode.")
p.add_argument("--device", default="cpu", help="Torch device (cpu, cuda, cuda:0, ...).")
p.add_argument("--output", default="benchmark_results.json")
return p.parse_args(argv)
if __name__ == "__main__":
sys.exit(run(parse_args()))
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