Frank
55f87fcdce
The 2026-10-09 wire aliases become the service names everywhere: systemd units, serve scripts, env files and FRANK_* variables, deploy-plan/deploy-remote service keys, container names, Prometheus job names + alert rules, Grafana dashboard queries, bats seams, runbook filenames, homepage/AGENTS lore, and historical prose. Rollback tags renamed in lockstep. Residue in the tree: zero. Retired frank-medium/small/npu names are history and stay. Known casualty: scripts/patches/*.patch context lines were rewritten; those retired-era vLLM-fork patches no longer apply byte-exact.
269 lines
9.5 KiB
C++
Executable File
269 lines
9.5 KiB
C++
Executable File
#!/usr/bin/env python3
|
|
"""
|
|
Benchmark n-gram speculative decoding vs no-spec for frank-dragoon.
|
|
|
|
Tests prompt-processing throughput with 16K token prompts (manageable request size).
|
|
Code-heavy prompt (high n-gram redundancy) vs prose prompt (low redundancy).
|
|
|
|
Usage:
|
|
# Generate prompts: python3 scripts/bench-ngram-mod.py generate /tmp/prompts/
|
|
# Run: python3 scripts/bench-ngram-mod.py /tmp/prompts/
|
|
"""
|
|
|
|
import json
|
|
import os
|
|
import sys
|
|
import time
|
|
import subprocess
|
|
import urllib.request
|
|
import urllib.error
|
|
from pathlib import Path
|
|
|
|
MODEL = "/home/dank/models/kingjones-Qwen3.8-Flash-Next-ROCmFP4-STRIX/Qwen3.8-Flash-Next-Q4_0-ROCmFP4-STRIX.gguf"
|
|
CHAT_TEMPLATE = "/home/dank/serverAppConfigs/scripts/chat-template.jinja"
|
|
BUILD_DIR = "/home/dank/GitPackages/ROCmFPX/build-qwen38/bin"
|
|
LLAMA_BIN = os.path.join(BUILD_DIR, "llama-server")
|
|
|
|
# 16K tokens ~= 64KB text
|
|
TARGET_PROMPT_TOKENS = 16000
|
|
TOKEN_CHAR_RATIO = 4
|
|
TARGET_PROMPT_BYTES = TARGET_PROMPT_TOKENS * TOKEN_CHAR_RATIO
|
|
|
|
CONTEXT = 65536
|
|
BATCH = 4096
|
|
UBATCH = 4096
|
|
THREADS = 16
|
|
THREAD_BATCH = 32
|
|
NGRAM_N_MAX = 24
|
|
NGRAM_N_MIN = 4
|
|
TEST_TOKENS = 128
|
|
SERVER_WAIT = 300
|
|
|
|
|
|
def start_server(port, spec_type):
|
|
cmd = [
|
|
LLAMA_BIN,
|
|
"--model", MODEL,
|
|
"--chat-template-file", CHAT_TEMPLATE,
|
|
"--jinja",
|
|
"--device", "ROCm0",
|
|
"--gpu-layers", "999",
|
|
"--parallel", "1",
|
|
"--ctx-size", str(CONTEXT),
|
|
"--batch-size", str(BATCH),
|
|
"--ubatch-size", str(UBATCH),
|
|
"--fit", "off",
|
|
"--flash-attn", "on",
|
|
"--cache-type-k", "f16",
|
|
"--cache-type-v", "f16",
|
|
"--threads", str(THREADS),
|
|
"--threads-batch", str(THREAD_BATCH),
|
|
"--cache-prompt",
|
|
"--cache-idle-slots",
|
|
"--metrics",
|
|
"--host", "127.0.0.1",
|
|
"--port", str(port),
|
|
"--temp", "0",
|
|
]
|
|
if spec_type == "ngram-mod":
|
|
cmd.extend([
|
|
"--spec-type", "ngram-mod",
|
|
"--spec-ngram-mod-n-max", str(NGRAM_N_MAX),
|
|
"--spec-ngram-mod-n-min", str(NGRAM_N_MIN),
|
|
])
|
|
else:
|
|
cmd.extend(["--spec-type", "none"])
|
|
|
|
env = os.environ.copy()
|
|
env["ROCM_PATH"] = "/opt/rocm"
|
|
env["LD_LIBRARY_PATH"] = f"{BUILD_DIR}:/opt/rocm/lib:/opt/rocm/lib/rocm_sysdeps/lib:/opt/rocm/lib/llvm/lib"
|
|
env["HSA_OVERRIDE_GFX_VERSION"] = "11.5.1"
|
|
env["HIP_VISIBLE_DEVICES"] = "0"
|
|
|
|
proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
|
|
print(f" Waiting for server on port {port} (spec={spec_type})...")
|
|
for i in range(SERVER_WAIT):
|
|
try:
|
|
req = urllib.request.urlopen(f"http://127.0.0.1:{port}/health", timeout=2)
|
|
resp = json.loads(req.read().decode())
|
|
if resp.get("status") == "ok":
|
|
print(f" Server ready after {i+1}s")
|
|
return proc
|
|
except:
|
|
time.sleep(1)
|
|
|
|
proc.terminate()
|
|
out, err = proc.communicate()
|
|
print(f"ERROR: Server failed to start in {SERVER_WAIT}s")
|
|
print(err.decode()[-2000:])
|
|
sys.exit(1)
|
|
|
|
|
|
def run_test(prompt_text, spec_type, port):
|
|
"""Send a request and extract prompt processing metrics."""
|
|
data = json.dumps({
|
|
"model": "frank-dragoon",
|
|
"messages": [{"role": "user", "content": prompt_text}],
|
|
"max_tokens": TEST_TOKENS,
|
|
"stream": False,
|
|
"temperature": 0,
|
|
}).encode()
|
|
|
|
req = urllib.request.Request(
|
|
f"http://127.0.0.1:{port}/v1/chat/completions",
|
|
data=data,
|
|
headers={"Content-Type": "application/json"},
|
|
)
|
|
|
|
start = time.monotonic()
|
|
resp = urllib.request.urlopen(req, timeout=600)
|
|
elapsed = time.monotonic() - start
|
|
|
|
result = json.loads(resp.read().decode())
|
|
timings = result.get("timings", {})
|
|
usage = result.get("usage", {})
|
|
|
|
return {
|
|
"prompt_tokens": usage.get("prompt_tokens", 0),
|
|
"prompt_ms": timings.get("prompt_ms", 0),
|
|
"prompt_per_second": timings.get("prompt_per_second", 0),
|
|
"prompt_per_token_ms": timings.get("prompt_per_token_ms", 0),
|
|
"predicted_per_second": timings.get("predicted_per_second", 0),
|
|
"total_time_s": elapsed,
|
|
"cache_n": timings.get("cache_n", 0),
|
|
}
|
|
|
|
|
|
def main():
|
|
if len(sys.argv) < 2:
|
|
print(f"Usage: {sys.argv[0]} <prompt_dir>")
|
|
sys.exit(1)
|
|
|
|
prompt_dir = Path(sys.argv[1])
|
|
prompt_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
code_prompt_file = prompt_dir / "prompt_code.txt"
|
|
prose_prompt_file = prompt_dir / "prompt_prose.txt"
|
|
|
|
# Generate prompts if they don't exist
|
|
if not code_prompt_file.exists() or not prose_prompt_file.exists():
|
|
print("Generating prompts...")
|
|
# Generate deterministic prompts
|
|
# Code prompt: structured algorithms + data generation + config schemas
|
|
code_lines = []
|
|
for i in range(50):
|
|
code_lines.append(f"# Algorithm {i}\n")
|
|
code_lines.append(f"def algo_{i}(x, y):\n")
|
|
code_lines.append(f" \"\"\"Algorithm {i} processing.\"\"\"\n")
|
|
code_lines.append(f" result = 0\n")
|
|
code_lines.append(f" for j in range({i * 10}):\n")
|
|
code_lines.append(f" result += x * y + j\n")
|
|
code_lines.append(f" return result\n\n")
|
|
code_text = "".join(code_lines)
|
|
# Pad/truncate to target
|
|
while len(code_text) < TARGET_PROMPT_BYTES:
|
|
code_text += "# Additional algorithm block\n"
|
|
code_text += f"def extra_{len(code_text)//100}():\n"
|
|
code_text += " x = 1\n"
|
|
code_text += " y = 2\n"
|
|
code_text += " return x + y\n\n"
|
|
code_text = code_text[:TARGET_PROMPT_BYTES]
|
|
|
|
# Prose prompt: historical/scientific/philosophical text
|
|
prose_text = ""
|
|
topics = [
|
|
"The Renaissance brought profound transformation to European civilization. Artists and scholars pushed the boundaries of human expression.",
|
|
"Quantum mechanics describes the behavior of matter and energy at atomic scales. The uncertainty principle limits measurement precision.",
|
|
"Ethical philosophy grapples with questions of right and wrong. Utilitarianism judges actions by their consequences and the happiness they produce.",
|
|
"Mountains rise like ancient sentinels, their peaks lost in clouds that swirl and dance across the sky above the valleys below.",
|
|
"The ocean whispers secrets to the shore, each wave carrying fragments of distant lands across the water before retreating back to sea.",
|
|
]
|
|
for _ in range(500):
|
|
prose_text += f"{topics[len(prose_text) % len(topics)]} "
|
|
prose_text = prose_text[:TARGET_PROMPT_BYTES]
|
|
|
|
code_prompt_file.write_text(code_text)
|
|
prose_prompt_file.write_text(prose_text)
|
|
print(f" Code: {code_prompt_file.stat().st_size:,} bytes")
|
|
print(f" Prose: {prose_prompt_file.stat().st_size:,} bytes")
|
|
|
|
print("=" * 60)
|
|
print(" N-gram Mod Benchmark — frank-dragoon")
|
|
print(f" STRIX ROCmFP4 · 64K context · 16K prompts")
|
|
print("=" * 60)
|
|
|
|
results = []
|
|
port_base = 7772
|
|
port_ngram = 7773
|
|
|
|
tests = [
|
|
("code", "none", port_base, code_prompt_file),
|
|
("prose", "none", port_ngram, prose_prompt_file),
|
|
("code", "ngram-mod", port_base, code_prompt_file),
|
|
("prose", "ngram-mod", port_ngram, prose_prompt_file),
|
|
]
|
|
|
|
for prompt_type, spec_type, port, pfile in tests:
|
|
print(f"\n=== {spec_type} ({prompt_type}) ===")
|
|
print(f" File: {pfile} ({pfile.stat().st_size:,} bytes)")
|
|
print(f" Spec: {spec_type}")
|
|
if spec_type == "ngram-mod":
|
|
print(f" n_max={NGRAM_N_MAX}, n_min={NGRAM_N_MIN}")
|
|
|
|
proc = start_server(port, spec_type)
|
|
|
|
try:
|
|
# Warm-up
|
|
print(" Warm-up...")
|
|
run_test("Hello test", spec_type, port)
|
|
|
|
# Full prompt
|
|
print(f" Sending {TARGET_PROMPT_TOKENS} token prompt...")
|
|
with open(pfile) as f:
|
|
prompt_text = f.read()
|
|
result = run_test(prompt_text, spec_type, port)
|
|
|
|
tps = result["prompt_per_second"]
|
|
print(f" Tokens: {result['prompt_tokens']}")
|
|
print(f" Prompt: {result['prompt_ms']:.0f}ms")
|
|
print(f" TPS: {tps:.1f} tok/s")
|
|
print(f" Total: {result['total_time_s']:.1f}s")
|
|
|
|
results.append({
|
|
"prompt_type": prompt_type,
|
|
"spec_type": spec_type,
|
|
"prompt_tokens": result["prompt_tokens"],
|
|
"prompt_ms": result["prompt_ms"],
|
|
"tps": tps,
|
|
"total_time_s": result["total_time_s"],
|
|
})
|
|
|
|
finally:
|
|
proc.terminate()
|
|
try:
|
|
proc.wait(timeout=5)
|
|
except:
|
|
proc.kill()
|
|
|
|
# Summary
|
|
print("\n" + "=" * 60)
|
|
print(" RESULTS SUMMARY")
|
|
print("=" * 60)
|
|
|
|
for ptype in ["code", "prose"]:
|
|
baseline = next((r for r in results if r["prompt_type"] == ptype and r["spec_type"] == "none"), None)
|
|
ngram = next((r for r in results if r["prompt_type"] == ptype and r["spec_type"] == "ngram-mod"), None)
|
|
if baseline and ngram:
|
|
gain = ((ngram["tps"] - baseline["tps"]) / baseline["tps"]) * 100 if baseline["tps"] > 0 else 0
|
|
print(f"\n{ptype.upper()} PROMPT:")
|
|
print(f" Baseline TPS: {baseline['tps']:.1f} tok/s ({baseline['prompt_ms']:.0f}ms)")
|
|
print(f" N-gram TPS: {ngram['tps']:.1f} tok/s ({ngram['prompt_ms']:.0f}ms)")
|
|
print(f" Speedup: {gain:+.1f}%")
|
|
|
|
print("\n" + "=" * 60)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|