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#!/usr/bin/env python3
"""
Helion-2.5-Rnd Benchmark Runner
Comprehensive benchmarking suite for performance testing
"""
import argparse
import json
import logging
import statistics
import time
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from inference.client import HelionClient
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class BenchmarkRunner:
"""Run comprehensive benchmarks on Helion model"""
def __init__(
self,
base_url: str = "http://localhost:8000",
output_dir: str = "./benchmark_results"
):
"""
Initialize benchmark runner
Args:
base_url: API base URL
output_dir: Directory for results
"""
self.client = HelionClient(base_url=base_url)
self.output_dir = Path(output_dir)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.results = {
'timestamp': datetime.now().isoformat(),
'base_url': base_url,
'tests': {}
}
def benchmark_latency(
self,
num_requests: int = 100,
prompt_lengths: List[int] = [128, 512, 2048],
max_tokens: int = 256
) -> Dict:
"""
Benchmark inference latency
Args:
num_requests: Number of requests per test
prompt_lengths: Different prompt lengths to test
max_tokens: Maximum tokens to generate
Returns:
Latency benchmark results
"""
logger.info("Running latency benchmark...")
results = {}
for prompt_len in prompt_lengths:
logger.info(f"Testing prompt length: {prompt_len}")
# Generate test prompt
test_prompt = "Hello world. " * (prompt_len // 13)
latencies = []
first_token_latencies = []
for i in range(num_requests):
try:
start_time = time.time()
response = self.client.complete(
prompt=test_prompt,
max_tokens=max_tokens,
temperature=0.7,
stream=False
)
end_time = time.time()
latency = (end_time - start_time) * 1000 # Convert to ms
latencies.append(latency)
if i % 10 == 0:
logger.info(f" Progress: {i+1}/{num_requests}")
except Exception as e:
logger.error(f"Request failed: {e}")
if latencies:
results[f"prompt_{prompt_len}"] = {
'num_samples': len(latencies),
'mean_ms': statistics.mean(latencies),
'median_ms': statistics.median(latencies),
'std_dev_ms': statistics.stdev(latencies) if len(latencies) > 1 else 0,
'min_ms': min(latencies),
'max_ms': max(latencies),
'p50_ms': np.percentile(latencies, 50),
'p90_ms': np.percentile(latencies, 90),
'p95_ms': np.percentile(latencies, 95),
'p99_ms': np.percentile(latencies, 99)
}
return results
def benchmark_throughput(
self,
duration_seconds: int = 60,
concurrent_requests: int = 10,
prompt_length: int = 512,
max_tokens: int = 128
) -> Dict:
"""
Benchmark throughput with concurrent requests
Args:
duration_seconds: How long to run test
concurrent_requests: Number of concurrent requests
prompt_length: Prompt length for testing
max_tokens: Maximum tokens to generate
Returns:
Throughput benchmark results
"""
logger.info(f"Running throughput benchmark for {duration_seconds}s...")
test_prompt = "The quick brown fox jumps over the lazy dog. " * (prompt_length // 45)
start_time = time.time()
end_time = start_time + duration_seconds
completed_requests = 0
failed_requests = 0
total_tokens = 0
latencies = []
def make_request():
try:
req_start = time.time()
response = self.client.complete(
prompt=test_prompt,
max_tokens=max_tokens,
temperature=0.7
)
req_end = time.time()
return {
'success': True,
'latency': req_end - req_start,
'tokens': len(response.split()) # Approximate
}
except Exception as e:
return {'success': False, 'error': str(e)}
with ThreadPoolExecutor(max_workers=concurrent_requests) as executor:
while time.time() < end_time:
futures = [executor.submit(make_request) for _ in range(concurrent_requests)]
for future in as_completed(futures):
result = future.result()
if result['success']:
completed_requests += 1
latencies.append(result['latency'] * 1000)
total_tokens += result.get('tokens', 0)
else:
failed_requests += 1
actual_duration = time.time() - start_time
return {
'duration_seconds': actual_duration,
'concurrent_requests': concurrent_requests,
'completed_requests': completed_requests,
'failed_requests': failed_requests,
'requests_per_second': completed_requests / actual_duration,
'total_tokens': total_tokens,
'tokens_per_second': total_tokens / actual_duration,
'avg_latency_ms': statistics.mean(latencies) if latencies else 0,
'p95_latency_ms': np.percentile(latencies, 95) if latencies else 0
}
def benchmark_context_length(
self,
context_lengths: List[int] = [1024, 4096, 16384, 65536],
num_samples: int = 10
) -> Dict:
"""
Benchmark performance across different context lengths
Args:
context_lengths: List of context lengths to test
num_samples: Number of samples per length
Returns:
Context length benchmark results
"""
logger.info("Running context length benchmark...")
results = {}
for ctx_len in context_lengths:
logger.info(f"Testing context length: {ctx_len}")
# Generate long context
base_text = "This is a test sentence for context length benchmarking. "
long_prompt = base_text * (ctx_len // len(base_text))
long_prompt = long_prompt[:ctx_len] + "\n\nSummarize the above text:"
latencies = []
for i in range(num_samples):
try:
start_time = time.time()
response = self.client.complete(
prompt=long_prompt,
max_tokens=256,
temperature=0.5
)
end_time = time.time()
latencies.append((end_time - start_time) * 1000)
except Exception as e:
logger.error(f"Context length {ctx_len} failed: {e}")
if latencies:
results[f"context_{ctx_len}"] = {
'mean_latency_ms': statistics.mean(latencies),
'median_latency_ms': statistics.median(latencies),
'std_dev_ms': statistics.stdev(latencies) if len(latencies) > 1 else 0
}
return results
def benchmark_generation_quality(
self,
test_prompts: Optional[List[str]] = None,
num_samples: int = 5
) -> Dict:
"""
Benchmark generation quality with diverse prompts
Args:
test_prompts: Custom test prompts
num_samples: Number of samples per prompt type
Returns:
Quality benchmark results
"""
logger.info("Running generation quality benchmark...")
if test_prompts is None:
test_prompts = [
"Explain quantum computing in simple terms:",
"Write a Python function to calculate fibonacci numbers:",
"Translate 'Hello, how are you?' to Spanish, French, and German:",
"Solve: If x + 5 = 12, what is x?",
"Write a haiku about artificial intelligence:"
]
results = {}
for i, prompt in enumerate(test_prompts):
logger.info(f"Testing prompt {i+1}/{len(test_prompts)}")
responses = []
for _ in range(num_samples):
try:
response = self.client.complete(
prompt=prompt,
max_tokens=512,
temperature=0.7
)
responses.append(response)
except Exception as e:
logger.error(f"Generation failed: {e}")
if responses:
results[f"prompt_{i+1}"] = {
'prompt': prompt[:50] + "...",
'num_responses': len(responses),
'avg_length': statistics.mean([len(r) for r in responses]),
'sample_response': responses[0][:200] + "..."
}
return results
def run_all_benchmarks(self, quick_mode: bool = False) -> Dict:
"""
Run all benchmark suites
Args:
quick_mode: Run faster with fewer samples
Returns:
Complete benchmark results
"""
logger.info("Starting comprehensive benchmark suite...")
if quick_mode:
logger.info("Running in quick mode (fewer samples)")
# Latency benchmark
logger.info("\n=== Latency Benchmark ===")
self.results['tests']['latency'] = self.benchmark_latency(
num_requests=20 if quick_mode else 100,
prompt_lengths=[128, 512] if quick_mode else [128, 512, 2048]
)
# Throughput benchmark
logger.info("\n=== Throughput Benchmark ===")
self.results['tests']['throughput'] = self.benchmark_throughput(
duration_seconds=30 if quick_mode else 60,
concurrent_requests=5 if quick_mode else 10
)
# Context length benchmark
logger.info("\n=== Context Length Benchmark ===")
self.results['tests']['context_length'] = self.benchmark_context_length(
context_lengths=[1024, 4096] if quick_mode else [1024, 4096, 16384],
num_samples=5 if quick_mode else 10
)
# Generation quality
logger.info("\n=== Generation Quality Benchmark ===")
self.results['tests']['generation_quality'] = self.benchmark_generation_quality(
num_samples=2 if quick_mode else 5
)
return self.results
def save_results(self, filename: Optional[str] = None):
"""
Save benchmark results to file
Args:
filename: Output filename
"""
if filename is None:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"benchmark_{timestamp}.json"
output_path = self.output_dir / filename
with open(output_path, 'w') as f:
json.dump(self.results, f, indent=2)
logger.info(f"Results saved to {output_path}")
def print_summary(self):
"""Print benchmark summary"""
logger.info("\n" + "="*60)
logger.info("BENCHMARK SUMMARY")
logger.info("="*60)
if 'latency' in self.results['tests']:
logger.info("\nLatency Results:")
for prompt_type, metrics in self.results['tests']['latency'].items():
logger.info(f" {prompt_type}:")
logger.info(f" Mean: {metrics['mean_ms']:.2f}ms")
logger.info(f" P95: {metrics['p95_ms']:.2f}ms")
logger.info(f" P99: {metrics['p99_ms']:.2f}ms")
if 'throughput' in self.results['tests']:
logger.info("\nThroughput Results:")
metrics = self.results['tests']['throughput']
logger.info(f" Requests/sec: {metrics['requests_per_second']:.2f}")
logger.info(f" Tokens/sec: {metrics['tokens_per_second']:.2f}")
logger.info(f" Avg Latency: {metrics['avg_latency_ms']:.2f}ms")
logger.info("\n" + "="*60)
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(description="Helion Benchmark Runner")
parser.add_argument("--base-url", type=str, default="http://localhost:8000")
parser.add_argument("--output-dir", type=str, default="./benchmark_results")
parser.add_argument("--quick", action="store_true", help="Run quick benchmark")
parser.add_argument("--test", type=str, choices=['latency', 'throughput', 'context', 'quality', 'all'],
default='all', help="Specific test to run")
args = parser.parse_args()
runner = BenchmarkRunner(
base_url=args.base_url,
output_dir=args.output_dir
)
if args.test == 'all':
results = runner.run_all_benchmarks(quick_mode=args.quick)
elif args.test == 'latency':
results = runner.benchmark_latency(num_requests=20 if args.quick else 100)
elif args.test == 'throughput':
results = runner.benchmark_throughput(duration_seconds=30 if args.quick else 60)
elif args.test == 'context':
results = runner.benchmark_context_length(num_samples=5 if args.quick else 10)
elif args.test == 'quality':
results = runner.benchmark_generation_quality(num_samples=2 if args.quick else 5)
runner.save_results()
runner.print_summary()
if __name__ == "__main__":
main() |