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#!/usr/bin/env python3
"""
Helion-2.5-Rnd Utility Functions
Common utilities for model inference and processing
"""
import json
import logging
import os
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import yaml
from transformers import AutoTokenizer
logger = logging.getLogger(__name__)
class ModelConfig:
"""Model configuration manager"""
def __init__(self, config_path: str = "model_config.yaml"):
"""Load configuration from YAML file"""
self.config_path = Path(config_path)
self.config = self._load_config()
def _load_config(self) -> Dict[str, Any]:
"""Load YAML configuration"""
if not self.config_path.exists():
logger.warning(f"Config file not found: {self.config_path}")
return self._default_config()
with open(self.config_path, 'r') as f:
config = yaml.safe_load(f)
logger.info(f"Loaded configuration from {self.config_path}")
return config
def _default_config(self) -> Dict[str, Any]:
"""Return default configuration"""
return {
"model": {
"name": "DeepXR/Helion-2.5-Rnd",
"max_position_embeddings": 131072,
},
"inference": {
"default_parameters": {
"temperature": 0.7,
"top_p": 0.9,
"max_new_tokens": 4096,
}
}
}
def get(self, key: str, default: Any = None) -> Any:
"""Get configuration value by dot-separated key"""
keys = key.split('.')
value = self.config
for k in keys:
if isinstance(value, dict):
value = value.get(k)
if value is None:
return default
else:
return default
return value
class TokenCounter:
"""Token counting utilities"""
def __init__(self, model_name: str = "meta-llama/Meta-Llama-3.1-70B"):
"""Initialize tokenizer for counting"""
try:
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
except Exception as e:
logger.warning(f"Failed to load tokenizer: {e}")
self.tokenizer = None
def count_tokens(self, text: str) -> int:
"""Count tokens in text"""
if self.tokenizer is None:
# Rough estimate: ~4 characters per token
return len(text) // 4
return len(self.tokenizer.encode(text))
def count_messages_tokens(self, messages: List[Dict[str, str]]) -> int:
"""Count tokens in message list"""
total = 0
for msg in messages:
# Add tokens for role and content
total += self.count_tokens(msg.get('role', ''))
total += self.count_tokens(msg.get('content', ''))
# Add overhead for formatting
total += 4
return total
def truncate_to_tokens(
self,
text: str,
max_tokens: int,
from_end: bool = False
) -> str:
"""Truncate text to maximum token count"""
if self.tokenizer is None:
# Character-based truncation
max_chars = max_tokens * 4
if from_end:
return text[-max_chars:]
return text[:max_chars]
tokens = self.tokenizer.encode(text)
if len(tokens) <= max_tokens:
return text
if from_end:
truncated_tokens = tokens[-max_tokens:]
else:
truncated_tokens = tokens[:max_tokens]
return self.tokenizer.decode(truncated_tokens)
class PromptTemplate:
"""Prompt templating utilities"""
TEMPLATES = {
"chat": (
"{% for message in messages %}"
"<|im_start|>{{ message.role }}\n{{ message.content }}<|im_end|>\n"
"{% endfor %}"
"<|im_start|>assistant\n"
),
"instruction": (
"### Instruction:\n{instruction}\n\n"
"### Response:\n"
),
"qa": (
"Question: {question}\n\n"
"Answer: "
),
"code": (
"# Task: {task}\n\n"
"```{language}\n"
),
"analysis": (
"Analyze the following:\n\n{content}\n\n"
"Analysis:"
)
}
@classmethod
def format(cls, template_name: str, **kwargs) -> str:
"""Format a template with given arguments"""
template = cls.TEMPLATES.get(template_name)
if template is None:
raise ValueError(f"Unknown template: {template_name}")
# Simple string formatting
try:
return template.format(**kwargs)
except KeyError as e:
raise ValueError(f"Missing required argument: {e}")
@classmethod
def format_chat(cls, messages: List[Dict[str, str]]) -> str:
"""Format chat messages into prompt"""
formatted = ""
for msg in messages:
role = msg.get('role', 'user')
content = msg.get('content', '')
formatted += f"<|im_start|>{role}\n{content}<|im_end|>\n"
formatted += "<|im_start|>assistant\n"
return formatted
class ResponseParser:
"""Parse and validate model responses"""
@staticmethod
def extract_code(response: str, language: Optional[str] = None) -> str:
"""Extract code from markdown code blocks"""
import re
if language:
pattern = f"```{language}\n(.*?)```"
else:
pattern = r"```(?:\w+)?\n(.*?)```"
matches = re.findall(pattern, response, re.DOTALL)
if matches:
return matches[0].strip()
# No code blocks found, return as is
return response.strip()
@staticmethod
def extract_json(response: str) -> Optional[Dict]:
"""Extract and parse JSON from response"""
import re
# Try to find JSON in code blocks
json_pattern = r"```json\n(.*?)```"
matches = re.findall(json_pattern, response, re.DOTALL)
if matches:
try:
return json.loads(matches[0])
except json.JSONDecodeError:
pass
# Try to parse entire response as JSON
try:
return json.loads(response)
except json.JSONDecodeError:
return None
@staticmethod
def split_sections(response: str) -> Dict[str, str]:
"""Split response into sections based on headers"""
import re
sections = {}
current_section = "main"
current_content = []
for line in response.split('\n'):
# Check for markdown headers
header_match = re.match(r'^#{1,3}\s+(.+)$', line)
if header_match:
# Save previous section
if current_content:
sections[current_section] = '\n'.join(current_content).strip()
# Start new section
current_section = header_match.group(1).lower().replace(' ', '_')
current_content = []
else:
current_content.append(line)
# Save last section
if current_content:
sections[current_section] = '\n'.join(current_content).strip()
return sections
class PerformanceMonitor:
"""Monitor inference performance"""
def __init__(self):
self.requests = []
self.start_time = time.time()
def record_request(
self,
duration: float,
input_tokens: int,
output_tokens: int,
success: bool = True
):
"""Record a request"""
self.requests.append({
'timestamp': time.time(),
'duration': duration,
'input_tokens': input_tokens,
'output_tokens': output_tokens,
'success': success,
'tokens_per_second': output_tokens / duration if duration > 0 else 0
})
def get_stats(self) -> Dict[str, Any]:
"""Get performance statistics"""
if not self.requests:
return {
'total_requests': 0,
'uptime_seconds': time.time() - self.start_time
}
successful = [r for r in self.requests if r['success']]
return {
'total_requests': len(self.requests),
'successful_requests': len(successful),
'failed_requests': len(self.requests) - len(successful),
'uptime_seconds': time.time() - self.start_time,
'avg_duration': sum(r['duration'] for r in successful) / len(successful),
'avg_tokens_per_second': sum(r['tokens_per_second'] for r in successful) / len(successful),
'total_input_tokens': sum(r['input_tokens'] for r in self.requests),
'total_output_tokens': sum(r['output_tokens'] for r in self.requests),
}
def reset(self):
"""Reset statistics"""
self.requests = []
self.start_time = time.time()
class SafetyFilter:
"""Basic safety filtering for outputs"""
UNSAFE_PATTERNS = [
r'\b(kill|murder|suicide)\s+(?:yourself|myself)',
r'\b(bomb|weapon)\s+(?:making|instructions)',
r'\bhate\s+speech\b',
]
@classmethod
def is_safe(cls, text: str) -> Tuple[bool, Optional[str]]:
"""
Check if text is safe
Returns:
(is_safe, reason)
"""
import re
text_lower = text.lower()
for pattern in cls.UNSAFE_PATTERNS:
if re.search(pattern, text_lower):
return False, f"Matched unsafe pattern: {pattern}"
return True, None
@classmethod
def filter_response(cls, text: str, replacement: str = "[FILTERED]") -> str:
"""Filter unsafe content from response"""
is_safe, reason = cls.is_safe(text)
if not is_safe:
logger.warning(f"Filtered unsafe content: {reason}")
return replacement
return text
def get_gpu_info() -> Dict[str, Any]:
"""Get GPU information"""
if not torch.cuda.is_available():
return {"available": False}
info = {
"available": True,
"count": torch.cuda.device_count(),
"devices": []
}
for i in range(torch.cuda.device_count()):
device_info = {
"id": i,
"name": torch.cuda.get_device_name(i),
"memory_total": torch.cuda.get_device_properties(i).total_memory,
"memory_allocated": torch.cuda.memory_allocated(i),
"memory_reserved": torch.cuda.memory_reserved(i),
}
info["devices"].append(device_info)
return info
def format_bytes(bytes_value: int) -> str:
"""Format bytes to human-readable string"""
for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
if bytes_value < 1024.0:
return f"{bytes_value:.2f} {unit}"
bytes_value /= 1024.0
return f"{bytes_value:.2f} PB" |