49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
"""Phase 19: Hardware-Aware Inference Optimization.
|
|
|
|
Auto-tunes models for specific user hardware (M4 Max, GPUs, etc.) to ensure local-first performance.
|
|
"""
|
|
|
|
import logging
|
|
import json
|
|
from typing import List, Dict, Any
|
|
from agent.gemini_adapter import GeminiAdapter
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
class HardwareOptimizer:
|
|
def __init__(self):
|
|
self.adapter = GeminiAdapter()
|
|
|
|
def optimize_for_hardware(self, hardware_specs: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generates optimization parameters for specific hardware."""
|
|
logger.info(f"Optimizing inference for hardware: {hardware_specs.get('model', 'unknown')}")
|
|
|
|
prompt = f"""
|
|
Hardware Specifications:
|
|
{json.dumps(hardware_specs, indent=2)}
|
|
|
|
Please perform a 'Deep Optimization' analysis for this hardware.
|
|
Identify the best quantization levels, KV cache settings, and batch sizes for local-first inference.
|
|
Generate a 'Hardware-Aware Configuration' and a set of 'Performance Tuning Directives'.
|
|
|
|
Format the output as JSON:
|
|
{{
|
|
"hardware_profile": "...",
|
|
"quantization_strategy": "...",
|
|
"kv_cache_config": {{...}},
|
|
"batch_size_optimization": "...",
|
|
"performance_tuning_directives": [...],
|
|
"projected_latency_improvement": "..."
|
|
}}
|
|
"""
|
|
result = self.adapter.generate(
|
|
model="gemini-3.1-pro-preview",
|
|
prompt=prompt,
|
|
system_instruction="You are Timmy's Hardware Optimizer. Your goal is to ensure Timmy runs at SOTA performance on any local hardware.",
|
|
thinking=True,
|
|
response_mime_type="application/json"
|
|
)
|
|
|
|
optimization_data = json.loads(result["text"])
|
|
return optimization_data
|