Sai Kumar Taraka
Initial commit: UVM testbench generator with coverage-driven auto-training
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# src/generation/engine.py — Generation engine (orchestrates model inference)
from __future__ import annotations
from typing import Dict, List, Optional
from src.config import DesignSpec, PipelineConfig
from src.models.base_model import GenerationModel
from src.models.template_model import TemplateModel
from src.utils.decorators import timer
class GenerationEngine:
"""Orchestrates TB generation using a 'model' (template or learned)."""
def __init__(self, model: Optional[GenerationModel] = None):
self.model = model or TemplateModel()
@timer
def generate(self, spec: DesignSpec, cfg: PipelineConfig,
extra_seqs: Optional[List[str]] = None) -> Dict[str, str]:
if not self.model.is_trained:
self.model.train([spec])
return self.model.predict(spec, cfg, extra_seqs=extra_seqs)