| """ |
| Discrete reward logic for the ChipForge environment. |
| """ |
|
|
| import os |
| import openai |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| api_key = os.environ.get("API_KEY") |
| base_url = os.environ.get("API_BASE_URL") |
| model = os.environ.get("MODEL_NAME") |
|
|
| from .constants import STEP_COST |
|
|
| def eval_tool_reward(status: str, attempts: int) -> float: |
| """ |
| Evaluates raw step reward based on tool pass/fail and attempt count. |
| Pass conditions: run_simulation uses 'pass', run_lint uses 'clean', |
| run_synthesis uses 'pass'. |
| """ |
| if status in ("pass", "clean"): |
| if attempts == 1: |
| return 0.3 |
| elif attempts == 2: |
| return 0.2 |
| else: |
| return 0.1 |
| return -0.5 |
|
|
| def eval_llm_submit(design_code: str, testbench_code: str, golden_code: str, task_desc: str) -> float: |
| """ |
| Uses an LLM judge to evaluate the design and testbench against the goal. |
| Returns a score between -1.0 and 1.0. |
| """ |
| api_key = os.environ.get("OPENAI_API_KEY") |
| if not api_key: |
| print("WARNING: OPENAI_API_KEY not found. Falling back to length-based heuristic.") |
| return 0.5 if (len(design_code.strip()) > 10 and len(testbench_code.strip()) > 10) else -1.0 |
|
|
| client = openai.OpenAI(api_key=api_key , base_url=base_url) |
| |
| system_prompt = ( |
| "You are an expert Verilog evaluation judge. Evaluate the provided design code and testbench against the task description and golden code. " |
| "Return ONLY a single float value between -1.0 and 1.0 representing the quality and correctness of the submission. " |
| "1.0 = Perfect, entirely correct. 0.0 = Partially correct. -1.0 = Completely incorrect. Do not output any other text." |
| ) |
| |
| user_prompt = ( |
| f"Task Description:\n{task_desc}\n\n" |
| f"Golden Reference Code:\n{golden_code}\n\n" |
| f"Submitted Design Code:\n{design_code}\n\n" |
| f"Submitted Testbench Code:\n{testbench_code}" |
| ) |
| |
| try: |
| response = client.chat.completions.create( |
| model=model, |
| messages=[ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": user_prompt} |
| ], |
| temperature=0.0, |
| max_tokens=10 |
| ) |
| score_str = response.choices[0].message.content.strip() |
| score = float(score_str) |
| return max(-1.0, min(1.0, score)) |
| except Exception as e: |
| print(f"LLM Evaluation failed: {e}") |
| |
| if len(design_code.strip()) > 10 and len(testbench_code.strip()) > 10: |
| return 0.5 |
| return -1.0 |
|
|
| def normalize_reward(raw_reward: float) -> float: |
| """ |
| Maps the raw reward into a [0, 1] scale across the action bounds. |
| Theoretical limits: raw [-1.0, 1.0] -> normalized [0.0, 1.0] |
| """ |
| mapped = (raw_reward + 1.0) / 2.0 |
| return max(0.0, min(1.0, mapped)) |
|
|
