Spaces:
Running
Running
File size: 7,312 Bytes
bf9e424 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | """Local-only inference runner for Ollama-backed MolForge testing.
This script is intentionally separate from `inference.py`.
Use `inference.py` for the judge-facing OpenAI-client baseline required by the
hackathon. Use this file for local development against Ollama's native API,
where reasoning models often behave better when `think` is explicitly disabled.
"""
from __future__ import annotations
import json
import os
from typing import Any, Dict, Optional, Tuple
import requests
from inference_common import (
COMPACT_SYSTEM_PROMPT,
SYSTEM_PROMPT,
build_model_payload,
extract_json,
)
try:
from molforge.models import MolForgeAction, MolForgeObservation
from molforge.server.molforge_environment import MolForgeEnvironment
except ImportError:
from models import MolForgeAction, MolForgeObservation
from server.molforge_environment import MolForgeEnvironment
OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
LOCAL_MODEL_NAME = os.getenv("LOCAL_MODEL_NAME", "gemma4:e2b")
LOCAL_NUM_EPISODES = int(os.getenv("LOCAL_NUM_EPISODES", "3"))
LOCAL_MAX_TURNS = int(os.getenv("LOCAL_MAX_TURNS", "10"))
OLLAMA_TIMEOUT_S = float(os.getenv("OLLAMA_TIMEOUT_S", "240"))
OLLAMA_RETRY_TIMEOUT_S = float(os.getenv("OLLAMA_RETRY_TIMEOUT_S", "120"))
OLLAMA_MAX_TOKENS = int(os.getenv("OLLAMA_MAX_TOKENS", "768"))
OLLAMA_THINK = os.getenv("OLLAMA_THINK", "false").lower() == "true"
def main() -> None:
env = MolForgeEnvironment()
scores = []
submission_scores = []
progress_scores = []
print(f"Using Ollama model: {LOCAL_MODEL_NAME}", flush=True)
print(f"Ollama base URL: {OLLAMA_BASE_URL}", flush=True)
print(f"Thinking enabled: {OLLAMA_THINK}", flush=True)
for episode_index in range(LOCAL_NUM_EPISODES):
observation = env.reset()
print(f"\n=== Episode {episode_index + 1}: {observation.scenario_id} ===", flush=True)
for _ in range(LOCAL_MAX_TURNS):
if observation.done:
break
action, source = choose_local_action(observation)
observation = env.step(action)
print(
f"step={observation.step_index:02d} action={action.action_type} actor={action.acting_role} "
f"source={source} reward={observation.reward:+.3f} budget={observation.remaining_budget} "
f"governance={observation.governance.status}",
flush=True,
)
print(f" {observation.last_transition_summary}", flush=True)
if observation.done:
break
grader_scores = observation.metadata.get("terminal_grader_scores", {})
final_score = float(grader_scores.get("final_score", grader_scores.get("submission_score", 0.0)))
submission_score = float(grader_scores.get("submission_score", 0.0))
progress_score = float(grader_scores.get("progress_score", 0.0))
scores.append(final_score)
submission_scores.append(submission_score)
progress_scores.append(progress_score)
print(f"final_score={final_score:.3f}", flush=True)
print(f"submission_score={submission_score:.3f}", flush=True)
print(f"progress_score={progress_score:.3f}", flush=True)
if observation.report_card:
print(observation.report_card, flush=True)
average = sum(scores) / len(scores)
average_progress = sum(progress_scores) / len(progress_scores)
print("\n=== Local Baseline Summary ===", flush=True)
print(
json.dumps(
{
"model": LOCAL_MODEL_NAME,
"scores": scores,
"average_final_score": round(average, 4),
"submission_scores": submission_scores,
"average_submission_score": round(sum(submission_scores) / len(submission_scores), 4),
"progress_scores": progress_scores,
"average_progress_score": round(average_progress, 4),
},
indent=2,
),
flush=True,
)
def choose_local_action(observation: MolForgeObservation) -> Tuple[MolForgeAction, str]:
"""Use Ollama output and fail loudly if it cannot produce a valid action."""
action, error = ask_ollama_model(observation)
if action is not None:
return action, "model"
raise RuntimeError(f"Local model action failed: {error}")
def ask_ollama_model(observation: MolForgeObservation) -> Tuple[Optional[MolForgeAction], str]:
"""Call Ollama's native chat API.
Official Ollama docs note that reasoning traces live in `message.thinking`
while the final answer lives in `message.content`, and that `think: false`
can disable thinking on the native chat endpoint.
"""
errors = []
try:
payload = build_model_payload(observation, compact=False)
response_json = ollama_chat(
system_prompt=SYSTEM_PROMPT,
user_payload=payload,
timeout_s=OLLAMA_TIMEOUT_S,
)
data = parse_ollama_json_response(response_json)
return MolForgeAction(**data), ""
except Exception as exc:
errors.append(f"full_prompt:{exc.__class__.__name__}:{exc}")
try:
payload = build_model_payload(observation, compact=True)
response_json = ollama_chat(
system_prompt=COMPACT_SYSTEM_PROMPT,
user_payload=payload,
timeout_s=OLLAMA_RETRY_TIMEOUT_S,
)
data = parse_ollama_json_response(response_json)
return MolForgeAction(**data), ""
except Exception as retry_exc:
errors.append(f"compact_prompt:{retry_exc.__class__.__name__}:{retry_exc}")
return None, " | ".join(errors)
def ollama_chat(
*,
system_prompt: str,
user_payload: Dict[str, Any],
timeout_s: float,
) -> Dict[str, Any]:
"""Issue a native Ollama chat request."""
response = requests.post(
f"{OLLAMA_BASE_URL.rstrip('/')}/api/chat",
json={
"model": LOCAL_MODEL_NAME,
"stream": False,
"think": OLLAMA_THINK,
"format": "json",
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": json.dumps(user_payload, indent=2)},
],
"options": {
"temperature": 0,
"num_predict": OLLAMA_MAX_TOKENS,
},
},
timeout=timeout_s,
)
response.raise_for_status()
return response.json()
def parse_ollama_json_response(response_json: Dict[str, Any]) -> Dict[str, Any]:
"""Extract a JSON action from a native Ollama response."""
message = response_json.get("message", {}) or {}
content = message.get("content", "") or ""
thinking = message.get("thinking", "") or ""
if content:
try:
return extract_json(content)
except Exception:
pass
if thinking:
try:
return extract_json(thinking)
except Exception:
pass
combined = f"{content}\n{thinking}".strip()
if combined:
return extract_json(combined)
raise ValueError("No parseable JSON action found in Ollama response")
if __name__ == "__main__":
main()
|