feat: Handle max trials per goal and theorem formatting

examples_search/miniF2F_search.py

@@ -3,7 +3,7 @@
 import subprocess, json, argparse
 from typing import Optional
 from pathlib import Path
-from pantograph.server import Server
+from pantograph.server import Server, ServerError
 from pantograph.search import SearchResult
 from pantograph.search_llm import LLMAgent
 
@@ -17,22 +17,47 @@ def read_test_data(use_valid: bool):
     with open(jsonl_path, 'r') as f:
         return [json.loads(l) for l in list(f)]
 
-def try_test_data(server, agent, entry: dict, max_steps: int) -> Optional[SearchResult]:
+def inplace_to_statement(expr: str) -> str:
+    bracket = 0
+    i = 0
+    while i < len(expr):
+        if expr[i] == ':' and bracket == 0:
+            break
+        elif expr[i] == '(':
+            bracket += 1
+        elif expr[i] == ')':
+            bracket -= 1
+        i += 1
+    if i == 0:
+        return expr[1:]
+    if i == len(expr):
+        return expr
+
+    return 'forall ' + expr[:i] + ' , ' + expr[i+1:]
+
+
+def try_test_data(server, agent, entry: dict, max_steps: int, max_trials_per_goal: int) -> Optional[SearchResult]:
     e = entry["formal_statement"]
+    print(e)
     informal_stmt = entry["informal_stmt"]
     informal_proof = entry["informal_proof"]
 
     key_position = e.find('theorem')
-    if key_position == -1:
+    if key_position != 0:
         # Can't output anything for this one
         return None
     e = e[key_position:]
+    # remove the tail := sorry
+    e, tail = e.rsplit(':=', 1)
+    # remove the head
     key_theorem, name, e = e.split(' ', 2)
-    e, tail = e.split(':=', 1)
+    target = inplace_to_statement(e.strip())
-    target = "forall " + ','.join(e.rsplit(':', 1))
     print(f"Target: {target}")
-    agent = LLMAgent(server)
+    try:
-    return agent.search(server=server, target=target, informal_stmt = informal_stmt, informal_proof = informal_proof,verbose=True, max_steps=max_steps)
+        return agent.search(server=server, target=target, informal_stmt = informal_stmt, informal_proof = informal_proof,verbose=True,
+                            max_steps=max_steps, max_trials_per_goal=max_trials_per_goal)
+    except ServerError as e:
+        return None
 
 def output_file_name(datum, use_hammer: bool, use_llm: bool):
     name = datum["id"]
@@ -53,6 +78,7 @@ if __name__ == '__main__':
     parser.add_argument('--validation', action='store_true')
     parser.add_argument('--use-llm', action='store_true')
     parser.add_argument('-s', '--max-steps', default=200)
+    parser.add_argument('-t', '--max-trials-per-goal', default=4)
     args = parser.parse_args()
 
     project_path, lean_path = get_project_and_lean_path()
@@ -60,17 +86,20 @@ if __name__ == '__main__':
     print(f"$LEAN_PATH: {lean_path}")
 
     test_data = read_test_data(args.validation)
-    server = Server(imports=["Mathlib"], project_path=project_path, lean_path=lean_path)
-    agent = LLMAgent(server, use_hammer=args.use_hammer, use_llm=args.use_llm)
     for datum in test_data:
         file_name = output_file_name(datum, args.use_hammer, args.use_llm)
+        placeholder_file_name = file_name.with_suffix('.placeholder')
         if file_name.is_file():
             print(f"Skipping {datum['id']}")
             continue
-        result = try_test_data(server, agent, datum, max_steps=args.max_steps)
+        server = Server(imports=["Example"], project_path=project_path, lean_path=lean_path, options=["maxHeartbeats=0"])
+        agent = LLMAgent(server, use_hammer=args.use_hammer, use_llm=args.use_llm)
+        result = try_test_data(server, agent, datum, max_steps=args.max_steps, max_trials_per_goal=args.max_trials_per_goal)
         if result is None:
-            with open(file_name + '-placeholder', 'w') as f:
+            with open(placeholder_file_name, 'w') as f:
                 json.dump({ 'id': datum['id'] }, f)
         else:
+            if placeholder_file_name.is_file():
+                placeholder_file_name.unlink()
             with open(file_name, 'w') as f:
                 json.dump({ 'id': datum['id'], 'success': result.success, 'steps': result.steps  }, f)

pantograph/search.py

@@ -63,7 +63,7 @@ class Agent:
                informal_stmt: str = "",
                informal_proof: str = "",
                max_steps: int = 100,
-               max_trial_per_goal: int = 5,
+               max_trials_per_goal: int = 5,
                verbose: bool = False) -> SearchResult:
 
         search_stack = [SearchState(state=server.goal_start(target),
@@ -99,7 +99,7 @@ class Agent:
             # Find the unsolved goal with the highest priority
             goal_id = search_state.next_goal_id
 
-            if search_state.trials[goal_id] > max_trial_per_goal:
+            if search_state.trials[goal_id] > max_trials_per_goal:
                 # force halt the search
                 tactic = None
             else:
@@ -118,6 +118,7 @@ class Agent:
                 continue
 
             try:
+                search_state.trials[goal_id] += 1
                 state = search_state.state
                 if verbose:
                     print(f"{state.state_id}.{goal_id}: {tactic} on {search_state.state.goals[goal_id]}")

pantograph/search_llm.py

@@ -13,6 +13,8 @@ class LLMAgent(Agent):
         super().__init__()
         self.n_trials = 5
         self.server = server
+
+        if use_llm:
             sgl.set_default_backend(sgl.OpenAI("gpt-4"))
 
         self.goal_tactic_id_map = collections.defaultdict(lambda : 0)
@@ -37,6 +39,7 @@ class LLMAgent(Agent):
         if i >= len(self.tactics) and not self.use_llm:
             return None
         elif i >= len(self.tactics):
+            assert self.use_llm
             new_state = None
             for ii in range(self.n_trials):
                 print(f"===============trail {str(ii)}============")