Fix issues with swe-bench

runs/swe-bench.py

@@ -7,7 +7,7 @@ import time
 
 print("Downloading dataset...")
 dataset = load_dataset("princeton-nlp/SWE-bench_Lite", split = "test", cache_dir='datasets_cache')
-preds_path = "llama2-70b-4096.jsonl"
+preds_path = "llama3-70b-8192.jsonl"
 try:
     with open(preds_path, "r") as file:
         preds_jsonl = file.read()
@@ -34,8 +34,8 @@ def save_jsonl(jsonl_object, file_path="preds.jsonl"):
 
 args = argparse.Namespace(
     # backend='mixtral-8x7b-32768',
-    backend='llama2-70b-4096',
+    backend='llama3-70b-8192',
-    temperature=0.5, 
+    temperature=0.7, 
     task='swe', 
     naive_run=False,
     prompt_sample='cot', 
@@ -50,12 +50,16 @@ print("Solving...")
 task = SWETask(dataset)
 
 
-for index in range(6,100):
+for index in range(3,4):
-    ys, infos = solve(args, task, index, to_print=False)
+    instance_id = dataset[index]["instance_id"]
+    size = len(dataset[index]["problem_statement"])
+    print(f" ### Task {index} -- {instance_id} -> size ({size} )###")
+    ys, infos, _ = solve(args, task, index, to_print=False)
     preds_jsonl = update_jsonl(dataset[index]["instance_id"], SWETask.parse_diff_block(ys[0]), args.backend, preds_jsonl)
     save_jsonl(preds_jsonl, preds_path)
-    # print("-----------Predicted----------------------")
+    print("-----------Predicted----------------------")
-    # print(SWETask.parse_diff_block(ys[0]))
+    print(SWETask.parse_diff_block(ys[0]))
-    # # print("-----------Expected----------------------")
+    print("-----------Expected----------------------")
-    # # print(dataset[index]["patch"])
+    print(dataset[index]["patch"])
-    time.sleep(60)
+    # time.sleep(1)
+

src/tot/methods/bfs.py

@@ -1,4 +1,4 @@
-import itertools, os
+import itertools, os, time
 import numpy as np
 from functools import partial
 
@@ -64,6 +64,7 @@ def solve(args, task, idx, to_print=True):
     ys = ['']  # current output candidates
     infos = []
     for step in range(task.steps):
+        # print(f"Step {step} - gen")
         # generation
         if args.method_generate == 'sample':
             new_ys = [get_samples(task, x, y, args.n_generate_sample, prompt_sample=args.prompt_sample, stop=task.stops[step]) for y in ys]
@@ -71,12 +72,13 @@ def solve(args, task, idx, to_print=True):
             new_ys = [get_proposals(task, x, y) for y in ys]
         new_ys = list(itertools.chain(*new_ys))
         ids = list(range(len(new_ys)))
+        time.sleep(5)
         # evaluation
+        # print(f"Step {step} - eval")
         if args.method_evaluate == 'vote':
             values = get_votes(task, x, new_ys, args.n_evaluate_sample)
         elif args.method_evaluate == 'value':
             values = get_values(task, x, new_ys, args.n_evaluate_sample)
-
         # selection
         if args.method_select == 'sample':
             ps = np.array(values) / sum(values)
@@ -92,12 +94,14 @@ def solve(args, task, idx, to_print=True):
         
         infos.append({'step': step, 'x': x, 'ys': ys, 'new_ys': new_ys, 'values': values, 'select_new_ys': select_new_ys})
         ys = select_new_ys
+        if step == 0:
+            time.sleep(5)
 
-    print(usage())
+    total_usage =  usage()
     
     if to_print: 
         print(ys)
-    return ys, {'steps': infos}
+    return ys, {'steps': infos}, total_usage
 
 def naive_solve(args, task, idx, to_print=True):
     global platform

src/tot/models.py

@@ -1,4 +1,4 @@
-import os
+import os, time
 import openai
 import backoff
 
@@ -37,6 +37,7 @@ def chatgpt(messages, model="gpt-4", temperature=0.7, max_tokens=1000, n=1, stop
         # log completion tokens
         completion_tokens += res["usage"]["completion_tokens"]
         prompt_tokens += res["usage"]["prompt_tokens"]
+        time.sleep(2)
     return outputs
     
 def gpt_usage(backend="gpt-4"):
@@ -45,14 +46,16 @@ def gpt_usage(backend="gpt-4"):
         cost = completion_tokens / 1000 * 0.06 + prompt_tokens / 1000 * 0.03
     elif backend == "gpt-3.5-turbo":
         cost = completion_tokens / 1000 * 0.002 + prompt_tokens / 1000 * 0.0015
+    elif backend == "gpt-4-turbo":
+        cost = completion_tokens / 1000 * 0.03 + prompt_tokens / 1000 * 0.01
     return {"completion_tokens": completion_tokens, "prompt_tokens": prompt_tokens, "cost": cost}
 
-def groq(prompt, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=1500, n=1, stop=None) -> list:
+def groq(prompt, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=1000, n=1, stop=None) -> list:
     global completion_tokens, prompt_tokens
     messages = [{"role": "user", "content": prompt}]
     return groqgpt(messages, model=model, temperature=temperature, max_tokens=max_tokens, n=n, stop=stop)
 
-def groqgpt(messages, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=2000,n=1, stop=None) -> list:
+def groqgpt(messages, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=1000,n=1, stop=None) -> list:
     global completion_tokens, prompt_tokens
     outputs = []
     while n > 0:
@@ -62,6 +65,7 @@ def groqgpt(messages, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=20
         # log completion tokens
         completion_tokens += res["usage"]["completion_tokens"]
         prompt_tokens += res["usage"]["prompt_tokens"]
+        time.sleep(2)
     return outputs
 
 def groq_usage():

src/tot/tasks/swe.py

@@ -20,13 +20,14 @@ class SWETask(Task):
         super().__init__()
         self.data = dataset
         self.steps = 2
-        self.stops = ['\nPatch:\n', None]
+        self.stops = ['\nPatch:\n', '<|eot_id|>']
 
     def __len__(self) -> int:
         return len(self.data)
     
     def get_input(self, idx: int) -> str:
         return instance_info.format(repo=self.data[idx]['repo'], base_commit=self.data[idx]['base_commit'], problem_statement=self.data[idx]['problem_statement'])
+        # return instance_info.format(repo=self.data[idx]['repo'], base_commit=self.data[idx]['base_commit'], problem_statement=self.data[idx]['text'])
     
     def test_output(self, idx: int, output: str):
         output = output.split('Patch:\n')[-1]
@@ -35,7 +36,7 @@ class SWETask(Task):
         if api_base == 'https://api.groq.com/openai/v1':
             score_output = groq(prompt, n=5, model='mixtral-8x7b-32768')
         else:
-            score_outputs = gpt(prompt, n=5, model='gpt-4')
+            score_outputs = gpt(prompt, n=5, model='gpt-4-turbo')
         scores = []
         for score_output in score_outputs:
             print("score_output: ",score_output)