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Commit b6edc328 authored by chenzk's avatar chenzk
Browse files

v1.0

parents
Pipeline #2229 canceled with stages
import numpy as np
from concurrent.futures import ProcessPoolExecutor
import tqdm
from lcb_runner.evaluation.utils_execute import BASE_IMPORTS, check_correctness
def evaluate_score(args) -> list[bool]:
gs, (c, i, o) = args
execution_results = []
for g in gs:
if i in g:
pass
else:
code_to_execute = f"{BASE_IMPORTS}\n{c}\nassert {o} == {g}"
execution_results.append(check_correctness(code_to_execute, 3))
if len(execution_results) == 0:
execution_results = [False] * len(gs)
return execution_results
def pass_at_k(n, c, k):
if n - c < k: return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
def code_execution_metrics(
samples,
generations,
):
# execute the code
references = [(doc["code"], doc["input"], doc["output"]) for doc in samples]
with ProcessPoolExecutor() as executor:
args_list = zip(generations, references)
results = executor.map(evaluate_score, args_list)
all_results = list(results)
# serial version
# all_results = []
# for i in range(len(generations)):
# generation = generations[i]
# result = evaluate_score([generation, references[i]])
# all_results.append(result)
# compute pass@1
pass_at_1s = []
for execution_result in all_results:
c, n = execution_result.count(True), len(execution_result)
pass_at_1s.append(pass_at_k(n, c, 1))
metrics = {"pass@1": sum(pass_at_1s) / len(pass_at_1s) * 100}
results = {}
for i, r in enumerate(all_results):
r_new = []
for _r in r:
r_new.append([_r])
results[i] = r_new
return [metrics, results]
# borrowed and extended from
# https://github.com/Naman-ntc/codescratch/blob/main/evaluation/bigcode-evaluation-harness/lm_eval/tasks/custom_metrics/apps_custom_metrics/utils.py
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import json
import multiprocessing
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
from tqdm import tqdm
from lcb_runner.evaluation.testing_util import run_test
from lcb_runner.evaluation.pass_k_utils import compute_metrics_from_results
def _temp_run(sample, generation, debug, result, metadata_list, timeout):
res, metadata = run_test(sample, test=generation, debug=debug, timeout=timeout)
result.append(res)
metadata_list.append(metadata)
def check_correctness(sample, generation, timeout, debug=True):
"""Check correctness of code generation with a global timeout.
The global timeout is to catch some extreme/rare cases not handled by the timeouts
inside `run_test`"""
manager = multiprocessing.Manager()
result = manager.list()
metadata_list = manager.list()
p = multiprocessing.Process(
target=_temp_run,
args=(sample, generation, debug, result, metadata_list, timeout),
)
p.start()
p.join(
timeout=(timeout + 1) * len(json.loads(sample["input_output"])["inputs"]) + 5
)
if p.is_alive():
p.kill()
if not result:
in_outs = json.loads(sample["input_output"])
# consider that all tests failed
result = [[-1 for i in range(len(in_outs["inputs"]))]]
if debug:
print(f"global timeout")
return result[0], metadata_list[0]
def evaluate_generations_by_problem(args):
problem_generations: list[str] = args[0]
sample = args[1]
debug: bool = args[2]
timeout: int = args[3]
res = []
metadata = []
for o_idx, o in enumerate(problem_generations):
curr_res = [-2]
try:
curr_res, curr_metadata = check_correctness(
sample, o, timeout=timeout, debug=debug
)
if debug:
print(f"\nSuccessful compilation of task {o_idx}!")
fixed = []
for e in curr_res:
if isinstance(e, np.ndarray):
e = e.item(0)
if isinstance(e, np.bool_):
e = bool(e)
fixed.append(e)
curr_res = fixed
if not np.all(curr_res):
if debug:
print(f"Results were not True for all test cases {curr_res=}\n")
except Exception as e:
if debug:
print(f"Compilation failed, test framework exception = {repr(e)}{e}\n")
# break
curr_metadata = {
"error": repr(e),
"error_code": -5,
"error_message": "TestRunnerError",
}
finally:
assert isinstance(curr_res, list)
assert isinstance(curr_metadata, dict)
res.append(curr_res)
metadata.append(curr_metadata)
if debug:
for i, r in enumerate(problem_generations):
print("Sample\n")
print(r)
print("\n")
print("Result\n")
print(res[i])
print("*" * 30 + "\n\n")
return res, metadata
def evaluate_generations(
samples_list: list,
generations_list: list[list[str]],
debug: bool = False,
num_process_evaluate: int = 16,
timeout=6,
):
"""We take the list of code generations and try to compile them
and the run their corresponding unit tests which are retrieved from the APPS dataset.
Args:
generations: list of code generations (same order as samples in APPS dataset)
level: difficulty level used in the generation, can be "all", "introductory", "interview" or "competition"
Returns:
results: dictionary of results, key is the problem index, value is a list of results for each generation
[-2] = compile error, [-1] = runtime error [False] = failed test case [True] = passed test case
"""
# generations are code generations in the same order of the dataset
inputs = [
[(generations_list[index], samples_list[index], debug, timeout), index]
for index in range(len(generations_list))
]
with tqdm(total=len(inputs)) as pbar:
with ProcessPoolExecutor(
max_workers=1 if debug else num_process_evaluate
) as executor:
futures = {
executor.submit(evaluate_generations_by_problem, arg): index
for arg, index in inputs
}
results = {}
metadata = {}
for future in as_completed(futures):
index = futures[future]
results[index], metadata[index] = future.result()
pbar.update(1)
assert len(results) == len(
inputs
), f"results = {len(results)} inputs = {len(inputs)} {results=}"
# results = {i: r for r, (_, i) in zip(results, inputs)}
return results, metadata
def codegen_metrics(
samples_list,
generations_list,
k_list=[1, 5, 10, 20, 40, 50, 75, 100, 125, 150, 200, 500, 1000],
num_process_evaluate=16,
timeout=6,
debug=False,
):
samples_linear = []
generations_linear = []
remap_index = []
results = defaultdict(list)
metadatas = defaultdict(list)
for idx, (sample, generation_list) in enumerate(
zip(samples_list, generations_list)
):
assert isinstance(generation_list, list), generations_list[0]
for generation in generation_list:
assert isinstance(generation, str), generations_list[0]
samples_linear.append(sample)
generations_linear.append([generation])
remap_index.append(idx)
print(f"Evaluating {len(samples_linear)}...")
results_linear, metadatas_linear = evaluate_generations(
samples_linear,
generations_linear,
debug=debug,
num_process_evaluate=num_process_evaluate,
timeout=timeout,
)
for idx, sub_results in sorted(results_linear.items(), key=lambda x: x[0]):
results[remap_index[idx]].append(sub_results[0])
for idx, sub_metadatas in sorted(metadatas_linear.items(), key=lambda x: x[0]):
metadatas[remap_index[idx]].append(sub_metadatas[0])
metrics = compute_metrics_from_results(results, k_list=k_list)
final_metadata = []
for key in sorted(list(metadatas.keys())):
final_metadata.append(metadatas[key])
for i in range(len(final_metadata)):
if type(final_metadata[i]) is not list:
final_metadata[i] = [json.dumps(final_metadata[i])]
else:
final_metadata[i] = [json.dumps(x) for x in final_metadata[i]]
assert len(final_metadata[i]) == len(
generations_list[0]
), f"{len(final_metadata[i])=}"
return [metrics, results, final_metadata]
import json
import argparse
import numpy as np
from datetime import datetime
from lcb_runner.lm_styles import LanguageModelStore
from lcb_runner.evaluation.pass_k_utils import (
estimate_pass_at_k,
compute_metrics_from_results,
)
from lcb_runner.utils.scenarios import Scenario
from lcb_runner.utils.path_utils import get_eval_all_output_path
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
type=str,
default="gpt-3.5-turbo-0301",
help="Name of the model to use matching `lm_styles.py`",
)
parser.add_argument(
"--scenario",
type=Scenario,
default=Scenario.codegeneration,
help="Type of scenario to run",
)
parser.add_argument(
"--n", type=int, default=10, help="Number of samples to generate"
)
parser.add_argument(
"--temperature", type=float, default=0.2, help="Temperature for sampling"
)
parser.add_argument(
"--eval_all_file",
type=str,
default=None,
help="Alternative way to provide the evaluation file",
)
parser.add_argument(
"--start_date",
type=str,
default=None,
help="Start date for the contest to filter the evaluation file (format - YYYY-MM-DD)",
)
parser.add_argument(
"--end_date",
type=str,
default=None,
help="End date for the contest to filter the evaluation file (format - YYYY-MM-DD)",
)
parser.add_argument(
"--platform",
type=str,
default=None,
help="Platform to filter the evaluation file",
)
args = parser.parse_args()
if args.eval_all_file is None:
model = LanguageModelStore[args.model]
args.eval_all_file = get_eval_all_output_path(model, args)
return args
def compute_scores(args):
with open(args.eval_all_file, "r") as f:
results = json.load(f)
for res in results:
res["contest_date"] = datetime.fromisoformat(res["contest_date"])
if args.start_date is not None:
args.start_date = datetime.strptime(args.start_date, "%Y-%m-%d")
results = [
result for result in results if args.start_date <= result["contest_date"]
]
if args.end_date is not None:
args.end_date = datetime.strptime(args.end_date, "%Y-%m-%d")
results = [
result for result in results if result["contest_date"] <= args.end_date
]
if args.platform is not None:
results = [result for result in results if result["platform"] == args.platform]
print(len(results))
totals = [len(x["graded_list"]) for x in results]
corrects = [sum(x["graded_list"]) for x in results]
easy_totals = [len(x["graded_list"]) for x in results if x["difficulty"] == "easy"]
med_totals = [len(x["graded_list"]) for x in results if x["difficulty"] == "medium"]
hard_totals = [len(x["graded_list"]) for x in results if x["difficulty"] == "hard"]
easy_corrects = [sum(x["graded_list"]) for x in results if x["difficulty"] == "easy"]
med_corrects = [sum(x["graded_list"]) for x in results if x["difficulty"] == "medium"]
hard_corrects = [sum(x["graded_list"]) for x in results if x["difficulty"] == "hard"]
for k in [1, 5, 10, 25, 50, 100, 150, 200]:
print(
f"Pass@{k} = ",
estimate_pass_at_k(totals, corrects, k).mean(),
# np.array(
# [estimate_pass_at_k(t, c, k) for t, c in zip(totals, corrects)]
# ).mean(),
)
print(
f"Easy Pass@{k} = ",
estimate_pass_at_k(easy_totals, easy_corrects, k).mean(),
)
print(
f"Medium Pass@{k} = ",
estimate_pass_at_k(med_totals, med_corrects, k).mean(),
)
print(
f"Hard Pass@{k} = ",
estimate_pass_at_k(hard_totals, hard_corrects, k).mean(),
)
pass_1_list = [result["pass@1"] for result in results]
print(f"Pass@1: {sum(pass_1_list) / len(pass_1_list)}")
easy_pass_1_list = [
result["pass@1"]
for result in results
if "difficulty" in result and result["difficulty"] == "easy"
]
if len(easy_pass_1_list) > 0:
print(f"Easy Pass@1: {sum(easy_pass_1_list) / len(easy_pass_1_list)}")
medium_pass_1_list = [
result["pass@1"]
for result in results
if "difficulty" in result and result["difficulty"] == "medium"
]
if len(medium_pass_1_list) > 0:
print(f"Medium Pass@1: {sum(medium_pass_1_list) / len(medium_pass_1_list)}")
hard_pass_1_list = [
result["pass@1"]
for result in results
if "difficulty" in result and result["difficulty"] == "hard"
]
if len(hard_pass_1_list) > 0:
print(f"Hard Pass@1: {sum(hard_pass_1_list) / len(hard_pass_1_list)}")
if __name__ == "__main__":
compute_scores(get_parser())
import ast
import json
import tqdm
from lcb_runner.evaluation.pass_k_utils import compute_metrics_from_results
def parse_assert_statement(statement):
"""
Parse a Python assert statement and extract the expected output
from the right side of the '==' operator as a string.
:param statement: A string containing the assert statement.
:return: The expected output from the assert statement as a string.
"""
try:
parsed = ast.parse(statement, mode="exec")
except SyntaxError:
return "Invalid syntax"
if len(parsed.body) == 0:
return "Empty statement"
if not isinstance(parsed.body[0], ast.Assert):
return "Not an assert statement"
comparison = parsed.body[0].test
if not isinstance(comparison, ast.Compare) or not isinstance(
comparison.ops[0], ast.Eq
):
return "Not an equality assertion"
# Extract and return the right side of the '==' operator as a string
return ast.get_source_segment(statement, comparison.comparators[0])
def check_testcase_output(testcase_str, expected_output):
if len(testcase_str.splitlines()) > 1:
for line in testcase_str.splitlines():
if line.startswith("#"):
continue
if "assert" in line:
testcase_str = line
break
testcase_str = testcase_str.strip()
if "assert" in testcase_str:
testcase_output_str = str(parse_assert_statement(testcase_str))
else:
testcase_output_str = testcase_str
global_result = None
try:
testcase_output_eval = eval(testcase_output_str)
except:
global_result = False
# print("Failed to eval testcase output", testcase_output_str)
# breakpoint()
try:
expected_output_eval = json.loads(expected_output)
except:
global_result = False
print("Failed to eval expected testcase output", expected_output)
if global_result is None:
global_result = testcase_output_eval == expected_output_eval
return global_result
def test_output_metrics(
samples,
generations,
k_list=[1, 5],
):
num_samples = len(samples)
results = []
for idx in tqdm.tqdm(list(range(num_samples))):
idx_results = []
sample = samples[idx]
extracted_generation_list = generations[idx]
for extracted_generation in extracted_generation_list:
global_result = check_testcase_output(
extracted_generation, sample["output"]
)
idx_results.append([global_result])
results.append(idx_results)
results = {result_idx: results[result_idx] for result_idx in range(len(results))}
metrics = compute_metrics_from_results(results, k_list=k_list)
return [metrics, results]
import numpy as np
import json
from lcb_runner.benchmarks import load_generation_dataset, CodeGenerationProblem
from lcb_runner.evaluation import codegen_metrics
dataset = load_generation_dataset()
dataset = sorted(dataset, key=lambda x: x.question_id)
def check_model(model_key):
path = f"/home/naman/Repos/LiveCodeBench/run_models_outputs/{model_key}/chat_0.2_checked.json"
with open(path) as f:
old_results = json.load(f)
old_results = sorted(old_results, key=lambda x: x["question_id"])
assert old_results[0]["question_id"] == dataset[0].question_id
def debug(idx):
codegen_metrics(
[dataset[idx].get_evaluation_sample()],
[old_results[idx]["code_list"][:1]],
debug=True,
)
def run(idx):
return codegen_metrics(
[dataset[idx].get_evaluation_sample()],
[old_results[idx]["code_list"]],
)
debug(380)
exit()
# debug(196)
# debug(352)
metrics = codegen_metrics(
[d.get_evaluation_sample() for d in dataset],
[r["code_list"] for r in old_results],
num_process_evaluate=12,
)
old_pass1 = np.mean([np.mean(r["pass1_list"]) for r in old_results])
print(old_pass1)
print(metrics[0]["pass@1"])
for idx in range(400):
old_pass1 = np.mean(old_results[idx]["pass1_list"])
new_pass1 = metrics[0]["detail"]["pass@1"][idx]
if not abs(old_pass1 - new_pass1) < 1e-4:
print(idx, old_pass1, new_pass1)
# model_key = "GPT-4-Turbo-1106"
# check_model(model_key)
model_key = "Claude-3-Opus"
check_model(model_key)
model_key = "GPT-4-0613"
check_model(model_key)
model_key = "Mistral-Large"
check_model(model_key)
model_key = "Claude-3-Sonnet"
check_model(model_key)
model_key = "GPT-3.5-Turbo-0301"
check_model(model_key)
model_key = "Gemini-Pro"
check_model(model_key)
import numpy as np
def estimate_pass_at_k(num_samples, num_correct, k):
"""Estimates pass@k of each problem and returns them in an array."""
def estimator(n: int, c: int, k: int) -> float:
"""Calculates 1 - comb(n - c, k) / comb(n, k)."""
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
import itertools
if isinstance(num_samples, int):
num_samples_it = itertools.repeat(num_samples, len(num_correct))
else:
assert len(num_samples) == len(num_correct)
num_samples_it = iter(num_samples)
return np.array(
[estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)]
)
def compute_metrics_from_results(results, k_list=[1, 5]):
total = []
correct = []
task_ids = []
for task_id, res in results.items():
all_correct = []
for generation in res:
gen = np.array(generation)
all_correct.append(np.all(gen > 0))
task_ids.append(task_id)
total.append(len(all_correct))
correct.append(sum(all_correct))
total = np.array(total)
correct = np.array(correct)
ks = k_list
detail_pass_at_k = {
f"pass@{k}": estimate_pass_at_k(total, correct, k).tolist()
for k in ks
if (total >= k).all()
}
pass_at_k = {
f"pass@{k}": estimate_pass_at_k(total, correct, k).mean()
for k in ks
if (total >= k).all()
}
detail_metrics = {k: dict(zip(task_ids, v)) for k, v in detail_pass_at_k.items()}
pass_at_k["detail"] = detail_metrics
return pass_at_k
def extract_instance_results(results):
instance_wise_grades = {}
for task_id, res in results.items():
instance_wise_grades[task_id] = []
for generation in res:
instance_wise_grades[task_id].append(all([g > 0 for g in generation]))
instance_wise_grades = [
v for _, v in sorted(instance_wise_grades.items(), key=lambda item: item[0])
]
return instance_wise_grades
import ast
import json
import sys
import faulthandler
import platform
# used for debugging to time steps
from datetime import datetime
# to run the solution files we're using a timing based approach
import signal
import numpy as np
# for capturing the stdout
from io import StringIO
# used for testing the code that reads from input
from unittest.mock import patch, mock_open
from pyext import RuntimeModule
from enum import Enum
def truncatefn(s, length=300):
assert isinstance(s, str)
if len(s) <= length:
return s
return s[: length // 2] + "...(truncated) ..." + s[-length // 2 :]
class CODE_TYPE(Enum):
call_based = 0
standard_input = 1
# stuff for setting up signal timer
class TimeoutException(Exception):
pass
def timeout_handler(signum, frame):
print("alarm went off")
# return
raise TimeoutException
signal.signal(signal.SIGALRM, timeout_handler)
# timeout = 6 # seconds
# used to capture stdout as a list
# from https://stackoverflow.com/a/16571630/6416660
# alternative use redirect_stdout() from contextlib
class Capturing(list):
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
# Make closing the StringIO a no-op
self._stringio.close = lambda x: 1
return self
def __exit__(self, *args):
self.append(self._stringio.getvalue())
del self._stringio # free up some memory
sys.stdout = self._stdout
def only_int_check(val):
return isinstance(val, int)
def string_int_check(val):
return isinstance(val, str) and val.isdigit()
def combined_int_check(val):
return only_int_check(val) or string_int_check(val)
def run_test(sample, test=None, debug=False, timeout=6):
"""
if test(generated_code) is not None it'll try to run the code.
otherwise it'll just return an input and output pair.
"""
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
if debug:
print(f"start = {datetime.now().time()}")
try:
in_outs = json.loads(sample["input_output"])
except ValueError:
in_outs = None
if in_outs:
if in_outs.get("fn_name") is None:
which_type = CODE_TYPE.standard_input # Standard input
method_name = None
else:
which_type = CODE_TYPE.call_based # Call-based
method_name = in_outs["fn_name"]
if debug:
print(f"loaded input_output = {datetime.now().time()}")
if test is None:
assert False, "should not happen: test code is none"
return in_outs, {"error": "no test code provided"}
elif test is not None:
results = []
sol = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(6*10**5)\n"
if debug:
print(f"loading test code = {datetime.now().time()}")
if which_type == CODE_TYPE.call_based:
sol += test
if debug:
print(f"sol = {sol}")
signal.alarm(timeout)
try:
tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol)
if "class Solution" not in test:
tmp = tmp_sol
else:
tmp = tmp_sol.Solution()
signal.alarm(0)
except Exception as e:
signal.alarm(0)
if debug:
print(f"type 0 compilation error = {e}")
results.append(-2)
return results, {
"error": repr(e),
"error_code": -1,
"error_message": "Compilation Error",
}
signal.alarm(0)
elif which_type == CODE_TYPE.standard_input:
# sol
# if code has if __name__ == "__main__": then remove it
try:
astree = ast.parse(test)
last_block = astree.body[-1]
if isinstance(last_block, ast.If):
condition = last_block.test
if ast.unparse(condition).strip() == "__name__ == '__main__'":
test = (
ast.unparse(astree.body[:-1])
+ "\n"
+ ast.unparse(last_block.body)
)
except:
pass
tmp_test = test.split("\n")
new_test = []
for x in tmp_test:
if (not x.startswith("from ")) and (not x.startswith("import ")):
new_test.append("\t" + x + "\n")
else:
new_test.append(x + "\n")
tmp_test = new_test
new_test = ""
started = False
for i in tmp_test:
if i.startswith("\t") and not started:
new_test += "stdin = sys.stdin\nstdout = sys.stdout\n"
new_test += "def code():\n"
new_test += i
started = True
elif started and ((i.startswith("from ")) or (i.startswith("import "))):
new_test += "\t" + i
else:
new_test += i
tmp_test = new_test
sol += tmp_test
if debug:
print(f"sol = {sol}")
method_name = "code"
signal.alarm(timeout)
try:
tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol)
tmp = tmp_sol
signal.alarm(0)
except Exception as e:
signal.alarm(0)
if debug:
print(f"type 1 compilation error = {e}")
results.append(-2)
return results, {
"error": repr(e),
"error_code": -1,
"error_message": "Compilation Error",
}
signal.alarm(0)
if debug:
print(f"get method = {datetime.now().time()}")
try:
method = getattr(tmp, method_name) # get_attr second arg must be str
except:
signal.alarm(0)
e = sys.exc_info()
print(f"unable to get function error = {e}")
results.append(-2)
return results, {
"error": repr(e),
"error_code": -1,
"error_message": "Unable to extract code",
}
for index, inputs in enumerate(in_outs["inputs"]):
raw_inputs = inputs
raw_outputs = in_outs["outputs"][index]
if which_type == CODE_TYPE.call_based:
inputs = [json.loads(line) for line in inputs.split("\n")]
in_outs["outputs"][index] = json.loads(in_outs["outputs"][index])
truncate_line_size = 300 // (raw_inputs.count("\n") + 1)
raw_inputs = "\n".join(
[
truncatefn(line, truncate_line_size)
for line in raw_inputs.strip().split("\n")
]
)
raw_outputs = truncatefn(raw_outputs, 200)
else:
raw_inputs = truncatefn(raw_inputs)
raw_outputs = truncatefn(raw_outputs, 200)
# JSON forces dictionaries to have string keys; this undoes this (assuming a singleton list)
try:
if isinstance(inputs[0], dict):
inputs = [{int(k): v for k, v in inputs[0].items()}]
except:
True
try:
if isinstance(in_outs["outputs"][index], dict):
in_outs["outputs"][index] = [
{int(k): v for k, v in in_outs["outputs"][index].items()}
]
except:
True
try:
if isinstance(in_outs["outputs"][index][0], dict):
in_outs["outputs"][index] = [
{int(k): v for k, v in in_outs["outputs"][index][0].items()}
]
except:
True
if debug:
print(
f"time: {datetime.now().time()} testing index = {index} inputs = {inputs}, {type(inputs)}. type = {which_type}"
)
if which_type == CODE_TYPE.call_based: # Call-based
signal.alarm(timeout)
faulthandler.enable()
try:
output = method(*inputs)
raw_true_output = output
raw_true_output_copy = json.dumps(output)
raw_true_output_copy = truncatefn(raw_true_output_copy, 200)
# ground truth sequences are not tuples
if isinstance(output, tuple):
output = list(output)
tmp_result = output == in_outs["outputs"][index]
if (
isinstance(in_outs["outputs"][index], list)
and in_outs["outputs"][index]
):
tmp_result = tmp_result or (
output == in_outs["outputs"][index][0]
)
# ground truth sequences are not tuples
try:
if isinstance(output[0], tuple):
tmp_result = tmp_result or (
[list(x) for x in output]
== in_outs["outputs"][index][0]
)
except:
True
results.append(tmp_result)
if tmp_result != True:
return results, {
"output": raw_true_output_copy,
"expected": raw_outputs,
"inputs": raw_inputs,
"error_code": -2,
"error_message": "Wrong Answer",
}
# reset the alarm
signal.alarm(0)
except Exception as e:
signal.alarm(0)
faulthandler.disable()
if debug:
print(
f"Standard input runtime error or time limit exceeded error = {e}"
)
results.append(-1)
if "timeoutexception" in repr(e).lower():
return results, {
"error": repr(e),
"error_code": -3,
"error_message": "Time Limit Exceeded",
"inputs": raw_inputs,
"expected": raw_outputs,
}
else:
return results, {
"error": repr(e),
"error_code": -4,
"error_message": "Runtime Error",
"inputs": raw_inputs,
"expected": raw_outputs,
}
faulthandler.disable()
signal.alarm(0)
if debug:
print(
f"outputs = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}"
)
elif which_type == CODE_TYPE.standard_input: # Standard input
faulthandler.enable()
passed = False
if isinstance(inputs, list):
inputs = "\n".join(inputs)
if isinstance(in_outs["outputs"][index], list):
in_outs["outputs"][index] = "\n".join(in_outs["outputs"][index])
signal.alarm(timeout)
with Capturing() as output:
try:
call_method(method, inputs)
# reset the alarm
signal.alarm(0)
passed = True
except Exception as e:
# runtime error or took too long
signal.alarm(0)
print(
f"Call-based runtime error or time limit exceeded error = {repr(e)}{e}"
)
results.append(-1)
if "timeoutexception" in repr(e).lower():
return results, {
"error": repr(e),
"error_code": -3,
"error_message": "Time Limit Exceeded",
"inputs": raw_inputs,
"expected": raw_outputs,
}
else:
return results, {
"error": repr(e),
"error_code": -4,
"error_message": "Runtime Error",
"inputs": raw_inputs,
"expected": raw_outputs,
}
signal.alarm(0)
raw_true_output = output[0]
raw_true_output_copy = truncatefn(raw_true_output, 200)
output = raw_true_output.splitlines()
if not passed:
if debug:
nl = "\n"
if not isinstance(inputs, list):
print(
f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}"
)
else:
print(
f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}"
)
continue
if passed and debug:
print(
f"==> output = {output}, test outputs = {in_outs['outputs'][index]}"
)
if custom_compare_(output, in_outs["outputs"][index]):
tmp_result = True
results.append(tmp_result)
continue
# ground truth sequences are expressed as lists not tuples
if isinstance(output, tuple):
output = list(output)
tmp_result = False
try:
tmp_result = output == [in_outs["outputs"][index]]
if isinstance(in_outs["outputs"][index], list):
tmp_result = tmp_result or (output == in_outs["outputs"][index])
if isinstance(output[0], str):
tmp_result = tmp_result or (
[e.strip() for e in output] == in_outs["outputs"][index]
)
except Exception as e:
if debug:
print(f"Failed check1 exception = {e}")
pass
if tmp_result == True:
results.append(tmp_result)
continue
# try one more time without \n
if isinstance(in_outs["outputs"][index], list):
for tmp_index, i in enumerate(in_outs["outputs"][index]):
in_outs["outputs"][index][tmp_index] = i.split("\n")
in_outs["outputs"][index][tmp_index] = [
x.strip() for x in in_outs["outputs"][index][tmp_index] if x
]
else:
in_outs["outputs"][index] = in_outs["outputs"][index].split("\n")
in_outs["outputs"][index] = list(
filter(len, in_outs["outputs"][index])
)
in_outs["outputs"][index] = list(
map(lambda x: x.strip(), in_outs["outputs"][index])
)
try:
tmp_result = output == [in_outs["outputs"][index]]
if isinstance(in_outs["outputs"][index], list):
tmp_result = tmp_result or (output == in_outs["outputs"][index])
except Exception as e:
if debug:
print(f"Failed check2 exception = {e}")
pass
if tmp_result == True:
results.append(tmp_result)
continue
# try by converting the output into a split up list too
if isinstance(output, list):
output = list(filter(len, output))
if debug:
nl = "\n"
if not isinstance(inputs, list):
print(
f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}"
)
else:
print(
f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}"
)
if tmp_result == True:
results.append(tmp_result)
continue
if debug:
print(f"{tmp_result=} @a")
try:
tmp_result = output == [in_outs["outputs"][index]]
if isinstance(in_outs["outputs"][index], list):
tmp_result = tmp_result or (output == in_outs["outputs"][index])
except Exception as e:
if debug:
print(f"Failed check3 exception = {e}")
pass
if debug:
print(f"{tmp_result=} @b")
try:
all_ints = all(
combined_int_check(e1) and combined_int_check(e2)
for e1, e2 in zip(output, in_outs["outputs"][index])
)
if not all_ints:
if debug:
print(
[
combined_int_check(e1) and combined_int_check(e2)
for e1, e2 in zip(output, in_outs["outputs"][index])
]
)
output_float = [float(e) for e in output]
gt_float = [float(e) for e in in_outs["outputs"][index]]
tmp_result = tmp_result or (
(len(output_float) == len(gt_float))
and np.allclose(output_float, gt_float)
)
except Exception as e:
pass
if debug:
print(f"{tmp_result=} @c")
try:
if isinstance(output[0], list):
all_ints = all(
combined_int_check(e1) and combined_int_check(e2)
for e1, e2 in zip(output[0], in_outs["outputs"][index])
)
if not all_ints:
output_float = [float(e) for e in output[0]]
gt_float = [float(e) for e in in_outs["outputs"][index][0]]
tmp_result = tmp_result or (
(len(output_float) == len(gt_float))
and np.allclose(output_float, gt_float)
)
except Exception as e:
pass
if tmp_result == True:
results.append(tmp_result)
continue
if debug:
print(f"{tmp_result=} @d")
# try by converting the stuff into split up list
if isinstance(in_outs["outputs"][index], list):
for tmp_index, i in enumerate(in_outs["outputs"][index]):
in_outs["outputs"][index][tmp_index] = set(i.split())
else:
in_outs["outputs"][index] = set(in_outs["outputs"][index].split())
if debug:
print(f"{tmp_result=} @e")
try:
tmp_result = output == in_outs["outputs"][index]
except Exception as e:
if debug:
print(f"Failed check4 exception = {e}")
continue
if tmp_result == True:
results.append(tmp_result)
continue
if debug:
print(f"{tmp_result=} @f")
# try by converting the output into a split up list too
if isinstance(output, list):
for tmp_index, i in enumerate(output):
output[tmp_index] = i.split()
output = list(filter(len, output))
for tmp_index, i in enumerate(output):
output[tmp_index] = set(i)
else:
output = output.split()
output = list(filter(len, output))
output = set(output)
if debug:
print(f"{tmp_result=} @g")
# try:
# tmp_result = set(frozenset(s) for s in output) == set(
# frozenset(s) for s in in_outs["outputs"][index]
# )
# except Exception as e:
# if debug:
# print(f"Failed check5 exception = {e}")
# if they are all numbers, round so that similar numbers are treated as identical
# try:
# all_ints = all(
# combined_int_check(e1) and combined_int_check(e2)
# for e1, e2 in zip(output, in_outs["outputs"][index])
# )
# tmp_result = tmp_result or (
# set(frozenset(round(float(t), 3) for t in s) for s in output)
# == set(
# frozenset(round(float(t), 3) for t in s)
# for s in in_outs["outputs"][index]
# )
# )
# except Exception as e:
# if debug:
# print(f"Failed check6 exception = {e}")
if debug:
print(f"{tmp_result=} @h")
if tmp_result == True and debug:
print("PASSED")
results.append(tmp_result)
if tmp_result != True:
return results, {
"output": raw_true_output_copy,
"expected": raw_outputs,
"inputs": raw_inputs,
"error_code": -2,
"error_message": "Wrong Answer",
}
if debug:
nl = "\n"
if not isinstance(inputs, list):
print(
f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}"
)
else:
print(
f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}"
)
print(f"results = {results}")
return results, {}
def custom_compare_(output, ground_truth):
if isinstance(output, list):
output_1 = "\n".join(output)
if stripped_string_compare(output_1, ground_truth):
return True
if isinstance(output, list):
output_2 = [o.lstrip().rstrip() for o in output]
output_2 = "\n".join(output_2)
if stripped_string_compare(output_2, ground_truth):
return True
return False
def stripped_string_compare(s1, s2):
s1 = s1.lstrip().rstrip()
s2 = s2.lstrip().rstrip()
return s1 == s2
def call_method(method, inputs):
if isinstance(inputs, list):
inputs = "\n".join(inputs)
inputs_line_iterator = iter(inputs.split("\n"))
# sys.setrecursionlimit(10000)
# @patch('builtins.input', side_effect=inputs.split("\n"))
@patch("builtins.open", mock_open(read_data=inputs))
@patch("sys.stdin", StringIO(inputs))
@patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator))
@patch("sys.stdin.readlines", lambda *args: inputs.split("\n"))
@patch("sys.stdin.read", lambda *args: inputs)
# @patch('sys.stdout.write', print)
def _inner_call_method(_method):
try:
return _method()
except SystemExit as e:
pass
finally:
pass
return _inner_call_method(method)
def reliability_guard(maximum_memory_bytes=None):
"""
This disables various destructive functions and prevents the generated code
from interfering with the test (e.g. fork bomb, killing other processes,
removing filesystem files, etc.)
WARNING
This function is NOT a security sandbox. Untrusted code, including, model-
generated code, should not be blindly executed outside of one. See the
Codex paper for more information about OpenAI's code sandbox, and proceed
with caution.
"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(
resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)
)
resource.setrlimit(
resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)
)
if not platform.uname().system == "Darwin":
resource.setrlimit(
resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)
)
faulthandler.disable()
import builtins
builtins.exit = None
builtins.quit = None
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.kill = None
os.system = None
os.putenv = None
os.remove = None
os.removedirs = None
os.rmdir = None
os.fchdir = None
os.setuid = None
os.fork = None
os.forkpty = None
os.killpg = None
os.rename = None
os.renames = None
os.truncate = None
os.replace = None
os.unlink = None
os.fchmod = None
os.fchown = None
os.chmod = None
os.chown = None
os.chroot = None
os.fchdir = None
os.lchflags = None
os.lchmod = None
os.lchown = None
os.getcwd = None
os.chdir = None
import shutil
shutil.rmtree = None
shutil.move = None
shutil.chown = None
import subprocess
subprocess.Popen = None # type: ignore
__builtins__["help"] = None
import sys
sys.modules["ipdb"] = None
sys.modules["joblib"] = None
sys.modules["resource"] = None
sys.modules["psutil"] = None
sys.modules["tkinter"] = None
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
BASE_IMPORTS = """from itertools import accumulate, chain, combinations, count, permutations, product, groupby, islice, repeat
from copy import deepcopy
from string import ascii_lowercase
from math import floor, log2, log10, sqrt, comb, gcd, ceil, inf, isqrt
from collections import defaultdict, deque, Counter
from bisect import bisect, bisect_left, bisect_right, insort
from heapq import heappush, heappop, heapify, merge
from functools import reduce, cache, lru_cache
from random import randrange, shuffle
from operator import itemgetter, sub
from re import search as re_search # Assuming 're' refers to a regex search
from os.path import commonprefix
from typing import List, Tuple, Dict, Set, Optional, Union, Any, Callable, Iterable, Iterator, Generator
import copy
import string
import math
import collections
import bisect
import heapq
import functools
import random
import itertools
import operator
import re
import numpy as np
import pandas as pd
from math import log, prod # 'log' and 'prod' are functions in the math module
from collections import deque, defaultdict, Counter, OrderedDict
from itertools import accumulate, permutations, combinations, product, groupby, islice, chain, repeat, zip_longest, cycle
from functools import lru_cache, reduce, partial
# from sortedcontainers import SortedList, SortedDict, SortedSet
# import sortedcontainers
from operator import iand
import sys
"""
def check_correctness(check_program, timeout=3):
"""
Evaluates the functional correctness of a completion by running the test
suite provided in the problem.
:param completion_id: an optional completion ID so we can match
the results later even if execution finishes asynchronously.
"""
manager = multiprocessing.Manager()
result = manager.list()
p = multiprocessing.Process(target=unsafe_execute, args=(check_program, result, timeout))
p.start()
p.join(timeout=timeout + 1)
if p.is_alive():
p.kill()
if not result:
result.append("timed out")
return result[0] == "passed"
def unsafe_execute(check_program, result, timeout):
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
rmtree = shutil.rmtree
rmdir = os.rmdir
chdir = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
exec_globals = {}
with swallow_io():
with time_limit(timeout):
exec(check_program, exec_globals)
result.append("passed")
except TimeoutException:
result.append("timed out")
except BaseException as e:
result.append(f"failed: {e}")
# Needed for cleaning up.
shutil.rmtree = rmtree
os.rmdir = rmdir
os.chdir = chdir
@contextlib.contextmanager
def time_limit(seconds):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.setitimer(signal.ITIMER_REAL, seconds)
signal.signal(signal.SIGALRM, signal_handler)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
@contextlib.contextmanager
def swallow_io():
stream = WriteOnlyStringIO()
with contextlib.redirect_stdout(stream):
with contextlib.redirect_stderr(stream):
with redirect_stdin(stream):
yield
@contextlib.contextmanager
def create_tempdir():
with tempfile.TemporaryDirectory() as dirname:
with chdir(dirname):
yield dirname
class TimeoutException(Exception):
pass
class WriteOnlyStringIO(io.StringIO):
"""StringIO that throws an exception when it's read from"""
def read(self, *args, **kwargs):
raise OSError
def readline(self, *args, **kwargs):
raise OSError
def readlines(self, *args, **kwargs):
raise OSError
def readable(self, *args, **kwargs):
"""Returns True if the IO object can be read."""
return False
class redirect_stdin(contextlib._RedirectStream): # type: ignore
_stream = "stdin"
@contextlib.contextmanager
def chdir(root):
if root == ".":
yield
return
cwd = os.getcwd()
os.chdir(root)
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(cwd)
def reliability_guard(maximum_memory_bytes=None):
"""
This disables various destructive functions and prevents the generated code
from interfering with the test (e.g. fork bomb, killing other processes,
removing filesystem files, etc.)
WARNING
This function is NOT a security sandbox. Untrusted code, including, model-
generated code, should not be blindly executed outside of one. See the
Codex paper for more information about OpenAI's code sandbox, and proceed
with caution.
"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))
resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))
faulthandler.disable()
import builtins
builtins.exit = None
builtins.quit = None
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.kill = None
os.system = None
os.putenv = None
os.remove = None
os.removedirs = None
os.rmdir = None
os.fchdir = None
os.setuid = None
os.fork = None
os.forkpty = None
os.killpg = None
os.rename = None
os.renames = None
os.truncate = None
os.replace = None
os.unlink = None
os.fchmod = None
os.fchown = None
os.chmod = None
os.chown = None
os.chroot = None
os.fchdir = None
os.lchflags = None
os.lchmod = None
os.lchown = None
os.getcwd = None
os.chdir = None
import shutil
shutil.rmtree = None
shutil.move = None
shutil.chown = None
import subprocess
subprocess.Popen = None # type: ignore
__builtins__["help"] = None
import sys
sys.modules["ipdb"] = None
sys.modules["joblib"] = None
sys.modules["resource"] = None
sys.modules["psutil"] = None
sys.modules["tkinter"] = None
\ No newline at end of file
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
class LMStyle(Enum):
OpenAIChat = "OpenAIChat"
OpenAIReason = "OpenAIReason"
Claude = "Claude" # Claude 1 and Claude 2
Claude3 = "Claude3"
Gemini = "Gemini"
MistralWeb = "MistralWeb"
CohereCommand = "CohereCommand"
DataBricks = "DataBricks"
DeepSeekAPI = "DeepSeekAPI"
GenericBase = "GenericBase"
DeepSeekCodeInstruct = "DeepSeekCodeInstruct"
CodeLLaMaInstruct = "CodeLLaMaInstruct"
StarCoderInstruct = "StarCoderInstruct"
CodeQwenInstruct = "CodeQwenInstruct"
Phind = "Phind"
WizardCoder = "WizardCoder"
MagiCoder = "MagiCoder"
OC = "OC"
Eurusx = "Eurusx"
Qwen1point5 = "Qwen1point5"
Smaug2 = "Smaug2"
LLaMa3 = "LLaMa3"
DracarysLlama = "DracarysLlama"
DracarysQwen = "DracarysQwen"
@dataclass
class LanguageModel:
model_name: str
model_repr: str
model_style: LMStyle
release_date: datetime | None # XXX Should we use timezone.utc?
link: str | None = None
def __hash__(self) -> int:
return hash(self.model_name)
LanguageModelList: list[LanguageModel] = [
LanguageModel(
"meta-llama/Meta-Llama-3-70B",
"LLama3-70b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3-70B",
),
LanguageModel(
"meta-llama/Meta-Llama-3-8B",
"LLama3-8b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3-8B",
),
LanguageModel(
"meta-llama/Meta-Llama-3-8B-Instruct",
"LLama3-8b-Ins",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
),
LanguageModel(
"meta-llama/Meta-Llama-3-70B-Instruct",
"LLama3-70b-Ins",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-8B",
"LLama3.1-8b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-70B",
"LLama3.1-70b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-70B",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-405B-FP8",
"LLama3.1-405b-Base-FP8",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
"LLama3.1-8b-Ins",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-70B-Instruct",
"LLama3.1-70b-Ins",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct",
),
LanguageModel(
"meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
"LLama3.1-405b-Ins-FP8",
LMStyle.LLaMa3,
datetime(2023, 1, 1),
link="https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
),
LanguageModel(
"deepseek-ai/deepseek-coder-33b-base",
"DSCoder-33b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-33b-base",
),
LanguageModel(
"deepseek-ai/deepseek-coder-6.7b-base",
"DSCoder-6.7b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base",
),
LanguageModel(
"deepseek-ai/deepseek-coder-1.3b-base",
"DSCoder-1.3b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base",
),
LanguageModel(
"deepseek-ai/deepseek-coder-33b-instruct",
"DSCoder-33b-Ins",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 9, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct",
),
LanguageModel(
"deepseek-ai/deepseek-coder-6.7b-instruct",
"DSCoder-6.7b-Ins",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 9, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct",
),
LanguageModel(
"deepseek-ai/deepseek-coder-1.3b-instruct",
"DSCoder-1.3b-Ins",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 8, 1),
link="https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct",
),
LanguageModel(
"deepseek-chat",
"DeepSeek-V2",
LMStyle.DeepSeekAPI,
datetime(2023, 8, 1),
link="https://huggingface.co/deepseek-ai/DeepSeek-V2",
),
LanguageModel(
"deepseek-coder",
"DeepSeekCoder-V2.5",
LMStyle.DeepSeekAPI,
datetime(2023, 8, 1),
link="https://huggingface.co/deepseek-ai/DeepSeek-V2",
),
LanguageModel(
"deepseek-ai/DeepSeek-V2-Chat",
"DeepSeek-V2-Chat",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat",
),
LanguageModel(
"deepseek-ai/DeepSeek-Coder-V2-Instruct",
"DeepSeek-Coder-V2-Instruct",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct",
),
LanguageModel(
"deepseek-ai/DeepSeek-V2-Chat-0628",
"DeepSeek-V2-Chat-0628",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628",
),
LanguageModel(
"deepseek-ai/DeepSeek-Coder-V2-Instruct-0724",
"DeepSeek-Coder-V2-Instruct-0724",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct-0724",
),
LanguageModel(
"deepseek-ai/DeepSeek-V2-Lite-Chat",
"DeepSeek-V2-Lite-Chat",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat",
),
LanguageModel(
"deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"DeepSeek-Coder-V2-Lite-Instruct",
LMStyle.DeepSeekCodeInstruct,
datetime(2023, 12, 30),
link="https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
),
LanguageModel(
"codellama/CodeLlama-70b-hf",
"CodeLlama-70b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-70b-hf",
),
LanguageModel(
"codellama/CodeLlama-34b-hf",
"CodeLlama-34b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-34b-hf",
),
LanguageModel(
"codellama/CodeLlama-13b-hf",
"CodeLlama-13b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-13b-hf",
),
LanguageModel(
"codellama/CodeLlama-7b-hf",
"CodeLlama-7b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-7b-hf",
),
LanguageModel(
"codellama/CodeLlama-70b-Instruct-hf",
"CodeLlama-70b-Ins",
LMStyle.CodeLLaMaInstruct,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-70b-hf",
),
LanguageModel(
"codellama/CodeLlama-34b-Instruct-hf",
"CodeLlama-34b-Ins",
LMStyle.CodeLLaMaInstruct,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf",
),
LanguageModel(
"codellama/CodeLlama-13b-Instruct-hf",
"CodeLlama-13b-Ins",
LMStyle.CodeLLaMaInstruct,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf",
),
LanguageModel(
"codellama/CodeLlama-7b-Instruct-hf",
"CodeLlama-7b-Ins",
LMStyle.CodeLLaMaInstruct,
datetime(2023, 1, 1),
link="https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf",
),
LanguageModel(
"gpt-3.5-turbo-0301",
"GPT-3.5-Turbo-0301",
LMStyle.OpenAIChat,
datetime(2021, 10, 1),
link="https://openai.com/blog/new-models-and-developer-products-announced-at-devday",
),
LanguageModel(
"gpt-3.5-turbo-0125",
"GPT-3.5-Turbo-0125",
LMStyle.OpenAIChat,
datetime(2021, 10, 1),
link="https://openai.com/blog/new-embedding-models-and-api-updates#:~:text=Other%20new%20models%20and%20lower%20pricing",
),
LanguageModel(
"gpt-4-0613",
"GPT-4-0613",
LMStyle.OpenAIChat,
datetime(2021, 10, 1),
link="https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4",
),
LanguageModel(
"gpt-4-1106-preview",
"GPT-4-Turbo-1106",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://openai.com/blog/new-models-and-developer-products-announced-at-devday",
),
LanguageModel(
"gpt-4-turbo-2024-04-09",
"GPT-4-Turbo-2024-04-09",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4",
),
LanguageModel(
"gpt-4o-2024-05-13",
"GPT-4O-2024-05-13",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"gpt-4o-2024-08-06",
"GPT-4O-2024-08-06",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"gpt-4o-mini-2024-07-18",
"GPT-4O-mini-2024-07-18",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"chatgpt-4o-latest",
"ChatGPT-4O-Latest-08-24",
LMStyle.OpenAIChat,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"o1-preview-2024-09-12",
"O1-Preview-2024-09-12 (N=1)",
LMStyle.OpenAIReason,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"o1-mini-2024-09-12",
"O1-Mini-2024-09-12 (N=1)",
LMStyle.OpenAIReason,
datetime(2023, 4, 30),
link="https://openai.com/index/spring-update",
),
LanguageModel(
"claude-instant-1",
"Claude-Instant-1",
LMStyle.Claude,
datetime(2022, 12, 31),
link="https://www.anthropic.com/index/introducing-claude",
),
LanguageModel(
"claude-2",
"Claude-2",
LMStyle.Claude,
datetime(2022, 12, 31),
link="https://www.anthropic.com/index/claude-2",
),
LanguageModel(
"claude-3-opus-20240229",
"Claude-3-Opus",
LMStyle.Claude3,
datetime(2023, 9, 1),
link="https://www.anthropic.com/index/claude-3",
),
LanguageModel(
"claude-3-sonnet-20240229",
"Claude-3-Sonnet",
LMStyle.Claude3,
datetime(2023, 9, 1),
link="https://www.anthropic.com/index/claude-3",
),
LanguageModel(
"claude-3-5-sonnet-20240620",
"Claude-3.5-Sonnet",
LMStyle.Claude3,
datetime(2024, 3, 31),
link="https://www.anthropic.com/news/claude-3-5-sonnet",
),
LanguageModel(
"claude-3-haiku-20240307",
"Claude-3-Haiku",
LMStyle.Claude3,
datetime(2023, 4, 30),
link="https://www.anthropic.com/index/claude-3",
),
LanguageModel(
"gemini-1.5-pro-002",
"Gemini-Pro-1.5-002",
LMStyle.Gemini,
datetime(2023, 4, 30),
link="https://blog.google/technology/ai/gemini-api-developers-cloud",
),
LanguageModel(
"gemini-1.5-flash-002",
"Gemini-Flash-1.5-002",
LMStyle.Gemini,
datetime(2023, 4, 30),
link="https://blog.google/technology/ai/gemini-api-developers-cloud",
),
LanguageModel(
"databricks-dbrx-instruct",
"DBRX-Ins",
LMStyle.DataBricks,
datetime(2023, 1, 1),
link="https://huggingface.co/databricks/dbrx-instruct",
),
LanguageModel(
"bigcode/starcoder2-3b",
"StarCoder2-3b",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/bigcode/starcoder2-7b-magicoder-instruct/tree/main",
),
LanguageModel(
"bigcode/starcoder2-7b",
"StarCoder2-7b",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/bigcode/starcoder2-7b-magicoder-instruct/tree/main",
),
LanguageModel(
"bigcode/starcoder2-15b",
"StarCoder2-15b",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/bigcode/starcoder2-7b-magicoder-instruct/tree/main",
),
LanguageModel(
"google/codegemma-7b",
"CodeGemma-7b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/google/codegemma-7b",
),
LanguageModel(
"google/codegemma-2b",
"CodeGemma-2b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/google/codegemma-2b",
),
LanguageModel(
"google/gemma-7b",
"Gemma-7b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/google/gemma-7b",
),
LanguageModel(
"google/gemma-2b",
"Gemma-2b-Base",
LMStyle.GenericBase,
datetime(2023, 1, 1),
link="https://huggingface.co/google/gemma-2b",
),
LanguageModel(
"mistral-large-latest",
"Mistral-Large",
LMStyle.MistralWeb,
datetime(2023, 1, 1),
link="https://mistral.ai/news/mistral-large/",
),
LanguageModel(
"open-mixtral-8x22b",
"Mixtral-8x22B-Ins",
LMStyle.MistralWeb,
datetime(2023, 1, 1),
link="https://mistral.ai/news/mixtral-8x22b/",
),
LanguageModel(
"open-mixtral-8x7b",
"Mixtral-8x7B-Ins",
LMStyle.MistralWeb,
datetime(2023, 1, 1),
link="https://mistral.ai/news/mixtral-8x7b/",
),
LanguageModel(
"open-mixtral-8x7b",
"Mixtral-8x7B-Ins",
LMStyle.MistralWeb,
datetime(2023, 1, 1),
link="https://mistral.ai/news/mixtral-8x7b/",
),
LanguageModel(
"codestral-latest",
"Codestral-Latest",
LMStyle.MistralWeb,
datetime(2023, 1, 1),
link="https://mistral.ai/news/codestral/",
),
LanguageModel(
"command-r",
"Command-R",
LMStyle.CohereCommand,
datetime(2023, 1, 1),
link="https://docs.cohere.com/docs/models",
),
LanguageModel(
"command-r-plus",
"Command-R+",
LMStyle.CohereCommand,
datetime(2023, 1, 1),
link="https://docs.cohere.com/docs/models",
),
LanguageModel(
"Qwen/CodeQwen1.5-7B",
"CodeQwen15-7B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/CodeQwen1.5-7B",
),
LanguageModel(
"Qwen/CodeQwen1.5-7B-Chat",
"CodeQwen15-7B-Chat",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat",
),
LanguageModel(
"Qwen/Qwen2-72B",
"Qwen2-Base-72B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2-72B",
),
LanguageModel(
"Qwen/Qwen2-72B-Instruct",
"Qwen2-Ins-72B",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2-72B-Instruct",
),
LanguageModel(
"Qwen/Qwen2.5-7B",
"Qwen2.5-Base-7B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-7B",
),
LanguageModel(
"Qwen/Qwen2.5-7B-Instruct",
"Qwen2.5-Ins-7B",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct",
),
LanguageModel(
"Qwen/Qwen2.5-32B",
"Qwen2.5-Base-32B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-32B",
),
LanguageModel(
"Qwen/Qwen2.5-32B-Instruct",
"Qwen2.5-Ins-32B",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-32B-Instruct",
),
LanguageModel(
"Qwen/Qwen2.5-72B",
"Qwen2.5-Base-72B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-72B",
),
LanguageModel(
"Qwen/Qwen2.5-72B-Instruct",
"Qwen2.5-Ins-72B",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-72B-Instruct",
),
LanguageModel(
"Qwen/Qwen2.5-Coder-7B",
"Qwen2.5-Coder-Base-7B",
LMStyle.GenericBase,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-Coder-7B",
),
LanguageModel(
"Qwen/Qwen2.5-Coder-7B-Instruct",
"Qwen2.5-Coder-Ins-7B",
LMStyle.CodeQwenInstruct,
datetime(2023, 8, 30),
link="https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct",
),
LanguageModel(
"m-a-p/OpenCodeInterpreter-DS-33B",
"OC-DS-33B",
LMStyle.OC,
datetime(2023, 1, 1),
link="https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-33B/",
),
LanguageModel(
"m-a-p/OpenCodeInterpreter-DS-6.7B",
"OC-DS-6.7B",
LMStyle.OC,
datetime(2023, 9, 1),
link="https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/",
),
LanguageModel(
"m-a-p/OpenCodeInterpreter-DS-1.3B",
"OC-DS-1.3B",
LMStyle.OC,
datetime(2023, 9, 1),
link="https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-1.3B/",
),
LanguageModel(
"stabilityai/stable-code-3b",
"StableCode-3B",
LMStyle.GenericBase,
datetime(2023, 9, 1),
link="https://huggingface.co/stabilityai/stable-code-3b/",
),
LanguageModel(
"bigcode/starcoder2-instruct-15b-v0.1",
"StarCoder2-Ins-v0.1",
LMStyle.LLaMa3,
datetime(2023, 4, 30),
link="https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1",
),
LanguageModel(
"qwen/Qwen1.5-72B-Chat",
"Qwen-1.5-72B-Chat ",
LMStyle.Qwen1point5,
datetime(2024, 3, 31),
link="https://huggingface.co/qwen/Qwen1.5-72B-Chat/",
),
LanguageModel(
"abacusai/Smaug-2-72B",
"Smaug-2-72B ",
LMStyle.Smaug2,
datetime(2024, 3, 31),
link="https://huggingface.co/abacusai/Smaug-2-72B/",
),
LanguageModel(
"WizardCoderLM/WizardCoderCoder-Python-34B-V1.0",
"WCoder-34B-V1",
LMStyle.WizardCoder,
datetime(2023, 1, 1),
link="https://huggingface.co/WizardCoderLM/WizardCoderCoder-Python-34B-V1.0",
),
LanguageModel(
"WizardCoderLM/WizardCoderCoder-33B-V1.1",
"WCoder-33B-V1.1",
LMStyle.WizardCoder,
datetime(2023, 9, 1),
link="https://huggingface.co/WizardCoderLM/WizardCoderCoder-33B-V1.1",
),
LanguageModel(
"Phind/Phind-CodeLlama-34B-v2",
"Phind-34B-V2",
LMStyle.Phind,
datetime(2023, 1, 1),
link="https://huggingface.co/Phind/Phind-CodeLlama-34B-v2",
),
LanguageModel(
"ise-uiuc/Magicoder-S-DS-6.7B",
"MagiCoderS-DS-6.7B",
LMStyle.MagiCoder,
datetime(2023, 7, 30),
link="https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B",
),
LanguageModel(
"ise-uiuc/Magicoder-S-CL-7B",
"MagiCoderS-CL-7B",
LMStyle.MagiCoder,
datetime(2023, 1, 1),
link="https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B",
),
LanguageModel(
"openbmb/Eurus-70b-sft",
"Eurus-70B-SFT (n=1)",
LMStyle.Eurusx,
datetime(2023, 1, 1),
link="https://huggingface.co/openbmb/Eurus-70b-sft",
),
LanguageModel(
"openbmb/Eurux-8x22b-nca",
"Eurux-8x22b-NCA (n=1)",
LMStyle.Eurusx,
datetime(2023, 4, 30),
link="https://huggingface.co/openbmb/Eurux-8x22b-nca",
),
LanguageModel(
"abacusai/Dracarys-Llama-3.1-70B-Instruct",
"LLama3.1-70b-Ins",
LMStyle.DracarysLlama,
datetime(2023, 1, 1),
link="https://huggingface.co/abacusai/Dracarys-Llama-3.1-70B-Instruct",
),
LanguageModel(
"abacusai/Dracarys-72B-Instruct",
"Qwen2-Ins-72B",
LMStyle.DracarysQwen,
datetime(2023, 1, 1),
link="https://huggingface.co/abacusai/Dracarys-72B-Instruct",
),
]
LanguageModelStore: dict[str, LanguageModel] = {
lm.model_name: lm for lm in LanguageModelList
}
if __name__ == "__main__":
print(list(LanguageModelStore.keys()))
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