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generate.py
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generate.py
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#
# Copyright (c) 2022, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
#
import json
import os
import pprint
import torch
import pdb
import glob
from tqdm import tqdm
import pickle as pkl
import numpy as np
from collections import Counter
from transformers import RobertaTokenizer, T5ForConditionalGeneration
import datasets.utils as dsutils
def generate_prompt(args, test_case_path, prompt_path, solutions_path, tokenizer,
starter_path=None):
_input = "\nQUESTION:\n"
with open(prompt_path, "r") as f:
data = f.readlines()
data = "".join(data)
_input += data
if starter_path != None:
with open(starter_path, "r") as f:
data = f.readlines()
data = "".join(data)
data = "\n" + data
_input += data
if os.path.exists(test_case_path):
with open(test_case_path, "r") as f:
data = json.load(f)
if not data.get("fn_name"):
_input += "\nUse Standard Input format"
else:
_input += "\nUse Call-Based format"
elif starter_path is not None and os.path.exists(starter_path):
_input += "\nUse Call-Based format"
else:
_input += "\nUse Standard Input format"
_input += "\nANSWER:\n"
return _input
def generate_critic_inputs(args, test_case_path, prompt_path, solutions_path, tokenizer,
starter_path=None, gt_solutions=False):
_input = generate_prompt(args, test_case_path, prompt_path, solutions_path, tokenizer, starter_path)
q_tokens = tokenizer.encode(_input, verbose=False, max_length=args.source_len)
in_tokens = [tokenizer.eos_token_id] * args.source_len
in_tokens[:len(q_tokens)] = q_tokens
in_tokens = in_tokens[:args.source_len]
solutions = json.load(open(solutions_path, 'r'))
all_texts = []
gt_errors = []
all_codes = []
for sol_index, solution in enumerate(solutions):
if gt_solutions:
solution_str = dsutils.reindent_code(solution)
else:
solution_str = dsutils.reindent_code(solution['code'])
a_tokens = tokenizer.encode(solution_str)
code = [-100] * args.max_len
code[:len(a_tokens)] = a_tokens
code = code[:args.max_len]
all_texts.append(in_tokens)
all_codes.append(code)
if gt_solutions:
gt_errors.append(dsutils.get_error_type(True, binary=args.binary_prediction))
else:
gt_errors.append(dsutils.get_error_type(solution['result'], binary=args.binary_prediction))
return all_texts, all_codes, gt_errors
def main(args):
argsdict = vars(args)
print(pprint.pformat(argsdict))
original_problems = glob.glob(args.test_path + '/*')
problems = sorted(original_problems)
if not os.path.exists(args.output_path):
os.makedirs(args.output_path, exist_ok=True)
print("Saving results to {}".format(args.output_path))
if args.start > len(problems) or args.start < 0:
print(f"start index {args.start} > number of problems {len(problems)}")
return
start = args.start
if args.end is None or args.end > len(problems):
end = len(problems)
else:
end = args.end
problems = problems[start:end]
# Set up model
tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base', cache_dir=args.tokenizer_path)
print("Loading model from {}...".format(args.model_path))
if args.critic_scores:
model = T5ForConditionalGeneration.from_pretrained(args.model_path, tuning_mode='critic')
else:
model = T5ForConditionalGeneration.from_pretrained(args.model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
if args.critic_scores:
all_preds = []
all_gts = []
# main eval loop
for index, problem in tqdm(enumerate(problems), ncols=0, total=len(problems)):
prob_path = os.path.join(problem)
print(f"problem path = {prob_path}")
problem_id = int(problem.split('/')[-1])
'''
if args.critic_scores and \
os.path.exists(os.path.join(args.output_path, f"{problem_id}_gt{args.gt_solutions}.pkl")):
continue
elif os.path.exists(os.path.join(args.output_path, f"{problem_id}.json")):
continue
'''
test_case_path = os.path.join(prob_path, "input_output.json")
prompt_path = os.path.join(prob_path, "question.txt")
starter_path = os.path.join(prob_path, "starter_code.py")
if args.critic_scores and not args.gt_solutions:
solutions_path = os.path.join(prob_path, "gen_solutions.json")
else:
solutions_path = os.path.join(prob_path, "solutions.json")
if not os.path.exists(starter_path):
starter_path = None
if args.critic_scores:
input_texts, input_codes, gt_error_types = generate_critic_inputs(args, test_case_path, prompt_path, solutions_path,
tokenizer, starter_path, args.gt_solutions)
else:
input_text = generate_prompt(args, test_case_path, prompt_path, solutions_path,
tokenizer, starter_path)
with torch.no_grad():
if args.critic_scores:
text_tensor = torch.tensor(input_texts).to(device)
code_tensor = torch.tensor(input_codes).to(device)
gt_error_tensor = torch.tensor(gt_error_types).to(device)
curr_inputs = {'input_ids': text_tensor, 'error_types': gt_error_tensor, 'labels': code_tensor}
_, error_preds, error_hidden_states = model(**curr_inputs, return_error_hidden_states=True)
assert len(gt_error_types) == len(error_preds)
all_preds.extend(error_preds.cpu().numpy().tolist())
all_gts.extend(gt_error_types)
saved_critic_scores = {'code': input_codes, 'prompt': input_texts,
'gt_error_type': gt_error_types,
'pred_error_type': error_preds.cpu().numpy(),
'error_hidden_states': error_hidden_states.cpu().numpy()}
if args.gt_solutions:
scores_loc = os.path.join(prob_path, "solutions_critic_scores.pkl")
else:
scores_loc = os.path.join(prob_path, "gen_solutions_critic_scores.pkl")
pkl.dump(saved_critic_scores, open(scores_loc, 'wb'))
else:
input_ids = torch.LongTensor(tokenizer.encode(input_text,
verbose=False,
max_length=args.source_len)).unsqueeze(0).cuda()
num_loops = int(args.num_seqs / args.num_seqs_per_iter)
output_programs = []
for i in tqdm(range(num_loops), ncols=0, total=num_loops, leave=False):
output_ids = model.generate(
input_ids,
do_sample=True,
temperature=args.temperature,
max_length=args.max_len,
num_return_sequences=args.num_seqs_per_iter,
top_p=0.95)
for output_id in output_ids:
output_programs.append(tokenizer.decode(output_id, skip_special_tokens=True))
saved_codes = {}
saved_codes[problem_id] = {'code': output_programs, 'prompt': input_text}
codes_loc = os.path.join(args.output_path, f"{problem_id}.json")
with open(codes_loc, "w") as f:
json.dump(saved_codes, f)
if args.critic_scores:
print("Total number of samples: {}".format(len(all_gts)))
acc = (np.array(all_preds) == np.array(all_gts)).sum()/len(all_gts)
print("Error Pred Acc: {}".format(acc))
print("Prediction distribution: {}".format(Counter(all_preds)))
print("GT distribution: {}".format(Counter(all_gts)))
if __name__ == "__main__":
from configs.generate_configs import *
main(args)