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import jsonlines
import argparse
import pprint
import sys
import os
import re
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
from human_eval.data import write_jsonl, read_problems, stream_jsonl
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
try:
if torch.backends.mps.is_available():
device = "mps"
except:
pass
def read_mbpp(path):
mbpp_problems = {}
with jsonlines.open(path, "r") as fin:
for obj in fin:
mbpp_problems[obj["task_id"]] = obj
return mbpp_problems
def extract_text(prompt, remove_lines=True):
token = '\"\"\"'
start = token
end = '>>>'
start_idx = prompt.find(start) + len(start)
end_idx = prompt.find(end)
output = prompt[start_idx: end_idx]
if remove_lines:
output = output.replace('\n', ' ')
output = re.sub(r"\s+", " ", output).strip()
return output
def generate_prompt(input):
INSTRUCTION = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Create a Python script for this problem:
{input}
### Response:"""
return INSTRUCTION
def get_model(
load_8bit: bool = False,
base_model: str = "bigcode/starcoder",
):
assert base_model, (
"Please specify a --base_model, e.g. --base_model='bigcode/starcoder'"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
if device == "cuda":
model = AutoModelForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
elif device == "mps":
model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map={"": device},
torch_dtype=torch.float16,
)
model.config.pad_token_id = tokenizer.pad_token_id
if not load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
return tokenizer, model
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='bigcode/starcoder', help="")
parser.add_argument('--output_path', type=str, help="")
parser.add_argument('--start_index', type=int, default=0, help="")
parser.add_argument('--end_index', type=int, default=164, help="")
parser.add_argument('--temperature', type=float, default=0.8, help="")
parser.add_argument('--N', type=int, default=200, help="")
parser.add_argument('--max_len', type=int, default=512, help="")
parser.add_argument('--decoding_style', type=str, default='sampling', help="")
parser.add_argument('--num_seqs_per_iter', type=int, default=50, help='')
parser.add_argument('--overwrite', action='store_true', help='')
parser.add_argument('--mbpp_path', type=str, help="")
args = parser.parse_args()
argsdict = vars(args)
print(pprint.pformat(argsdict))
STOP_SEQS = ['\nclass', '\ndef', '\n#', '\nif', '\nprint']
problems = read_mbpp(args.mbpp_path)
task_ids = sorted(problems.keys())[args.start_index: args.end_index]
prompts = []
for task_id in task_ids:
prompt = f"\n{problems[task_id]['text']}\nTest examples:"
if task_id == 493:
# The test examples are too long. We choose to only include the function name.
test_example = problems[task_id]['test_list'][0]
prompt += f"\ncalculate_polygons(startx, starty, endx, endy, radius)"
else:
for test_example in problems[task_id]['test_list']:
prompt += f"\n{test_example}"
prompts.append(prompt)
num_samples = len(prompts)
print("Number of samples: {}".format(num_samples))
tokenizer, model = get_model(base_model=args.model)
generation_config = GenerationConfig(
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
temperature=args.temperature,
max_length=args.max_len,
num_return_sequences=args.num_seqs_per_iter,
eos_token_id=tokenizer.eos_token_id,
top_p=0.95
)
print(f"Loaded {args.model}.")
for i in tqdm(range(num_samples), ncols=0, total=num_samples):
output_file = args.output_path + '/{}.jsonl'.format(args.start_index + i)
if os.path.exists(output_file) and not args.overwrite:
print(f'Skip {output_file} as it already exists')
continue
prompt = prompts[i].replace(' ', '\t')
prompt_batch = [generate_prompt(prompt)]
ids_batch = [task_ids[i]]
completion_seqs = []
encoding = tokenizer(prompt_batch, return_tensors="pt", truncation=True, max_length=args.max_len).to(device)
if args.decoding_style == 'sampling':
loops = int(args.N / args.num_seqs_per_iter)
else:
loops = 1
for _ in tqdm(range(loops), total=loops, leave=False, ncols=0):
with torch.no_grad():
if args.decoding_style == 'sampling':
gen_tokens = model.generate(
**encoding,
generation_config=generation_config
)
if gen_tokens is not None:
gen_seqs = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
else:
gen_seqs = None
if gen_seqs is not None:
assert len(ids_batch) == 1
task_id = ids_batch[0]
for seq_idx, gen_seq in enumerate(gen_seqs):
completion_seq = gen_seq.split("### Response:")[-1]
completion_seq = completion_seq.replace('\t', ' ')
all_code = gen_seq.replace('\t', ' ')
completion_seqs.append(
{'task_id': task_id,
'completion': completion_seq,
'all_code': all_code,
}
)
print("Saving results to {}".format(output_file))
write_jsonl(output_file, completion_seqs)
if __name__ == '__main__':
main() |