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inference.py
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from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
import re
import random
import numpy as np
from pathlib import Path
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from tqdm import tqdm
from llama3 import ModelArgs, Transformer, Tokenizer, FunctionLM
from tecton_score import tecton_score_inference
from tecton_generate import tecton_generate_inference
from funchub.math import *
def setup_model_parallel() -> Tuple[int, int]:
local_rank = int(os.environ.get("LOCAL_RANK", -1))
world_size = int(os.environ.get("WORLD_SIZE", -1))
torch.distributed.init_process_group("nccl")
initialize_model_parallel(world_size)
torch.cuda.set_device(local_rank)
return local_rank, world_size
def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int, func_load_path: str, func_dict: dict) -> FunctionLM:
start_time = time.time()
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
assert (
world_size == len(checkpoints)
), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
ckpt_path = checkpoints[local_rank]
print("Loading")
checkpoint = torch.load(ckpt_path, map_location="cpu")
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
model_args: ModelArgs = ModelArgs(max_seq_len=8000, max_batch_size=1, **params)
tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = tokenizer.n_words
torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args).cuda().half()
torch.set_default_tensor_type(torch.FloatTensor)
model.load_state_dict(checkpoint, strict=False)
funcmodel = FunctionLM(model, tokenizer, func_dict = func_dict, load_path=func_load_path)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return funcmodel
def main(ckpt_dir: str, tokenizer_path: str, temperature: float = 0, top_p: float = 0.95, mode: str = "generate", dataset = "funcqa",
return_top: int = 5, logits_bias: float = 0, func_load_path: str = "None", st_idx=0, ed_idx=10000, suffix=""):
# set random seed
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
random.seed(1)
np.random.seed(1)
size = ckpt_dir.split("/")[-1] # size of llama model (eg. 7B, 13B etc)
local_rank, world_size = setup_model_parallel()
if local_rank > 0:
sys.stdout = open(os.devnull, 'w')
templates = {}
if dataset == "gsm8k-xl":
for name in os.listdir("data/gsm8k-xl/template"):
with open(f"data/gsm8k-xl/template/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open(f"data/gsm8k-xl/test.json") as f:
data = [json.loads(line) for line in f.readlines()]
raw_test_cases = [i["question"] for i in data]
enhanced_v = [i["enhanced_v"] for i in data]
test_cases = []
for v, q in zip(enhanced_v, raw_test_cases):
for i in range(len(v)):
q = q.replace(f"{{v_{i+1}}}", str(v[i]))
test_cases.append(q)
max_gen_len = 512
func_dict = json.load(open("data/gsm8k-xl/func_dict.json"))
doc_dict = json.load(open("data/gsm8k-xl/doc_dict.json"))
exemplar_dict = json.load(open("data/gsm8k-xl/exemplar_dict.json"))
setting = "gsm8k"
elif dataset == "svamp-xl":
for name in os.listdir("data/gsm8k-xl/template"):
with open(f"data/gsm8k-xl/template/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open("data/svamp-xl/test.json") as f:
data = json.load(f)
test_cases = [i["question"] for i in data]
max_gen_len = 512
func_dict = json.load(open("data/gsm8k-xl/func_dict.json"))
doc_dict = json.load(open("data/gsm8k-xl/doc_dict.json"))
exemplar_dict = json.load(open("data/gsm8k-xl/exemplar_dict.json"))
setting = "gsm8k"
elif dataset == "mawps-xl":
for name in os.listdir("data/gsm8k-xl/template"):
with open(f"data/gsm8k-xl/template/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open("data/mawps-xl/test.json") as f:
data = json.load(f)
test_cases = [i["question"] for i in data]
max_gen_len = 512
func_dict = json.load(open("data/gsm8k-xl/func_dict.json"))
doc_dict = json.load(open("data/gsm8k-xl/doc_dict.json"))
exemplar_dict = json.load(open("data/gsm8k-xl/exemplar_dict.json"))
setting = "gsm8k"
elif dataset == "asdiv-xl":
for name in os.listdir("data/gsm8k-xl/template"):
with open(f"data/gsm8k-xl/template/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open("data/asdiv-xl/test.json") as f:
data = json.load(f)
test_cases = [i["question"] for i in data]
max_gen_len = 512
func_dict = json.load(open("data/gsm8k-xl/func_dict.json"))
doc_dict = json.load(open("data/gsm8k-xl/doc_dict.json"))
exemplar_dict = json.load(open("data/gsm8k-xl/exemplar_dict.json"))
setting = "gsm8k"
elif dataset == "funcqa_mh":
for name in os.listdir("data/funcqa/template_mh"):
with open(f"data/funcqa/template_mh/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open("data/funcqa/funcqa_mh.json") as f:
data = json.load(f)
test_cases = [i["question"] for i in data]
max_gen_len = 512
func_dict = json.load(open("data/funcqa/func_dict.json"))
doc_dict = json.load(open("data/funcqa/doc_dict.json"))
exemplar_dict = json.load(open("data/funcqa/exemplar_dict.json"))
setting = "funcqa"
elif dataset == "funcqa_oh":
for name in os.listdir("data/funcqa/template_oh"):
with open(f"data/funcqa/template_oh/{name}") as f:
templates[name.split("_")[-1].replace(".txt", "")] = f.read()
with open("data/funcqa/funcqa_oh.json") as f:
data = json.load(f)
max_gen_len = 512
func_dict = json.load(open("data/funcqa/func_dict.json"))
doc_dict = json.load(open("data/funcqa/doc_dict.json"))
exemplar_dict = json.load(open("data/funcqa/exemplar_dict.json"))
test_cases = [i["question"] for i in data]
setting = "funcqa"
funcmodel = load(ckpt_dir, tokenizer_path, local_rank, world_size, func_load_path=func_load_path, func_dict=func_dict) # load the trained model from func_load_path
funcmodel.set_bias(logits_bias) # set the model bias from bias flag
funcmodel.eval()
for case_idx, question in tqdm(enumerate(test_cases), total=len(test_cases)):
if case_idx < st_idx: # can pass start and end indexes as flags to only eval part of the test set
continue
if case_idx >= ed_idx:
break
if mode == "score":
log = tecton_score_inference(
templates, case_idx, question, funcmodel, setting, dataset, doc_dict, exemplar_dict, temperature, top_p, max_gen_len, return_top)
elif mode == "generate":
log = tecton_generate_inference(
templates, case_idx, question, funcmodel, setting, dataset, doc_dict, exemplar_dict, temperature, top_p, max_gen_len, return_top)
if local_rank == 0:
try:
func_model_name = func_load_path.split('/')[-1].split('.')[0]
except:
func_model_name = func_load_path
output_dir = f"outputs/{dataset}"
os.makedirs(output_dir, exist_ok=True)
with open(f"{output_dir}/inference-{size}-{func_model_name}-{mode}-{dataset}-bias_{logits_bias}{suffix}.jsonl", "a") as f:
f.write(json.dumps(log) + "\n")
if __name__ == "__main__":
fire.Fire(main)