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import pandas as pd
import argparse
from transformers import AutoTokenizer
import jsonlines
from tqdm import tqdm
from vllm import LLM, SamplingParams
#Input all the arguments
parser = argparse.ArgumentParser()
parser.add_argument("--data_subset", type = str, default = "latency", help = "type of non-func requirement")
parser.add_argument("--temperature", type = float, default = 0.0, help = "temperature")
parser.add_argument("--max_new_tokens", type = int, default = 8, help = "max length of tokens")
parser.add_argument("--top_p", type = float, default = 0.95, help = "top_p")
parser.add_argument("--prompt", type = str, default = "base_prompt", help = "type of prompt")
parser.add_argument("--num_samples", type = int, default = 1, help = "number of samples")
parser.add_argument("--model_path", type = str, required = True, help = "HF path for OS models")
parser.add_argument("--load_in_8bit", action = "store_true", help = "Load model in 8bit")
parser.add_argument("--load_in_4bit", action = "store_true", help = "Load model in 4bit")
parser.add_argument("--precision", type = str, default = "fp16", help = "Model precision, from: fp32, fp16 or bf16")
parser.add_argument("--tensor_parallel_size", type = int, default = 1, help = "Tensor parallel size")
parser.add_argument("--swap_space", type = int, default = 4, help = "The size (GiB) of CPU memory per GPU to use as swap space.")
parser.add_argument("--batch_size", type = int, default = 1, help = "Number of examples to send to llm engine at once.")
args = parser.parse_args()
argsdict = vars(args)
# Function to extract the classification prediction
def extract_single_predictions(input_string):
if input_string.strip().split()[0].lower() == "A".lower():
return "A"
elif input_string.strip().split()[0].lower() == "B".lower():
return "B"
return None
model_basename = args.model_path.split("/")[-1]
llm_tokenizer = AutoTokenizer.from_pretrained(
args.model_path,
truncation_side="left",
padding_side="right", # padding on the right is needed to cut off padding in `complete_code`
trust_remote_code=True,
)
GREEDY = True
sampling_params = SamplingParams(
n = 1, # for multisamples we sample multiple times
temperature = args.temperature if not GREEDY else 0.0,
top_p = args.top_p if not GREEDY else 1.0,
top_k = 50 if not GREEDY else -1,
max_tokens = args.max_new_tokens,
stop_token_ids = [llm_tokenizer.eos_token_id])
llm = LLM(model = args.model_path,
tensor_parallel_size = args.tensor_parallel_size,
swap_space = args.swap_space,
trust_remote_code = True)
# Initializing variables
dataset_path = os.path.join("datasets", f"{args.data_subset}.jsonl")
args.num_samples = 1
data = []
max_tokens = []
generations = []
left_prompts = []
right_prompts = []
generations = []
with jsonlines.open(dataset_path) as data_file:
for data_item in data_file:
data.append(data_item)
left_prompts.append(data_item["classification_left_prompt"])
right_prompts.append(data_item["classification_right_prompt"])
print("Starting model inference...")
left_llm_outputs = llm.generate(left_prompts, sampling_params)
left_predictions = [extract_single_predictions(output.outputs[0].text) for output in left_llm_outputs]
right_llm_outputs = llm.generate(right_prompts, sampling_params)
right_predictions = [extract_single_predictions(output.outputs[0].text) for output in right_llm_outputs]
for i, data_item in tqdm(enumerate(left_predictions)):
curr_sample = data[i]
curr_sample["left_output"] = left_predictions[i]
curr_sample["right_output"] = right_predictions[i]
for prompt in ["base_prompt", "coding_concepts", "chain_of_thought", "one_shot", "classification_left_prompt", "classification_right_prompt"]:
if(prompt in curr_sample):
del curr_sample[prompt]
generations.append(curr_sample)
# Saving the generations
generations = pd.DataFrame(generations)
path = os.path.join("generations", "classification", args.data_subset, os.path.split(args.model_path)[1], args.prompt, f"{args.num_samples}_samples")
if not os.path.exists(path):
os.makedirs(path)
path = os.path.join(path, "generated_outputs.jsonl")
generations.to_json(path, orient = "records", lines = True) |