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"""
Get stats of a dataset.
Usage: python3 -m fastchat.data.get_stats --in sharegpt.json
"""
import argparse
from concurrent.futures import ProcessPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
K = 1e3
M = 1e6
def tokenize_one_sample(c):
for i in range(len(c["conversations"])):
v = c["conversations"][i]["value"]
c["conversations"][i]["value"] = tokenizer.tokenize(v)
return c
def tokenize_dataset(content):
processed = []
with ProcessPoolExecutor() as executor:
for result in tqdm(
executor.map(tokenize_one_sample, content), total=len(content)
):
processed.append(result)
return processed
def compute_stats(content):
sample_lens = []
sample_turns = []
prompt_lens = []
res_lens = []
for c in content:
sample_len = 0
sample_turns.append(len(c["conversations"]) // 2)
for i in range(len(c["conversations"]) // 2):
p = c["conversations"][i * 2]["value"]
r = c["conversations"][i * 2 + 1]["value"]
turn_len = len(p) + len(r)
sample_len += turn_len
prompt_lens.append(len(p))
res_lens.append(len(r))
sample_lens.append(sample_len)
return sample_lens, sample_turns, prompt_lens, res_lens
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--in-file", type=str)
parser.add_argument(
"--model-name-or-path", type=str, default="meta-llama/Llama-2-7b-chat-hf"
)
args = parser.parse_args()
content = json.load(open(args.in_file, "r"))
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=False)
content = tokenize_dataset(content)
sample_lens, sample_turns, prompt_lens, res_lens = compute_stats(content)
print(f"#sequence: {len(content)/K:.2f} K")
print(f"#tokens: {np.sum(sample_lens)/M:.2f} M")
print(f"avg. turns: {np.mean(sample_turns):.2f}")
print(f"avg. prompt length: {np.mean(prompt_lens):.2f}")
print(f"avg. response length: {np.mean(res_lens):.2f}")
print("\n- Histogram -")
bin_edges = [0, 1024, 2048, 4096, 8192, 16384, 32768]
hist = np.histogram(sample_lens, bins=bin_edges)[0]
for i in range(len(hist)):
print(f"L{bin_edges[i]} - {bin_edges[i+1]}: {hist[i]}")
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