transformersprofiling/generating_common_ngrams.py at main · YangZhou08/transformersprofiling · GitHub

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from collections import Counter
import re

import torch
import argparse
# import contexttimer

import datasets
from datasets import load_dataset

from src.transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
from src.transformers import GPTNeoXForCausalLM
from src.transformers import LlamaConfig, LlamaPreTrainedModel

from tqdm import tqdm
# from sampling.utils import norm_logits, sample

import torch.nn.functional as F

from src.transformers.generation.logits_process import LogitsProcessorList
import time
import numpy as np

from termcolor import colored
from src.transformers import Trainer, TrainingArguments
from torch import nn
from src.transformers import DataCollatorForLanguageModeling
from src.transformers.generation.utils import GenerationConfig
from src.transformers.models.llama.modeling_llama import LlamaForCausalLM, SimpleSmallModel
import time

from torch.utils.data import DataLoader

import socket

hostname = socket.gethostname()
print("Hostname:", hostname)

if "lovelace" in hostname:
    # cache_dir = "/home/bc20/yang/transformersprofiling"
    datasetsrc = "/home/yangzho6/c4_parts/downloads/c4_file2.json"
    dir_models = "/home/yangzho6/model_checkpoints"
    synthesized_dir_path = "/home/yangzho6/c4llm_synthesized/"
    synthesized_data_path = "/home/yangzho6/c4llm_synthesized/tensor_dir/"
elif "ada" in hostname:
    # cache_dir = "/home/bc20/yang/transformersprofiling"
    datasetsrc = "/home/beidic/yangzho6/c4_parts/downloads/c4_file2.json"
    dir_models = "/home/beidic/yangzho6/model_checkpoints"
    synthesized_dir_path = "/home/beidic/yangzho6/c4llm_synthesized/"
    # synthesized_data_path = "/home/beidic/yangzho6/c4llm_synthesized/tensor_dir/"
    synthesized_data_path = "/home/beidic/yangzho6/c4llm_synthesized/tensor_dir2/"
else:
    # cache_dir = "/home/bc20/yang/transformersprofiling"
    dir_models = "/home/yangzho6/model_checkpoints"
    synthesized_dir_path = "/home/yangzho6/c4llm_synthesized/"
    synthesized_data_path = "/home/yangzho6/c4llm_synthesized/tensor_dir/"

from termcolor import colored
import json

torch_device = "cuda:0"

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", cache_dir = dir_models)

dataset = load_dataset('json', data_files = datasetsrc, split = "train")

def generate_ngrams(tokens, n=3):
    return zip(*[tokens[i:] for i in range(n)])

def count_batch(batch):
    batch_counter = Counter()
    # print(len(batch["text"]))
    for text in batch["text"]:
        tokens = tokenizer.tokenize(text)
        three_ngrams = zip(*[tokens[i:] for i in range(3)])
        three_ngrams = list(three_ngrams)
        batch_counter.update(three_ngrams)

    ngrams_counts = [(ngram, count) for ngram, count in batch_counter.items()]
    # print(len(ngrams_counts))
    # ngram, count = ngrams_counts[0]
    ngrams, counts = zip(*ngrams_counts)
    ngrams = list(ngrams)
    counts = list(counts)
    # print(len(ngrams), len(counts))
    # ngrams, counts = zip(*ngrams_counts) if ngrams_counts else ([], [])
    # ngrams = list(ngrams)
    # counts = list(counts)

    # return {"ngrams_counts": [ngrams_counts] * 100}
    # print(len(batch["text"]))
    return {"ngrams": [ngrams] * len(batch["text"]), "counts": [counts] * len(batch["text"])}
    ngram_list = [ngrams] * len(batch["text"])
    print(len(ngram_list))
    print(len(ngram_list[0]))
    counts_list = [counts] * len(batch["text"])
    print(len(counts_list[0]))
    return {"ngrams": ngram_list, "counts": counts_list}

num_pros = 8
# batch_size = 250
batch_size = 100
datalength = (len(dataset) // (num_pros * batch_size)) * (num_pros * batch_size)
dataset = dataset.select(range(datalength))

batched_counts = dataset.map(
    count_batch,
    batched = True,
    num_proc = num_pros,
    batch_size = batch_size,
)

total_counts = Counter()
total_length = len(batched_counts)
num_batch = total_length // (num_pros * batch_size)
remaining_length = total_length % (num_pros * batch_size)
size_stride = [batch_size] * (num_batch * num_pros)
if remaining_length > 0:
    for i in range(num_pros):
        size_stride.append(remaining_length//4)
print(size_stride)
index = 0
for idx in tqdm(size_stride):
    # print(index)
    ngrams = batched_counts[index]["ngrams"]  # Access the first (and only) element in the list
    counts = batched_counts[index]["counts"]  # Access the first (and only) element in the list
    # print(len(ngrams), len(counts))

    # Update the total counts
    for j in range(len(ngrams)):
        ngram = ngrams[j]
        # print(len(ngram))
        ngram = tuple(ngram)
        count = counts[j]
        # print(len(ngram))
        # for ngram, count in zip(ngrams, counts):
        total_counts[ngram] += count
    index += idx
    # print(total_counts.most_common(200))

# Now total_counts has the aggregated count of all ngrams
most_common_3grams = total_counts.most_common(1000)
print(most_common_3grams)
file_path = "file1_1000_most_common_3grams.json"

counter_dict = dict(total_counts)
with open(file_path, "w", encoding = "utf-8") as f:
    # json.dump(most_common_3grams, f, ensure_ascii = False, indent = 4)
    json.dump(counter_dict, f, ensure_ascii = False, indent = 4)

'''
print("checking with the sequential implementation")
sequential_counts = Counter()
for text in tqdm(dataset["text"]):
    tokens = tokenizer.tokenize(text)
    three_ngrams = zip(*[tokens[i:] for i in range(3)])
    three_ngrams = list(three_ngrams)
    sequential_counts.update(three_ngrams)

print(sequential_counts.most_common(200))
'''