Witllm/wit/meaning_dataset.py

import os
import datasets
import torch
import math
import random
from itertools import chain
from typing import Dict, Tuple
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
import numpy as np
from torch.utils.data import BatchSampler


class MeaningMap:  # 16777216 1048576 8192

    def __init__(self, size=1048576, vocab_size=4096, max_subitem=10):
        self.size = size
        self.vocab_size = vocab_size
        self.max_subitem = max_subitem

        path = "./data/"
        file = "structured_language_" + str(size) + "_" + str(vocab_size) + "_" + str(max_subitem)
        file = path + file
        file_start = file + "_start" + ".npy"
        file_len = file + "_len" + ".npy"
        file_data = file + "_data" + ".npy"

        if not os.path.exists(path):
            os.mkdir(path)
        if os.path.exists(file_start) and os.path.exists(file_len) and os.path.exists(file_data):
            print("Load from disk cache: " + file)
            self.ms_data = np.load(file_data)
            self.ms_start = np.load(file_start)
            self.ms_len = np.load(file_len)
            print("Load end")
        else:
            print("Disk cache miss, build new one.")

            mm = np.empty((size, max_subitem), dtype=np.int32)

            index = np.arange(0, size)
            mm = np.random.random((size, max_subitem))

            mask_zero = mm.copy()
            mask_zero[:, 0] = 0.0
            mask_zero.sort(axis=1)
            thre = np.random.random((size)).reshape(-1, 1).repeat(max_subitem, axis=1)
            mask_zero = mask_zero > thre

            item_sum = mm.sum(axis=1)
            scale = (index / item_sum).reshape(-1, 1).repeat(max_subitem, axis=1)
            mm = mm * scale
            mm[mask_zero] = 0

            mm[:vocab_size, 0] = np.arange(0, vocab_size)
            mm[:vocab_size, 1:] = 0
            mm = mm.astype(np.int32)

            ms = []  # meaning sequence
            ms_start = []  # meaning sequence start
            ms_len = []  # meaning sequence length
            index = 0
            for i in range(self.vocab_size):
                ms.append([i])
                ms_start.append(index)
                ms_len.append(1)
                index = index + 1

            for i in range(self.vocab_size, size):
                m = mm[i]
                m = m[m > 0]
                ma = []
                for newm in m.tolist():
                    ma = ma + ms[newm]
                ms.append(ma)
                ms_start.append(index)
                ms_len.append(len(ma))
                index = index + len(ma)

            ms_data = list(chain(*ms))
            np.save(file_data, np.array(ms_data).astype(np.int32))
            np.save(file_start, np.array(ms_start).astype(np.int32))
            np.save(file_len, np.array(ms_len).astype(np.int32))

            self.ms_data = ms_data
            self.ms_start = ms_start
            self.ms_len = ms_len
            print("Disk cache build end.")

    def GetSequence(self, meaning):
        start = self.ms_start[meaning]
        len = self.ms_len[meaning]
        return self.ms_data[start : start + len]

    def MaxLength(self):
        return max(self.ms_len)


class MeaningDataset(Dataset):

    def __init__(
        self,
        start=131072,
        end=1048576,
        size=32768,
        vocab_size=4096,
        max_subitem=10,
        min_seq_len=2,
        seed=42,
        data=None,
        length=None,
    ):
        if data != None and length != None:
            self.data = data
            self.length = length
            return
        np.random.seed(seed)
        mm = MeaningMap(size=end, vocab_size=vocab_size, max_subitem=max_subitem)  # 1048576
        self.data = []
        self.length = []
        meanings = np.random.randint(start, end, size=(size))
        for m in meanings:
            sq = mm.GetSequence(m)
            if len(sq) >= min_seq_len:
                self.data.append(sq)
                self.length.append(len(sq))

        unique, counts = np.unique(self.length, return_counts=True)
        print("----------------------------------------------------------------")
        print("MeaningDataset start:" + str(start) + " end:" + str(end) + " space:" + str(end - start))
        print("MeaningDataset size:" + str(len(self.length)))
        print("MeaningDataset max sequence length:" + str(max(unique)))
        print("MeaningDataset most popular sequence length:" + str(unique[np.argmax(counts)]))
        print("----------------------------------------------------------------")

    def __len__(self):
        return len(self.data)

    def len(self):
        return len(self.data)

    def __getitem__(self, idx):
        output = {}
        data = torch.tensor(self.data[idx]).long()
        output["input_ids"] = data
        output["labels"] = data.clone()
        output["token_type_ids"] = torch.zeros(data.shape)
        return output

    def GetBatch(self, index_list):  # must equal sequence length
        data = [self.data[i] for i in index_list]
        output = {}
        data = torch.tensor(np.stack(data, axis=0)).long()
        output["input_ids"] = data
        output["labels"] = data.clone()
        output["token_type_ids"] = torch.zeros(data.shape)
        return output

    def Split(self, ratio):
        l = len(self.data)
        middle = int(l * ratio)
        d_shuffle = self.data.copy()
        l_shuffle = self.length.copy()
        md1 = MeaningDataset(data=d_shuffle[:middle], length=l_shuffle[:middle])
        md2 = MeaningDataset(data=d_shuffle[middle:], length=l_shuffle[middle:])
        return md1, md2


class BatchGroupMeaningDataloader(Dataset):

    def __init__(self, dataset: MeaningDataset, batch_size, shuffle=True, drop_last=True):
        self.dataset = dataset
        self.batch_size = batch_size
        self.drop_last = drop_last

        length = dataset.length
        unique, counts = np.unique(length, return_counts=True)
        gl = {}
        for u in unique:
            gl[u] = np.where(length == u)[0]

        lens = list(gl.keys())
        gs = {}
        if shuffle:
            for k in gl.keys():
                sl = gl[k].copy()
                np.random.shuffle(sl)
                gs[k] = sl
        else:
            for k in gl.keys():
                sl = gl[k].copy()
                gs[k] = sl

        index = np.zeros((0, batch_size), dtype=np.int64)
        for l in lens:
            batch = len(gs[l]) // batch_size
            new = gs[l][0 : batch * batch_size].reshape(batch, batch_size)
            index = np.concatenate((index, new), axis=0)
        if shuffle:
            index_shuffle = np.arange(0, index.shape[0])
            np.random.shuffle(index_shuffle)
            index = index[index_shuffle]
        self.indexBatch = index
        print("Dataloader batch size:" + str(batch_size) + " count:" + str(len(index)))
        print("Dataloader total:" + str(len(length)) + " drop:" + str(len(length) - len(index) * batch_size))

    def __len__(self):
        return len(self.indexBatch)

    def __getitem__(self, idx):
        # print("get idx" + str(idx))
        return self.dataset.GetBatch(self.indexBatch[idx])


if __name__ == "__main__":

    md = MeaningDataset(4096, 8100, size=1024)
    train, val = md.Split(0.95)

    dl = BatchGroupMeaningDataloader(train, 32)
    length = len(dl)
    it = iter(dl)
    ne1 = next(it)
    ne2 = next(it)
    ne3 = next(it)

    dl = DataLoader(
        train,
        num_workers=1,
        persistent_workers=True,
        shuffle=False,
    )
    it = iter(dl)
    ne1 = next(it)
    ne2 = next(it)
    ne3 = next(it)

    for i in range(10):
        daf = next(it)["input_ids"].numpy().tolist()

        print(daf)