507 lines
17 KiB
Python
507 lines
17 KiB
Python
import os
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import datasets
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import torch
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import math
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import random
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from itertools import chain
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from typing import Dict, Tuple
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from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
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import numpy as np
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from torch.utils.data import BatchSampler
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import copy
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class MeaningMap:
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def __init__(self, size=1048576, vocab_size=4096, max_subitem=10, use_cache=True):
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self.size = size
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self.vocab_size = vocab_size
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self.max_subitem = max_subitem
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path = "./data/"
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file = "structured_language_" + str(size) + "_" + str(vocab_size) + "_" + str(max_subitem)
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file = path + file + ".npz"
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if not os.path.exists(path):
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os.mkdir(path)
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if os.path.exists(file) and use_cache:
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print("Load from disk cache: " + file)
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loaded = np.load(file)
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slhwm = loaded["slhwm"]
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dlra = loaded["dlra"]
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self.ms_map = slhwm[:, 4:]
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self.ms_data = dlra[:, 0]
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self.ms_start = slhwm[:, 0]
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self.ms_len = slhwm[:, 1]
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self.ms_level = dlra[:, 1]
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self.ms_rank_idx = dlra[:, 2].astype(np.uint32)
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self.ms_rank_all = dlra[:, 3].astype(np.uint32)
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self.ms_height = slhwm[:, 2]
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self.ms_weight = slhwm[:, 3]
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print("Load end")
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else:
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print("Disk cache miss, build new one.")
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map = np.empty((size, max_subitem), dtype=np.int32)
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index = np.arange(0, size)
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map = np.random.random((size, max_subitem))
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mask_zero = map.copy()
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mask_zero[:, 0] = 0.0
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mask_zero.sort(axis=1)
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thre = np.random.random((size)).reshape(-1, 1).repeat(max_subitem, axis=1)
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mask_zero = mask_zero > thre
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item_sum = map.sum(axis=1)
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scale = (index / item_sum).reshape(-1, 1).repeat(max_subitem, axis=1)
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map = (map * scale).astype(np.int32)
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map[mask_zero] = -1
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map[:vocab_size, 0] = np.arange(0, vocab_size)
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map[:vocab_size, 1:] = -1
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ms_data = [] # meaning sequence
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ms_level = [] # meaning level, vocab's level is 0
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ms_rank_idx = [] # meaning index of all level
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ms_rank_all = [] # meaning all of all level
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ms_start = [] # meaning sequence start
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ms_len = [] # meaning sequence length
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ms_height = [] # meaning tree height
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ms_weight = [] # meaning tree weight
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index = 0
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for i in range(self.vocab_size):
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ms_data.append(np.array([i]))
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ms_level.append(np.array([0]))
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ms_rank_idx.append(np.array([0]))
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ms_rank_all.append(np.array([0]))
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ms_start.append(index)
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ms_len.append(1)
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ms_height.append(0)
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ms_weight.append(1)
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index = index + 1
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for i in range(self.vocab_size, size):
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m = map[i]
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m = m[m >= 0] # donot cut off the map such as [0]
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m_list = m.tolist()
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m_len = len(m_list)
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assert m_list, "map list can not be empty list"
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ma = np.concatenate([ms_data[newm] for newm in m_list])
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ml = np.concatenate([ms_level[newm] + 1 for newm in m_list])
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mr = np.concatenate(
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[
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([0xFFFFFFF0 + i] if newm < self.vocab_size else ms_rank_idx[newm] * 16 + i)
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for i, newm in enumerate(m_list)
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]
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)
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mrl = np.concatenate(
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[
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([0xFFFFFFF0 + m_len] if newm < self.vocab_size else ms_rank_all[newm] * 16 + m_len)
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for i, newm in enumerate(m_list)
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]
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)
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# ml = ml[ma > 0] # cut off the 0 token, such as [12,32,0,42,32]
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# mr = mr[ma > 0]
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# mrl = mrl[ma > 0]
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# ma = ma[ma > 0]
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ms_data.append(ma)
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ms_level.append(ml)
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ms_rank_idx.append(mr)
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ms_rank_all.append(mrl)
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ms_start.append(index)
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ms_len.append(len(ma))
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ms_height.append(max([ms_height[sub_m] for sub_m in m_list]) + 1)
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ms_weight.append(sum(ms_weight[sub_m] for sub_m in m_list))
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index = index + len(ma)
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ms_data = np.array(list(chain(*ms_data))).astype(np.int32)
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ms_level = np.array(list(chain(*ms_level))).astype(np.int32)
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ms_rank_idx = np.array(list(chain(*ms_rank_idx))).astype(np.uint32)
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ms_rank_all = np.array(list(chain(*ms_rank_all))).astype(np.uint32)
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d = np.ones(ms_rank_idx.shape, dtype=np.uint32)
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d = ((d * 0xFFFFFFFF) << (ms_level * 4)).astype(np.uint32)
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ms_rank_idx = (
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((ms_rank_idx & 0xF) << 28)
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+ ((ms_rank_idx & 0xF0) << 20)
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+ ((ms_rank_idx & 0xF00) << 12)
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+ ((ms_rank_idx & 0xF000) << 4)
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+ ((ms_rank_idx & 0xF0000) >> 4)
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+ ((ms_rank_idx & 0xF00000) >> 12)
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+ ((ms_rank_idx & 0xF000000) >> 20)
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+ ((ms_rank_idx & 0xF0000000) >> 28)
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)
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ms_rank_idx = ((ms_rank_idx >> ((8 - ms_level) * 4)) + d).astype(np.uint32)
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ms_rank_all = (
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((ms_rank_all & 0xF) << 28)
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+ ((ms_rank_all & 0xF0) << 20)
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+ ((ms_rank_all & 0xF00) << 12)
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+ ((ms_rank_all & 0xF000) << 4)
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+ ((ms_rank_all & 0xF0000) >> 4)
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+ ((ms_rank_all & 0xF00000) >> 12)
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+ ((ms_rank_all & 0xF000000) >> 20)
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+ ((ms_rank_all & 0xF0000000) >> 28)
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)
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ms_rank_all = ((ms_rank_all >> ((8 - ms_level) * 4)) + d).astype(np.uint32)
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ms_start = np.array(ms_start).astype(np.int32)
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ms_height = np.array(ms_height).astype(np.int32)
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ms_weight = np.array(ms_weight).astype(np.int32)
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ms_len = np.array(ms_len).astype(np.int32)
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slhwm = np.concatenate(
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(
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ms_start.reshape((-1, 1)),
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ms_len.reshape((-1, 1)),
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ms_height.reshape((-1, 1)),
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ms_weight.reshape((-1, 1)),
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map,
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),
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axis=1,
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)
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dlra = np.stack((ms_data, ms_level, ms_rank_idx.astype(np.int32), ms_rank_all.astype(np.int32)), axis=1)
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np.savez(file, slhwm=slhwm, dlra=dlra)
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self.ms_data = ms_data # map[i]=ms_data[ms_start[i]:ms_start[i]+ms_len[i]]
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self.ms_level = ms_level
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self.ms_rank_idx = ms_rank_idx
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self.ms_rank_all = ms_rank_all
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self.ms_map = map # ms_map[i] = [sub(i),sub(i),sub(i),sub(i)...sub(i)]
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self.ms_start = ms_start
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self.ms_len = ms_len
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self.ms_height = ms_height
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self.ms_weight = ms_weight
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print("Disk cache build end.")
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def get_sequence(self, meaning): # return sequence[meaning]
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start = self.ms_start[meaning]
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len = self.ms_len[meaning]
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return (
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self.ms_data[start : start + len],
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self.ms_level[start : start + len],
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self.ms_rank_idx[start : start + len],
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self.ms_rank_all[start : start + len],
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)
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def get_tree(self, meaning): # return meaning all sub items
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tree = {}
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ms = self.ms_map[meaning]
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for m in ms[ms > 0].tolist():
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tree[m] = self.get_tree(m) if m >= self.vocab_size else m
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return tree
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def max_length(self):
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return max(self.ms_len)
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def get_tree_str(tree, prefix):
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if isinstance(tree, dict):
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base = ""
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last_is_dict = None
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for key, value in tree.items():
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new_prefix = (len(str(key)) + 2) * " " + prefix
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dict_string = MeaningMap.get_tree_str(value, new_prefix)
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if dict_string:
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base += "\n" + prefix + str(key) + ": " + dict_string
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last_is_dict = True
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else:
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base += "\n" + prefix + str(key) + " " if last_is_dict else str(key) + " "
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last_is_dict = False
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return base
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return None
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def token_frequency(tree, freq):
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if isinstance(tree, dict):
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for key, value in tree.items():
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if key in freq:
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freq[key] = freq[key] + 1
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else:
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freq[key] = 1
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MeaningMap.token_frequency(value, freq)
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class MeaningDataset(Dataset):
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def __init__(
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self,
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start,
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end,
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size,
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vocab_size,
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max_subitem=10,
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min_seq_len=2,
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seed=42,
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use_cache=True,
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):
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np.random.seed(seed)
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map = MeaningMap(size=end, vocab_size=vocab_size, max_subitem=max_subitem, use_cache=use_cache)
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np.random.seed(seed)
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self.tree = []
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self.seq = []
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self.level = []
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self.rank_idx = []
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self.rank_all = []
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self.seq_meaning = []
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self.m_height = map.ms_height
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self.m_weight = map.ms_weight
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meanings = np.random.randint(start, end, size=(size))
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seq_len = []
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for m in meanings:
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d, l, i, a = map.get_sequence(m)
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if len(d) >= min_seq_len:
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self.tree.append({m: map.get_tree(m)})
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self.seq.append(d)
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self.level.append(l)
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self.rank_idx.append(i)
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self.rank_all.append(a)
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self.seq_meaning.append(m)
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seq_len.append(len(d))
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unique, counts = np.unique(seq_len, return_counts=True)
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print("----------------------------------------------------------------")
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print("MeaningDataset start:" + str(start) + " end:" + str(end) + " space:" + str(end - start))
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print("MeaningDataset size:" + str(len(seq_len)))
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print("MeaningDataset max sequence length:" + str(max(unique)))
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print("MeaningDataset most popular sequence length:" + str(unique[np.argmax(counts)]))
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print("----------------------------------------------------------------")
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def __len__(self):
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return len(self.seq)
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def len(self):
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return len(self.seq)
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def __getitem__(self, idx):
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output = {}
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data = torch.tensor(self.seq[idx]).long()
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output["input_ids"] = data
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output["labels"] = data.clone()
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output["token_type_ids"] = torch.zeros(data.shape)
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output["tree"] = self.tree[idx]
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output["level"] = self.level[idx]
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return output
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def get_batch(self, idx_list): # must equal sequence length
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data = [self.seq[i] for i in idx_list]
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output = {}
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data = torch.tensor(np.stack(data, axis=0)).long()
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output["input_ids"] = data
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output["labels"] = data.clone()
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output["token_type_ids"] = torch.zeros(data.shape)
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output["tree"] = [self.tree[i] for i in idx_list]
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output["level"] = [self.level[i] for i in idx_list]
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return output
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def get_token(self, idx): # must equal sequence length
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return self.seq[idx]
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def get_tree(self, idx):
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return self.tree[idx]
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def print_tree(self, idx):
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tokens = self.seq[idx]
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tree = self.get_tree(idx)
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s = str(tokens) + "\n"
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s += MeaningMap.get_tree_str(tree, "")
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return s
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def copy(self, start, end):
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new = copy.deepcopy(self)
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new.tree = new.tree[start:end]
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new.seq = new.seq[start:end]
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new.level = new.level[start:end]
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new.rank_idx = new.rank_idx[start:end]
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new.rank_all = new.rank_all[start:end]
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new.seq_meaning = new.seq_meaning[start:end]
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return new
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def split(self, ratio):
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l = self.len()
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middle = int(l * ratio)
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return self.copy(0, middle), self.copy(middle, l)
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def token_frequency(self):
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freq = {}
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for t in self.tree:
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MeaningMap.token_frequency(t, freq)
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return freq
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def get_seq_mask(self, idx, level, index):
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assert index < 15, "index must < 15"
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assert level < 8, "level must < 8"
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rank_idx = (self.rank_idx[idx] >> (4 * level)).astype(np.int32) & 0xF
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rank_all = (self.rank_all[idx] >> (4 * level)).astype(np.int32) & 0xF
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return rank_idx == (rank_all + index if index < 0 else index)
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class BatchGroupMeaningDataloader(Dataset):
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def __init__(self, dataset: MeaningDataset, batch_size, shuffle=True, drop_last=True):
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self.dataset = dataset
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self.batch_size = batch_size
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self.drop_last = drop_last
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seq_len = [len(s) for s in dataset.seq]
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unique, counts = np.unique(seq_len, return_counts=True)
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gl = {}
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for u in unique:
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gl[u] = np.where(seq_len == u)[0]
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lens = list(gl.keys())
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gs = {}
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if shuffle:
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for k in gl.keys():
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sl = gl[k].copy()
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np.random.shuffle(sl)
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gs[k] = sl
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else:
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for k in gl.keys():
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sl = gl[k].copy()
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gs[k] = sl
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index = np.zeros((0, batch_size), dtype=np.int64)
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for l in lens:
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batch = len(gs[l]) // batch_size
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new = gs[l][0 : batch * batch_size].reshape(batch, batch_size)
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index = np.concatenate((index, new), axis=0)
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if shuffle:
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index_shuffle = np.arange(0, index.shape[0])
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np.random.shuffle(index_shuffle)
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index = index[index_shuffle]
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self.indexBatch = index
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print("Dataloader batch size:" + str(batch_size) + " count:" + str(len(index)))
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print("Dataloader total:" + str(len(seq_len)) + " drop:" + str(len(seq_len) - len(index) * batch_size))
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def __len__(self):
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return len(self.indexBatch)
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def __getitem__(self, idx):
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return self.dataset.get_batch(self.indexBatch[idx])
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def get_tree(self, idx):
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return [self.dataset.get_tree(i) for i in self.indexBatch[idx]]
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def print_tree(self, idx):
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idx_list = self.indexBatch[idx]
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s = "--------------------------------------------------------\n"
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for i in idx_list:
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s += self.dataset.print_tree(i)
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s += "--------------------------------------------------------\n"
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return s
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if __name__ == "__main__":
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md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, use_cache=False)
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train, val = md.split(0.95)
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fdaf = md.__getitem__(920)
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print(md.print_tree(920))
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print(md.rank_idx[920])
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print(md.rank_all[920])
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mask = md.get_seq_mask(920, 0, -1)
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print(mask)
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mask = md.get_seq_mask(920, 1, 0)
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print(mask)
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mask = md.get_seq_mask(920, 1, -1)
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print(mask)
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mask = md.get_seq_mask(920, 1, 1)
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print(mask)
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assert all(
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np.equal(
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mask[0:57],
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np.array(
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[
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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False,
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False,
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False,
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False,
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False,
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False,
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True,
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False,
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False,
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False,
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False,
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False,
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False,
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False,
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True,
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True,
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True,
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True,
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False,
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False,
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False,
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False,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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True,
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False,
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False,
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False,
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False,
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False,
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False,
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]
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),
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)
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), "False"
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freq = md.token_frequency()
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dl = BatchGroupMeaningDataloader(train, 2)
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# length = len(dl)
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# it = iter(dl)
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# ne1 = next(it)
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# ne2 = next(it)
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# ne3 = next(it)
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# map1 = dl.get_tree(0)
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# map2 = dl.get_tree(1)
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# print(dl.print_tree(0))
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|
|
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# dl = DataLoader(
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# train,
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|
# num_workers=1,
|
|
# persistent_workers=True,
|
|
# shuffle=False,
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|
# )
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|
# it = iter(dl)
|
|
# ne1 = next(it)
|
|
# ne2 = next(it)
|
|
# ne3 = next(it)
|
|
|
|
# for i in range(10):
|
|
# print(next(it)["input_ids"].numpy().tolist())
|