428 lines
15 KiB
Python
428 lines
15 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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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
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file_slhwm = file + "_slhwm" + ".npy"
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file_dli = file + "_dli" + ".npy"
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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_slhwm) and os.path.exists(file_dli) and use_cache:
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print("Load from disk cache: " + file)
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slhwm = np.load(file_slhwm)
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dli = np.load(file_dli)
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self.ms_map = slhwm[:, 4:]
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self.ms_data = dli[:, 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 = dli[:, 1]
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self.ms_idx = dli[:, 2].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_idx = [] # meaning index of lowest 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_idx.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]
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m_list = m.tolist()
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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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mi = np.concatenate(
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[
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([0xFFFFFFF0 + i] if newm < self.vocab_size else ms_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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ml = ml[ma > 0]
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mi = mi[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_idx.append(mi)
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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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# offsets = [0, 0, 4, 8, 12, 16, 20, 24, 28]
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# for idxmi, mi in enumerate(ms_idx):
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# level = ms_level[idxmi]
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# for idxnum, num in enumerate(mi):
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# l = level[idxnum]
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# elements = [(num >> offset) & 0xF for offset in offsets[l:0:-1]]
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# num = (num >> (l * 4)) << (l * 4)
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# num += sum(elem << (i * 4) for i, elem in enumerate(elements))
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# mi[idxnum] = num
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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_idx = np.array(list(chain(*ms_idx))).astype(np.uint32)
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d = np.ones(ms_idx.shape, dtype=np.uint32)
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d = ((d * 0xFFFFFFFF) << (ms_level * 4)).astype(np.uint32)
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ms_idx = (
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((ms_idx & 0xF) << 28)
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+ ((ms_idx & 0xF0) << 20)
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+ ((ms_idx & 0xF00) << 12)
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+ ((ms_idx & 0xF000) << 4)
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+ ((ms_idx & 0xF0000) >> 4)
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+ ((ms_idx & 0xF00000) >> 12)
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+ ((ms_idx & 0xF000000) >> 20)
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+ ((ms_idx & 0xF0000000) >> 28)
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)
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ms_idx = ((ms_idx >> ((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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dli = np.stack((ms_data, ms_level, ms_idx.astype(np.int32)), axis=1)
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np.save(file_slhwm, slhwm)
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np.save(file_dli, dli)
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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_data = ms_data # map[i]=ms_data[ms_start[i]:ms_start[i]+ms_len[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_level = ms_level
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self.ms_idx = ms_idx
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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 self.ms_data[start : start + len], self.ms_level[start : start + len], self.ms_idx[start : start + len]
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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=131072,
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end=1048576,
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size=32768,
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vocab_size=4096,
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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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data=None,
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length=None,
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tree=None,
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level=None,
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idx=None,
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use_cache=True,
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):
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if data != None and length != None and tree != None and level != None and idx != None:
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self.data = data
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self.length = length
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self.tree = tree
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self.level = level
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self.idx = idx
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return
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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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self.tree = []
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self.data = []
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self.level = []
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self.idx = []
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self.length = []
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meanings = np.random.randint(start, end, size=(size))
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for m in meanings:
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d, l, i = 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.data.append(d)
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self.level.append(l)
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self.idx.append(i)
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self.length.append(len(d))
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unique, counts = np.unique(self.length, 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(self.length)))
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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.data)
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def len(self):
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return len(self.data)
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def __getitem__(self, idx):
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output = {}
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data = torch.tensor(self.data[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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output["idx"] = self.idx[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.data[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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output["idx"] = [self.idx[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.data[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.data[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 split(self, ratio):
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l = len(self.data)
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middle = int(l * ratio)
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d_shuffle = self.data.copy()
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l_shuffle = self.length.copy()
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m_shuffle = self.tree.copy()
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level_shuffle = self.level.copy()
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i_shuffle = self.idx.copy()
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md1 = MeaningDataset(
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data=d_shuffle[:middle],
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length=l_shuffle[:middle],
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tree=m_shuffle[:middle],
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level=level_shuffle[:middle],
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idx=i_shuffle[:middle],
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)
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md2 = MeaningDataset(
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data=d_shuffle[middle:],
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length=l_shuffle[middle:],
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tree=m_shuffle[middle:],
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level=level_shuffle[middle:],
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idx=i_shuffle[middle:],
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)
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return md1, md2
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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(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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return [((int(i / (16**level)) & 0xF) == index) for i in idx]
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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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length = dataset.length
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unique, counts = np.unique(length, return_counts=True)
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gl = {}
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for u in unique:
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gl[u] = np.where(length == 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(length)) + " drop:" + str(len(length) - 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=128, 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.idx[920])
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fdasfe = MeaningDataset.get_seq_mask(md.idx[920], 1, 1)
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print(fdasfe)
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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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dl = DataLoader(
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train,
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num_workers=1,
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persistent_workers=True,
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shuffle=False,
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)
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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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for i in range(10):
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daf = next(it)["input_ids"].numpy().tolist()
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print(daf)
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