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