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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import json, math, random, os, sys
import numpy as np
import torch
from torch.utils.data import Dataset
from pytorch_lightning.utilities import rank_zero_info
from .binidx import MMapIndexedDataset
from .utils import MaybeIsPrime
class MyDataset(Dataset):
def __init__(self, args):
self.args = args
if args.data_type == "binidx":
self.vocab_size = args.vocab_size
rank_zero_info(f"Current vocab size = {self.vocab_size} (make sure it's correct)")
if args.my_pile_version == 1:
self.data = MMapIndexedDataset(args.data_file)
self.data_size = len(self.data._bin_buffer) // self.data._index._dtype_size
rank_zero_info(f"Data has {self.data_size} tokens.")
else:
data_list = open(args.data_file, "r", encoding='utf-8').read().strip().split('\n')
data_list = [i.strip().split(' ') for i in data_list]
self.data = []
self.data_size = int(data_list[-1][-1])
rank_zero_info(f"Data has {self.data_size} chunks.")
for d in data_list:
data = MMapIndexedDataset(d[0])
data_size = len(data._bin_buffer) // data._index._dtype_size
assert (data_size - args.ctx_len) == int(d[1])
self.data += [[int(d[-1]), int(d[1]), data]]
# rank_zero_info(self.data)
if args.my_qa_mask > 0:
# self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
self.data_pile = MMapIndexedDataset('/fsx/pile_deduped/pile_0.87_deduped_text_document')
self.data_pile_size = len(self.data_pile._bin_buffer) // self.data._index._dtype_size
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
# assert self.data_size == 332115325534 and self.vocab_size == 50277
self.samples_per_epoch = args.epoch_steps * args.real_bsz
assert self.samples_per_epoch == 40320
rank_zero_info(f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########")
dataset_slot = self.data_size // args.ctx_len
if args.my_pile_stage != 4:
assert MaybeIsPrime(args.magic_prime)
assert args.magic_prime % 3 == 2
assert args.magic_prime / dataset_slot > 0.99 and args.magic_prime / dataset_slot <= 1
elif args.data_type == "numpy":
self.data = np.load(args.data_file).astype("int")
self.vocab_size = args.vocab_size
rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)")
self.data_size = len(self.data)
rank_zero_info(f"Data has {self.data_size} tokens.")
elif args.data_type == "uint16":
self.data = np.fromfile(args.data_file, dtype=np.uint16).astype("int32").reshape(-1, args.my_sample_len)
self.vocab_size = args.vocab_size
rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)")
self.data_size = self.data.shape[0]
rank_zero_info(f"Data has {self.data_size} samples.")
elif args.data_type == "wds_img":
self.vocab_size = -1
self.data_size = -1
self.data = None
self.error_count = 0
else:
if args.data_type == "dummy":
rank_zero_info("Building dummy data...")
self.data = ""
for i in range(100000):
aa = (i) % 10000
bb = (i * i) % 10000
cc = aa + bb
self.data += f".{aa}+{bb}={cc}."
else:
self.data = open(args.data_file, "r", encoding=args.data_type).read()
rank_zero_info("Building token list...")
unique = sorted(list(set(self.data)))
self.vocab_size = len(unique)
# rank_zero_info()
# for u in unique:
# print(u, end=' ')
# rank_zero_info('\n\n')
xx = 0
xxObj = {}
for u in unique:
xxObj[xx] = u
xx += 1
with open(f"{args.proj_dir}/vocab.json", "w", encoding="utf-16le") as vocab_file:
vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
self.data_size = len(self.data)
rank_zero_info(f"Data has {self.data_size} tokens, {self.vocab_size} vocab size.")
self.stoi = {ch: i for i, ch in enumerate(unique)}
self.itos = {i: ch for i, ch in enumerate(unique)}
def __len__(self):
return self.args.epoch_steps * self.args.micro_bsz
def __getitem__(self, idx):
args = self.args
rank = self.global_rank
epoch = self.real_epoch
world_size = self.world_size
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}")
if args.data_type == "wds_img":
def init_wds(self, bias=0):
def identity(x):
return x
import webdataset as wds
import torchvision.transforms as transforms
# img_transform = transforms.Compose(
# [transforms.CenterCrop(256)]
# )
img_transform = transforms.Compose([
transforms.CenterCrop(512),
transforms.Resize((args.my_img_size))
])
self.data_raw = wds.WebDataset(args.data_file, resampled=True).shuffle(10000, initial=1000, rng=random.Random(epoch*100000+rank+bias*1e9)).decode("torchrgb").to_tuple("jpg", "json", "txt").map_tuple(img_transform, identity, identity)
for pp in self.data_raw.pipeline:
if 'Resampled' in str(pp):
pp.deterministic = True
def worker_seed():
return rank*100000+epoch+bias*1e9
pp.worker_seed = worker_seed
self.data = iter(self.data_raw)
# print(f"WebDataset loaded for rank {rank} epoch {epoch}")
if self.data == None:
init_wds(self)
trial = 0
while trial < 10:
try:
dd = next(self.data) # jpg, json, txt
break
except:
print(f'[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]')
self.error_count += 1
init_wds(self, self.error_count)
trial += 1
pass
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size} {dd[2]}")
# with open(f"sample_{rank}.txt", "a", encoding="utf-8") as tmp:
# tmp.write(f"epoch {epoch} idx {idx} rank {rank}/{world_size} {int(dd[1]['key'])}\n")
return dd[0], dd[2]
else:
if args.data_type == "uint16":
i = np.random.randint(0, self.data_size-1)
dix = self.data[i]
x = torch.tensor(dix[:-1], dtype=torch.long)
y = torch.tensor(dix[1:], dtype=torch.long)
else:
ctx_len = args.ctx_len
req_len = ctx_len + 1
magic_prime = args.magic_prime
data = self.data
if args.my_pile_stage > 0:
ii = 1 + epoch * self.samples_per_epoch + (idx * world_size) + rank
if args.my_qa_mask > 0:
ii_orig = ii
if ii % 2 == 0:
ii = -1
data = self.data_pile
else:
ii = ii // 2
if data == self.data_pile:
i = np.random.randint(0, self.data_pile_size - req_len)
else:
if args.my_pile_stage == 4 or ii < args.my_random_steps:
# cheat: pick a random spot in dataset
if args.my_pile_version == 1:
i = np.random.randint(0, self.data_size - req_len)
else:
i = np.random.randint(0, self.data_size)
else:
ii = ii - args.my_random_steps
factor = (math.sqrt(5) - 1) / 2
factor = int(magic_prime * factor)
i = ((factor * ii * ii * ii) % magic_prime) * ctx_len
i = i + args.my_pile_shift
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size} ii {ii} pos {round(i / self.data_size, 3)}")
else:
# cheat: pick a random spot in dataset
i = np.random.randint(0, self.data_size - req_len)
if args.data_type == "binidx":
if args.my_pile_version == 1:
dix = data.get(idx=0, offset=i, length=req_len).astype(int)
else:
# self.data : cutoff, chunk_count, data
for j in range(len(data)):
if i < data[j][0]:
ii = i
i = (i - (data[j-1][0] if j > 0 else 0)) % data[j][1]
dix = data[j][2].get(idx=0, offset=i, length=req_len).astype(int)
# print(ii, j, i)
break
elif args.data_type == "numpy":
dix = data[i : i + req_len]
else:
dix = [self.stoi[s] for s in data[i : i + req_len]]
if args.my_qa_mask == 1:
if data == self.data_pile:
z = [1] * ctx_len
else:
z = [0] * ctx_len
z_sum = 0
isGood = False
for i in range(3, ctx_len):
if dix[i] == 27 and dix[i-1] == 34 and dix[i-2] == 187 and dix[i-3] == 187:
isGood = True
if dix[i] == 0:
isGood = False
if isGood:
z[i] = 1
z_sum += 1
if z_sum == 0:
z = [1] * ctx_len
i = np.random.randint(0, self.data_pile_size - req_len)
dix = self.data_pile.get(idx=0, offset=i, length=req_len).astype(int)
z = torch.tensor(z, dtype=torch.bfloat16)
x = torch.tensor(dix[:-1], dtype=torch.long)
y = torch.tensor(dix[1:], dtype=torch.long)
# if ii_orig < 50:
# # if rank == 1:
# print('rank', rank, 'i', ii_orig, ii, i, 'x', x[:5], '...', x[-5:])
# else:
# exit(0)
if args.my_qa_mask == 1:
return x, y, z
return x, y
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