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pretrain_coglm.py
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pretrain_coglm.py
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# -*- encoding: utf-8 -*-
'''
@File : pretrain_coglm.py
@Time : 2021/12/30 15:56:40
@Author : Ming Ding
@Contact : dm18@mails.tsinghua.edu.cn
'''
# here put the import lib
import os
import sys
import math
import random
import torch
import argparse
import numpy as np
from icetk import icetk as tokenizer
tokenizer.add_special_tokens(['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
from SwissArmyTransformer import mpu, get_args, get_tokenizer
from SwissArmyTransformer.model.base_model import BaseModel, BaseMixin
from SwissArmyTransformer.training.deepspeed_training import training_main
from SwissArmyTransformer.data_utils import BinaryDataset
def get_batch(data_iterator, args, timers):
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
timers('data loader').start()
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
timers('data loader').stop()
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
tokens, position_ids, labels, loss_masks, attention_mask = [], [], [], [], []
# dispatch text-image, text samples
for sample in tokens_:
pad_masks = (sample == tokenizer['<pad>'])
if pad_masks.any(): # text-image
if random.random() < args.image_caption_ratio:
token, pos, label, attn_mask, loss_mask = make_image_text_understanding(sample)
else:
token, pos, label, attn_mask, loss_mask = make_text_image_generation(sample)
else:
if sample[0] >= tokenizer.num_image_tokens:
token, pos, label, attn_mask, loss_mask = make_text_understanding(sample)
else:
raise ValueError('temporally not support pure image samples')
tokens.append(token)
position_ids.append(pos)
labels.append(label)
attention_mask.append(attn_mask)
loss_masks.append(loss_mask)
tokens = torch.stack(tokens)
position_ids = torch.stack(position_ids)
labels = torch.stack(labels)
attention_mask = torch.stack(attention_mask).unsqueeze(1)
if args.fp16:
attention_mask = attention_mask.half()
loss_masks = torch.stack(loss_masks)
return tokens, position_ids, labels, attention_mask, loss_masks
def make_text_understanding(x, poisson_rate=3, mask_ratio=0.15):
token = x
pos = torch.arange(len(x), device=x.device, dtype=torch.long)
label = x.clone()
label[:-1] = x[1:]
attn_mask = torch.ones(len(x), len(x), device=x.device).tril() # TODO FP16
loss_mask = torch.zeros_like(x)
m = torch.distributions.Poisson(poisson_rate)
span_lens = m.sample(
torch.Size((int(len(x) / poisson_rate * mask_ratio) + 10,))
).clip_(1).long() # +10 to ensure enough
lensum = 0
for i in range(len(span_lens)):
lensum += span_lens[i]
if lensum > len(x) * mask_ratio:
break
lensum -= span_lens[i]
span_lens = span_lens[:i]
prev = sorted(random.sample(range(len(x) - lensum), len(span_lens)))
cum_lens, end_of_last_span = 0, 0
full_attn_slices = []
for i in range(len(prev)):
prev[i] += cum_lens
cum_lens += span_lens[i]
attn_mask[end_of_last_span: prev[i]] = 0
full_attn_slices.append(slice(end_of_last_span, prev[i]))
loss_mask[prev[i]: prev[i] + span_lens[i]] = 1
# the uni-direction region contains the previous one for each span
end_of_last_span = prev[i] + span_lens[i] + 1
for s in full_attn_slices:
attn_mask[:, s] = 1
return token, pos, label, attn_mask, loss_mask
def make_text_image_generation(x):
pad_indices = (x == tokenizer['<pad>']).nonzero().squeeze(1)
if pad_indices.shape[0] == 1 or pad_indices[1] - pad_indices[0] == 1:
# no sep pad, only single language
selected_text_slice = slice(0, pad_indices[0])
unselected_text_slice = None
elif pad_indices[1] - pad_indices[0] > 1:
# select one language at random
selected_text_slice, unselected_text_slice = slice(0, pad_indices[0]), slice(pad_indices[0] + 1, pad_indices[1])
if random.random() < 0.5:
tmp = selected_text_slice
selected_text_slice = unselected_text_slice
unselected_text_slice = tmp
else:
print('warning: non-text image.')
assert len(x) <= 512
token = x.clone()
token[-401] = tokenizer['<start_of_image>']
pos = torch.zeros_like(x)
pos[selected_text_slice] = torch.arange(len(pos[selected_text_slice]), device=x.device)
pos[-401:] = torch.arange(512, 512 + 401, device=x.device)
label = x.clone()
label[:-1] = x[1:]
loss_mask = torch.zeros_like(x)
loss_mask[-401:-1] = 1
attn_mask = torch.zeros(len(x), len(x), device=x.device) # TODO FP16
attn_mask[:, selected_text_slice] = 1
attn_mask[-401:, -401:] = 1
attn_mask[-401:, -401:].tril_()
# whether text unidirectional
# attn_mask[selected_text_slice, selected_text_slice].tril_()
# loss_mask[selected_text_slice.start: selected_text_slice.stop - 1] = 1
return token, pos, label, attn_mask, loss_mask
def make_image_text_understanding(x, patch_size=4, pseudo_mask_ratio=0.8):
token = x.clone()
text_lr = []
pad_indices = (x == tokenizer['<pad>']).nonzero().squeeze(1)
if pad_indices.shape[0] == 1 or pad_indices[1] - pad_indices[0] == 1:
# no sep pad, only single language
text_lr.append((0+1, pad_indices[0]+1))
elif pad_indices[1] - pad_indices[0] > 1:
# both zh and en
text_lr.extend([(0+1, pad_indices[0]+1), (pad_indices[0] + 1+1, pad_indices[1]+1)])
token[1:-400] = x[:-401] # move right to set aside start tokens
for l, r in text_lr:
is_en = (token[l:r] < 83823).all()
token[l-1] = tokenizer['<start_of_english>'] if is_en else tokenizer['<start_of_chinese>']
pos = torch.zeros_like(x)
for l, r in text_lr:
pos[l-1:r] = torch.arange(r-l+1, device=x.device)
pos[-400:] = torch.arange(512 +1, 512 + 401, device=x.device)
label = token.clone()
label[:-1] = token[1:]
loss_mask = torch.zeros_like(x)
attn_mask = torch.zeros(len(x), len(x), device=x.device) # TODO FP16
for l, r in text_lr:
loss_mask[l-1:r-1] = 1
attn_mask[l-1:r, l-1:r] = 1
attn_mask[l-1:r, l-1:r].tril_()
attn_mask[l-1:r, -400:] = 1
# sample patch_size * patch_size patches
n = int(pseudo_mask_ratio * 400 / patch_size ** 2)
lu_corners = random.sample(range((20 - patch_size)**2), k=n)
region_mask = torch.zeros_like(x, dtype=torch.bool)
region_mask[-400:] = True
for c in lu_corners:
x, y = c // (20-patch_size), c % (20-patch_size)
loss_mask[-400:].view(20, 20)[x: x + patch_size, y: y + patch_size - 1] = 1.
region_mask[-400:].view(20, 20)[x: x + patch_size, y: y + patch_size] = False
attn_mask[-400:, -400:] = 1.
attn_mask[-400:, -400:].tril_()
attn_mask.masked_fill_(region_mask.unsqueeze(1), 0)
attn_mask.masked_fill_(region_mask.unsqueeze(0), 1)
return token, pos, label, attn_mask, loss_mask
def forward_step(data_iterator, model, args, timers):
"""Forward step."""
# Get the batch.
timers('batch generator').start()
tokens, position_ids, labels, attention_mask, loss_mask = get_batch(
data_iterator, args, timers)
timers('batch generator').stop()
# Forward model.
logits, *mems = model(tokens, position_ids, attention_mask)
losses = mpu.vocab_parallel_cross_entropy(logits.contiguous().float(), labels)
# scaling loss mask
loss_mask = loss_mask.reshape(-1)
losses = losses.view(-1) * loss_mask
loss = torch.sum(losses) / loss_mask.sum()
# split loss
is_image_pos = (labels<20000).view(-1).logical_and(loss_mask)
is_english_pos = ((labels>20100) & (labels < 83823)).view(-1).logical_and(loss_mask)
is_chinese_pos = ((labels>=83823) & (labels < 145653)).view(-1).logical_and(loss_mask)
image_loss = losses[is_image_pos].sum() / max(is_image_pos.sum(), 1)
english_loss = losses[is_english_pos].sum() / max(is_english_pos.sum(), 1)
chinese_loss = losses[is_chinese_pos].sum() / max(is_chinese_pos.sum(), 1)
return loss, {'image_loss': image_loss, 'english_loss': english_loss, 'chinese_loss': chinese_loss}
def create_dataset_function(path, args):
def process_fn(row):
row = row.astype(np.int64)
return {'text': row}
return BinaryDataset(path, process_fn, length_per_sample=512)
if __name__ == '__main__':
py_parser = argparse.ArgumentParser(add_help=False)
py_parser.add_argument('--new_hyperparam', type=str, default=None)
py_parser.add_argument('--mask_ratio', type=float, default=0.15)
py_parser.add_argument('--image_caption_ratio', type=float, default=0.3)
known, args_list = py_parser.parse_known_args()
args = get_args(args_list)
args = argparse.Namespace(**vars(args), **vars(known))
training_main(args, model_cls=BaseModel, forward_step_function=forward_step, create_dataset_function=create_dataset_function)