You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
154 lines
6.3 KiB
Python
154 lines
6.3 KiB
Python
########################################################################################################
|
|
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
|
|
########################################################################################################
|
|
|
|
import os, sys, time, math, random, json, datetime
|
|
import logging
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.data import Dataset
|
|
from src.trainer import Trainer, TrainerConfig
|
|
from src.model import GPT, GPTConfig
|
|
from src.utils import set_seed
|
|
|
|
set_seed(42)
|
|
np.set_printoptions(precision=4, suppress=True, linewidth=200)
|
|
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO,)
|
|
|
|
# RWKV : our new model - fastest when ctx_len is long - VRAM friendly - good performance
|
|
# MHA_rotary : usual Multi-head Attention+Rotary+GeGLU - not as good
|
|
# MHA_shift : with time-shift - good performance
|
|
# MHA_pro : slow (lots of tricks) - VRAM hungry - very good performance
|
|
model_type = 'RWKV'
|
|
|
|
# datafile = u"V:\\NLP\\text8"
|
|
# datafile = u"V:\\NLP\\enwik8"
|
|
datafile = u"V:\\NLP\\simplebooks\\simplebooks-92-raw\\train.txt"
|
|
datafile_encoding = 'utf-8'
|
|
# datafile = u"Y:\\BlinkNLP\\_txt_\\txt\\_all.txt"
|
|
# datafile_encoding = 'utf-16'
|
|
|
|
model_level = 'character' # 'character' or 'word'
|
|
|
|
ctx_len = 256 # context length
|
|
n_layer = 5
|
|
n_head = 8
|
|
n_embd = n_head * 64
|
|
|
|
batch_size = 64
|
|
|
|
n_epoch = 50 # the 'epoch' here is actually very short (and of fixed length)
|
|
lr_init = 6e-4 if model_type == 'RWKV' else 4e-4 # seems RWKV can use higher lr
|
|
lr_final = 2e-4
|
|
|
|
betas = (0.9, 0.99)
|
|
weight_decay = 0 if model_type == 'RWKV' else 0.01 # seems wd is not very useful when we have enough data
|
|
epoch_length_fixed = 10000 # make an 'epoch' very short, so we can see the training progress
|
|
|
|
########################################################################################################
|
|
# Load data
|
|
########################################################################################################
|
|
|
|
print('loading data... ' + datafile)
|
|
|
|
class Dataset(Dataset):
|
|
def __init__(self, data, model_level, ctx_len):
|
|
print('building token list...')
|
|
if model_level == 'word':
|
|
import re
|
|
data = re.sub(r'(\n|\.|\,|\?|\!|\:|\;|\-|\—|\||\'|\"|\`|\(|\)|[0-9]|\[|\]|\{|\}|\=|\+|\*|\\|\/|\~|\&|\$|\#|\%)', r' \g<0> ', data)
|
|
data = re.sub(' +',' ',data)
|
|
print('splitting token...')
|
|
data = data.lower().split(' ')
|
|
unique = sorted(list(set(data)))
|
|
# for u in unique:
|
|
# print(u, end=' ')
|
|
data_size, vocab_size = len(data), len(unique)
|
|
print('\n\ndata has %d %ss, %d unique.' % (data_size, model_level, vocab_size))
|
|
self.stoi = { ch:i for i,ch in enumerate(unique) }
|
|
self.itos = { i:ch for i,ch in enumerate(unique) }
|
|
self.ctx_len = ctx_len
|
|
self.vocab_size = vocab_size
|
|
self.data = data
|
|
|
|
def __len__(self):
|
|
return epoch_length_fixed
|
|
|
|
def __getitem__(self, idx):
|
|
i = np.random.randint(0, len(self.data) - (self.ctx_len + 1)) # cheat: pick a random spot in dataset
|
|
chunk = self.data[i:i+self.ctx_len+1]
|
|
dix = [self.stoi[s] for s in chunk]
|
|
x = torch.tensor(dix[:-1], dtype=torch.long)
|
|
y = torch.tensor(dix[1:], dtype=torch.long)
|
|
return x, y
|
|
|
|
train_dataset = Dataset(open(datafile, "r", encoding=datafile_encoding).read(), model_level, ctx_len)
|
|
|
|
########################################################################################################
|
|
# Train model
|
|
########################################################################################################
|
|
|
|
model = GPT(GPTConfig(train_dataset.vocab_size, train_dataset.ctx_len, model_type=model_type,
|
|
n_layer=n_layer, n_head=n_head, n_embd=n_embd))
|
|
|
|
print('model', model_type, 'total epoch', n_epoch, 'batch_size', batch_size, 'n_layer', n_layer, 'n_head', n_head, 'n_embd', n_embd, 'len', ctx_len)
|
|
tconf = TrainerConfig(model_type=model_type, max_epochs=n_epoch, batch_size=batch_size, weight_decay=weight_decay,
|
|
learning_rate=lr_init, lr_decay=True, lr_final=lr_final, betas=betas,
|
|
warmup_tokens=0, final_tokens=n_epoch*len(train_dataset)*ctx_len, num_workers=0)
|
|
trainer = Trainer(model, train_dataset, None, tconf)
|
|
|
|
trainer.train()
|
|
|
|
torch.save(model, 'trained-' + datetime.datetime.today().strftime('%Y-%m-%d-%H-%M-%S') + '.pth')
|
|
|
|
########################################################################################################
|
|
# Run model to generate text
|
|
########################################################################################################
|
|
|
|
from src.utils import sample_logits
|
|
|
|
NUM_OF_RUNS = 5
|
|
LENGTH_OF_EACH = 300
|
|
|
|
for run in range(NUM_OF_RUNS):
|
|
context = "it was"
|
|
|
|
if model_level == 'word':
|
|
x = np.array([train_dataset.stoi[s] for s in context.strip().lower().split(' ')], dtype=np.int64)
|
|
else:
|
|
x = np.array([train_dataset.stoi[s] for s in context], dtype=np.int64)
|
|
|
|
real_len = len(x)
|
|
if real_len < ctx_len:
|
|
x = np.pad(x, (0, ctx_len - real_len))
|
|
print_begin = 0
|
|
|
|
for i in range(LENGTH_OF_EACH):
|
|
|
|
if i == 0:
|
|
print(('-' * 80) + '\n' + context, end = '')
|
|
print_begin = real_len
|
|
|
|
with torch.no_grad():
|
|
xxx = torch.tensor(x[-ctx_len:], dtype=torch.long)[None,...].to("cuda:0")
|
|
out, _ = model(xxx)
|
|
pos = -1 if real_len >= ctx_len else real_len - 1
|
|
|
|
char = sample_logits(out, pos, temperature=1.0, min_p_pow=2.0, min_p_ratio=0.02) # our special sampling method
|
|
|
|
if real_len < ctx_len:
|
|
x[real_len] = char
|
|
else:
|
|
x = np.append(x, char)
|
|
real_len += 1
|
|
|
|
if i % 10 == 9 or i == LENGTH_OF_EACH-1:
|
|
if model_level == 'word':
|
|
completion = ' ' + ' '.join([train_dataset.itos[int(i)] for i in x[print_begin:real_len]])
|
|
completion = completion.replace('\n ', '\n')
|
|
else:
|
|
completion = ''.join([train_dataset.itos[int(i)] for i in x[print_begin:real_len]])
|
|
print(completion, end = '')
|
|
print_begin = real_len
|
|
print()
|