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.
118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import os, sys, time, math, random, json, datetime
|
|
import logging
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.nn import functional as F
|
|
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,)
|
|
|
|
model_type = 'RWKV' # 'RWKV' or 'RotaryMHA'
|
|
|
|
datafile = u"V:\\NLP\\simplebooks\\simplebooks-92-raw\\train.txt" # https://dldata-public.s3.us-east-2.amazonaws.com/simplebooks.zip
|
|
model_level = 'character' # 'character' or 'word'
|
|
|
|
ctx_size = 256 if 'character' else 128
|
|
nLayers = 5
|
|
nHead = 8
|
|
nEmb = 512
|
|
|
|
nepoch = 50
|
|
nbatchsz = 64
|
|
epoch_length_fixed = 10000 # make an epoch very short, so we can see the training progress
|
|
|
|
########################################################################################################
|
|
|
|
print("loading data...", end="")
|
|
|
|
class Dataset(Dataset):
|
|
def __init__(self, data, model_level, ctx_size):
|
|
if model_level == 'word':
|
|
data = data.replace('\n', ' \n ').replace(' ', ' ').split(' ')
|
|
|
|
unique = sorted(list(set(data)))
|
|
data_size, vocab_size = len(data), len(unique)
|
|
self.stoi = { ch:i for i,ch in enumerate(unique) }
|
|
self.itos = { i:ch for i,ch in enumerate(unique) }
|
|
print('data has %d %ss, %d unique.' % (data_size, model_level, vocab_size))
|
|
self.ctx_size = ctx_size
|
|
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_size + 1)) # CHEAT: pick a spot in the dataset at random
|
|
chunk = self.data[i:i+self.ctx_size+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="utf-8").read(), model_level, ctx_size)
|
|
|
|
########################################################################################################
|
|
|
|
model = GPT(GPTConfig(train_dataset.vocab_size, train_dataset.ctx_size, model_type=model_type,
|
|
n_layer=nLayers, n_head=nHead, n_embd=nEmb))
|
|
|
|
print('model', model_type, 'total epoch', nepoch, 'batchsz', nbatchsz, 'nLayers', nLayers, 'nHead', nHead, 'nEmb', nEmb, 'len', ctx_size)
|
|
tconf = TrainerConfig(model_type=model_type, max_epochs=nepoch, batch_size=nbatchsz,
|
|
learning_rate=6e-4 if model_type == 'RWKV' else 4e-4, betas=(0.9, 0.99), # RWKV can use higher LR
|
|
lr_decay=True, lr_final=2e-4, warmup_tokens=0, final_tokens=nepoch*len(train_dataset)*ctx_size, 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')
|
|
|
|
########################################################################################################
|
|
|
|
from src.utils import sample_logits
|
|
|
|
MAX_LEN = ctx_size
|
|
NUM_OF_RUNS = 5
|
|
LENGTH_OF_EACH = 300
|
|
|
|
for run in range(NUM_OF_RUNS):
|
|
context = "It was"
|
|
|
|
x = np.array([train_dataset.stoi[s] for s in context], dtype=np.int64)
|
|
|
|
real_len = len(x)
|
|
if real_len < MAX_LEN:
|
|
x = np.pad(x, (0, MAX_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[-MAX_LEN:], dtype=torch.long)[None,...].to("cuda:0")
|
|
out, _ = model(xxx)
|
|
pos = -1 if real_len >= MAX_LEN else real_len - 1
|
|
|
|
char = sample_logits(out, pos, temperature=1.0, min_p_pow=2.0, min_p_ratio=0.02)
|
|
|
|
if real_len < MAX_LEN:
|
|
x[real_len] = char
|
|
else:
|
|
x = np.append(x, char)
|
|
real_len += 1
|
|
|
|
if i % 10 == 9 or i == LENGTH_OF_EACH-1:
|
|
completion = ''.join([train_dataset.itos[int(i)] for i in x[print_begin:real_len]])
|
|
print(completion, end = '')
|
|
print_begin = real_len
|
|
print()
|