Spaces:
Running
Running
File size: 3,522 Bytes
2ef951d e0646b5 2ef951d 1c8791f 2ef951d 1c8791f 2ef951d 5e3f56c 2ef951d 5e3f56c 2ef951d 5e3f56c 2ef951d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
import torch
import mmap
import random
import os
from GPTLanguageModelClass import *
block_size = hyperparams.block_size
batch_size = hyperparams.batch_size
max_iters = hyperparams.max_iters
learning_rate = hyperparams.learning_rate
eval_every = hyperparams.eval_every
n_embd = hyperparams.n_embd
n_head = hyperparams.n_head
n_layer = hyperparams.n_layer
dropout = hyperparams.dropout
device = hyperparams.device
print(device)
if not os.path.exists("./vocab.txt") or not os.path.exists("./openwebtext/train_split.txt") or not os.path.exists("./openwebtext/val_split.txt"):
raise Exception("Please run extract.py first")
chars = ""
with open("./vocab.txt", 'r', encoding='utf-8') as f:
text = f.read()
chars = sorted(list(set(text)))
vocab_size = len(chars)
string_to_int = {ch: i for i, ch in enumerate(chars)}
int_to_string = {i: ch for i, ch in enumerate(chars)}
encode = lambda s: [string_to_int[ch] for ch in s]
decode = lambda x: ''.join([int_to_string[i] for i in x])
# memory map for using small snippets of text from a single file of any size
def get_random_chunk(split):
filename = "./openwebtext/train_split.txt" if split == 'train' else "./openwebtext/val_split.txt"
with open(filename, 'rb') as f:
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
# Determine the file size and a random position to start reading
file_size = len(mm)
start_pos = random.randint(0, (file_size) - block_size*batch_size)
# Seek to the random position and read the block of text
mm.seek(start_pos)
block = mm.read(block_size*batch_size-1)
# Decode the block to a string, ignoring any invalid byte sequences
decoded_block = block.decode('utf-8', errors='ignore').replace('\r', '')
# Train and test splits
data = torch.tensor(encode(decoded_block), dtype=torch.long)
return data
def get_batch(split):
data = get_random_chunk(split)
ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([data[i:i+block_size] for i in ix])
y = torch.stack([data[i+1:i+block_size+1] for i in ix])
x, y = x.to(device), y.to(device)
return x, y
@torch.no_grad()
def estimate_loss():
out = {}
model.eval()
for split in ['train', 'val']:
losses = torch.zeros(eval_every)
for k in range(eval_every):
X, Y = get_batch(split)
logits, loss = model(X, Y)
losses[k] = loss.item()
out[split] = losses.mean()
model.train()
return out
model = GPTLanguageModel(vocab_size).to(device)
model_pickle_path = './model.pt'
if os.path.exists(model_pickle_path):
print('loading model parameters...')
with open(model_pickle_path, 'rb') as f:
model = torch.load(f, map_location=device)
print('loaded successfully!')
# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
for iter in range(max_iters):
if iter % eval_every == 0:
losses = estimate_loss()
print(f"step: {iter}, train loss: {losses['train']:.3f}, val loss: {losses['val']:.3f}")
# sample a batch of data
xb, yb = get_batch('train')
# evaluate the loss
logits, loss = model.forward(xb, yb)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
print(loss.item())
with open(model_pickle_path, 'wb') as f:
torch.save(model, f)
print('model saved')
|