def sample_some(trainer, model, dataset, X_train, n_samples=40, out_path='./samples.png'):
prob = model_first_token(dataset, X_train)
start_pixel = np.random.choice(np.arange(dataset.vocab_size), size=(n_samples, 1), replace=True, p=prob.numpy())
start_pixel = torch.from_numpy(start_pixel).to(trainer.device)
flattened_image_size = 32 * 32
pixels = sample(model, start_pixel, flattened_image_size - 1, temperature=1.0, sample=True, top_k=40)
# for visualization we have to invert the permutation used to produce the pixels
iperm = torch.argsort(dataset.perm)
pixel_size = 32
plt.close('all')
n_cols = 8
n_rows = n_samples // n_cols
fig, axis = plt.subplots(n_rows, n_cols, figsize=(32, 16))
for i, ax in enumerate(axis.ravel()):
pxi = pixels[i][iperm] # undo the encoding permutation
pxi = pxi.view(pixel_size, pixel_size).cpu().numpy().astype(np.uint8) # grayscale -> 2D
ax.imshow(pxi, cmap='magma')
# ax.set_aspect(aspect=1.0)
plt.tight_layout()
plt.savefig(out_path)
plt.close('all')
def run_one(model, input_tokens):
input_ints = list(map(cast_int, input_tokens))
input_tensor = torch.tensor([input_ints], dtype=torch.long).to(
torch.cuda.current_device())
full_tensor = sample(model, input_tensor, EXPRESSION_SIZE)
full_ints = list(map(int, full_tensor[0]))
output_ints = full_ints[-EXPRESSION_SIZE:]
return list(map(cast_token, output_ints))
def run_one(model, n):
"Returns a list of tokens produced."
input_ints = get_pixels(n)
input_tensor = torch.tensor([input_ints], dtype=torch.long).to(
torch.cuda.current_device())
full_tensor = sample(model, input_tensor, OUTPUT_SIZE)
full_ints = list(map(int, full_tensor[0]))
output_ints = full_ints[-OUTPUT_SIZE:]
return [TOKENS[i] for i in output_ints]
def make_prediction(self, x_in):
# Reset tracker variables
self.batch, self.predict, self.prev_src_len, = 0, 0, 0
clip_src_mem = self.get_clip_src_mem_len(x_in[0]) + 1
x_cut = x_in[:, :clip_src_mem]
pred = x_cut.to(self.trainer.device)
pred = sample(self.trainer.model, pred, int(self.max_trg + 1))
return pred, clip_src_mem
def model_predict(self, x_in):
# Reset tracker variables
self.predict, self.batches = 0, 0
# Prepare input
cut_index = self.get_input_len(x_in[0])
x_cut = x_in[:, :cut_index]
pred = x_cut.to(self.trainer.device)
# Make prediction
output = sample(self.trainer.model, pred, int(self.max_trg + 1))
return output
def predict_pron(config, train_dataset, trainer, model, word):
try:
x = torch.tensor([train_dataset.stoi[s] for s in word],
dtype=torch.long)[None, ...].to(trainer.device)
y = sample(model,
x,
config['output_max_len'],
temperature=1.0,
sample=True,
top_k=10)[0]
completion = ''.join([train_dataset.itos[int(i)] for i in y])
except:
e = sys.exc_info()[0]
print('predict_pron(): error %s for word:%s' % (e, word))
# Typically, this can happen if a tested word contains a char
# that did not existing during the training step
completion = 'N/A'
return completion
n_head=8,
n_embd=512)
model = GPT(mconf)
from mingpt.trainer import Trainer, TrainerConfig
# initialize a trainer instance and kick off training
tconf = TrainerConfig(max_epochs=2,
batch_size=512,
learning_rate=6e-4,
lr_decay=True,
warmup_tokens=512 * 20,
final_tokens=2 * len(train_dataset) * block_size,
num_workers=4,
device='cpu',
ckpt_path="./prfaqgen.bin")
trainer = Trainer(model, train_dataset, None, tconf)
# trainer.train()
trainer.load_checkpoint()
# trainer.save_checkpoint()
# alright, let's sample some character-level Shakespeare
from mingpt.utils import sample
context = "The launch of Skills Store Arabic was GREEN. The team "
x = torch.tensor([train_dataset.stoi[s] for s in context],
dtype=torch.long)[None, ...].to(trainer.device)
y = sample(model, x, 2000, temperature=1.0, sample=True, top_k=40)[0]
completion = ''.join([train_dataset.itos[int(i)] for i in y])
print(completion)
chunk = self.data[idx:idx + self.block_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
block_size = 128
text = open('input.txt', 'r').read()
train_dataset = CharDataset(text, block_size)
from mingpt.model import GPT, GPTConfig
mconf = GPTConfig(train_dataset.vocab_size,
train_dataset.block_size,
n_layer=8,
n_head=8,
n_embd=512)
model = GPT(mconf)
from mingpt.trainer import Trainer, TrainerConfig
from mingpt.utils import sample
while True:
tconf = TrainerConfig(max_epochs=1, batch_size=256, learning_rate=6e-4)
trainer = Trainer(model, train_dataset, tconf)
trainer.train()
context = "O God, O God!"
x = torch.tensor([train_dataset.stoi[s] for s in context],
dtype=torch.long)[None, ...].to(trainer.device)
y = sample(model, x, 100, temperature=1.0, sample=True, top_k=10)[0]
completion = ''.join([train_dataset.itos[int(i)] for i in y])
print(completion)
def run(context):
x = torch.tensor([train_dataset.stoi[s] for s in context], dtype=torch.long)[None,...].cuda()
y = sample(model, x, 500, temperature=1.0, sample=True, top_k=10)[0]
completion = ''.join([train_dataset.itos[int(i)] for i in y])
# print(completion)
return completion
rp = torch.randperm(len(train_dataset))
nest = 5000 # how many images to use for the estimation
for i in range(nest):
a, _ = train_dataset[int(rp[i])]
t = a[0].item() # index of first token in the sequence
counts[t] +=1
prob = counts/counts.sum()
%%time
from mingpt.utils import sample
n_samples = 32
start_pixel = np.random.choice(np.arange(C.size(0)), size=(n_samples, 1), replace=True, p=prob)
start_pixel = torch.from_numpy(start_pixel).to(trainer.device)
pixels = sample(model, start_pixel, 32*32-1, temperature=1.0, sample=True, top_k=100)
# for visualization we have to invert the permutation used to produce the pixels
iperm = torch.argsort(train_dataset.perm)
ncol = 8
nrow = n_samples // ncoal
plt.figure(figsize = (16, 8))
for i in range(n_samples):
pxi = pixels[i][iperm] # note: undo the encoding permutation
plt.subplot(nrow, ncol, i+1)
plt.imshow(C[pxi].view(32, 32, 3).numpy().astype(np.unit8))
plt.axis('off')
#visualize some of the learned positional embeddings, maybe they contain structure
n_head=8,
n_emb=512,
)
model = GPT(model_config)
trainer_config = TrainerConfig(
max_epochs=200,
batch_size=512,
learning_rate=6e-4,
lr_decay=True,
warmup_tokens=512 * 20,
final_tokens=200 * len(train_dataset) * sample_block_size,
num_workers=4,
)
trainer = Trainer(model, train_dataset, None, trainer_config)
trainer.train()
context = "대한민국의 대통령"
sample_x = torch.tensor(
[train_dataset.char2idx[s] for s in context],
dtype=torch.long,
)[None, ...].to(trainer.device)
sample_y = sample(model,
sample_x,
2000,
temperature=0.9,
sampling=True,
top_k=5)[0]
completion = "".join([train_dataset.idx2char[int(i)] for i in sample_y])
print(completion)