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import json
import os.path
from zipfile import ZipFile
import numpy
import torch
from torch import nn, optim
from torch.serialization import MAP_LOCATION
class CustomTokenizer(nn.Module):
def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
super(CustomTokenizer, self).__init__()
next_size = input_size
if version == 0:
self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
next_size = hidden_size
if version == 1:
self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
self.intermediate = nn.Linear(hidden_size, 4096)
next_size = 4096
self.fc = nn.Linear(next_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)
self.optimizer: optim.Optimizer = None
self.lossfunc = nn.CrossEntropyLoss()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.version = version
def forward(self, x):
x, _ = self.lstm(x)
if self.version == 1:
x = self.intermediate(x)
x = self.fc(x)
x = self.softmax(x)
return x
@torch.no_grad()
def get_token(self, x):
"""
Used to get the token for the first
:param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model.
:return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model.
"""
return torch.argmax(self(x), dim=1)
def prepare_training(self):
self.optimizer = optim.Adam(self.parameters(), 0.001)
def train_step(self, x_train, y_train, log_loss=False):
# y_train = y_train[:-1]
# y_train = y_train[1:]
optimizer = self.optimizer
lossfunc = self.lossfunc
# Zero the gradients
self.zero_grad()
# Forward pass
y_pred = self(x_train)
y_train_len = len(y_train)
y_pred_len = y_pred.shape[0]
if y_train_len > y_pred_len:
diff = y_train_len - y_pred_len
y_train = y_train[diff:]
elif y_train_len < y_pred_len:
diff = y_pred_len - y_train_len
y_pred = y_pred[:-diff, :]
y_train_hot = torch.zeros(len(y_train), self.output_size)
y_train_hot[range(len(y_train)), y_train] = 1
y_train_hot = y_train_hot.to('cuda')
# Calculate the loss
loss = lossfunc(y_pred, y_train_hot)
# Print loss
if log_loss:
print('Loss', loss.item())
# Backward pass
loss.backward()
# Update the weights
optimizer.step()
def save(self, path):
info_path = os.path.basename(path) + '/.info'
torch.save(self.state_dict(), path)
data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version)
with ZipFile(path, 'a') as model_zip:
model_zip.writestr(info_path, data_from_model.save())
model_zip.close()
@staticmethod
def load_from_checkpoint(path, map_location: MAP_LOCATION = None):
old = True
with ZipFile(path) as model_zip:
filesMatch = [file for file in model_zip.namelist() if file.endswith('/.info')]
file = filesMatch[0] if filesMatch else None
if file:
old = False
data_from_model = Data.load(model_zip.read(file).decode('utf-8'))
model_zip.close()
if old:
model = CustomTokenizer()
else:
model = CustomTokenizer(data_from_model.hidden_size, data_from_model.input_size, data_from_model.output_size, data_from_model.version)
model.load_state_dict(torch.load(path, map_location))
return model
class Data:
input_size: int
hidden_size: int
output_size: int
version: int
def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.version = version
@staticmethod
def load(string):
data = json.loads(string)
return Data(data['input_size'], data['hidden_size'], data['output_size'], data['version'])
def save(self):
data = {
'input_size': self.input_size,
'hidden_size': self.hidden_size,
'output_size': self.output_size,
'version': self.version,
}
return json.dumps(data)
def auto_train(data_path, save_path='model.pth', load_model: str | None = None, save_epochs=1):
data_x, data_y = [], []
if load_model and os.path.isfile(load_model):
print('Loading model from', load_model)
model_training = CustomTokenizer.load_from_checkpoint(load_model, 'cuda')
else:
print('Creating new model.')
model_training = CustomTokenizer(version=1).to('cuda') # Settings for the model to run without lstm
save_path = os.path.join(data_path, save_path)
base_save_path = '.'.join(save_path.split('.')[:-1])
sem_string = '_semantic.npy'
feat_string = '_semantic_features.npy'
ready = os.path.join(data_path, 'ready')
for input_file in os.listdir(ready):
full_path = os.path.join(ready, input_file)
if input_file.endswith(sem_string):
data_y.append(numpy.load(full_path))
elif input_file.endswith(feat_string):
data_x.append(numpy.load(full_path))
model_training.prepare_training()
epoch = 1
while 1:
for i in range(save_epochs):
j = 0
for x, y in zip(data_x, data_y):
model_training.train_step(torch.tensor(x).to('cuda'), torch.tensor(y).to('cuda'), j % 50 == 0) # Print loss every 50 steps
j += 1
save_p = save_path
save_p_2 = f'{base_save_path}_epoch_{epoch}.pth'
model_training.save(save_p)
model_training.save(save_p_2)
print(f'Epoch {epoch} completed')
epoch += 1
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