112 lines
3.8 KiB
Python
Executable File
112 lines
3.8 KiB
Python
Executable File
"""
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The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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Modified version of Andrej Karpathy's "mingpt" repo found here: https://github.com/karpathy/minGPT
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"""
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import torch
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from torch.utils.data import Dataset
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from torch.utils.data.dataloader import DataLoader
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from gpt_model import Character_GPT
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class CharDataset(Dataset):
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def __init__(self, data):
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#Configure block size
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self.block_size = 10
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#Define possible characters, and create mapping from number to character
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chars = sorted(list(set(data)))
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self.index2char = { i:ch for i,ch in enumerate(chars) }
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self.char2index = { ch:i for i,ch in enumerate(chars) }
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vocab_size = len(chars)
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self.vocab_size = vocab_size
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self.data = data
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def __len__(self):
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return len(self.data) - self.block_size
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def __getitem__(self, idx):
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# grab a chunk of (block_size + 1) characters from the data
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chunk = self.data[idx:idx + self.block_size + 1]
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# encode every character to an integer
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dix = [self.char2index[s] for s in chunk]
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# return as tensors
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x = torch.tensor(dix[:-1], dtype=torch.long)
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y = torch.tensor(dix[1:],dtype=torch.long)
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return x, y
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def train_single_iteration(model, data_iter):
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try:
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batch = next(data_iter)
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except StopIteration:
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data_iter = iter(train_loader)
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batch = next(data_iter)
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batch = [t for t in batch]
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x, y = batch
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# forward the model
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loss = model.get_loss(x, y)
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# backprop and update the parameters
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model.zero_grad(set_to_none=True)
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loss.backward()
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optimizer.step()
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if __name__ == '__main__':
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#Variables to configure
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learning_rate = 0.0004
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num_iterations = 10000
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batch_size = 500
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checkpoint = 100 #How often to print out results of the model during training
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context = "Pacman" #Generative prompt
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layer_size = 100
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n_layer = 6 #How many transformer blocks to have
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# construct the training dataset
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text = open('input.txt', 'r').read()
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train_dataset = CharDataset(text)
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# setup the dataloader
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train_loader = DataLoader(
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train_dataset,
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sampler=torch.utils.data.RandomSampler(train_dataset, replacement=True, num_samples=int(1e10)),
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shuffle=False,
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pin_memory=True,
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batch_size=batch_size,
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num_workers=1,
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)
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train_iterations = iter(train_loader)
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#set up model and optimizer
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model = Character_GPT(train_dataset.block_size, n_embd=layer_size, n_layer=n_layer, vocab_size=train_dataset.vocab_size)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.00004)
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for i in range(num_iterations):
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train_single_iteration(model, train_iterations)
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if i % checkpoint == 0:
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with torch.no_grad():
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print("Iteration: " + str(i) + "\n")
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# sample from the model...
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print("Prompt: " + context)
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print("Generated result: ")
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x = torch.tensor([train_dataset.char2index[s] for s in context])[None,...]
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y = model.generate(x, 500)[0]
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completion = ''.join([train_dataset.index2char[int(i)] for i in y])
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print(completion + "\n")
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