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| import random | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| def set_seed(seed): | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| def top_k_logits(logits, k): | |
| v, ix = torch.topk(logits, k) | |
| out = logits.clone() | |
| out[out < v[:, [-1]]] = -float('Inf') | |
| return out | |
| def sample(model, x, steps, temperature=1.0, sample=False, top_k=None): | |
| """ | |
| take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in | |
| the sequence, feeding the predictions back into the model each time. Clearly the sampling | |
| has quadratic complexity unlike an RNN that is only linear, and has a finite context window | |
| of block_size, unlike an RNN that has an infinite context window. | |
| """ | |
| block_size = model.get_block_size() | |
| model.eval() | |
| for k in range(steps): | |
| x_cond = x if x.size(1) <= block_size else x[:, -block_size:] # crop context if needed | |
| logits, _ = model(x_cond) | |
| # pluck the logits at the final step and scale by temperature | |
| logits = logits[:, -1, :] / temperature | |
| # optionally crop probabilities to only the top k options | |
| if top_k is not None: | |
| logits = top_k_logits(logits, top_k) | |
| # apply softmax to convert to probabilities | |
| probs = F.softmax(logits, dim=-1) | |
| # sample from the distribution or take the most likely | |
| if sample: | |
| ix = torch.multinomial(probs, num_samples=1) | |
| else: | |
| _, ix = torch.topk(probs, k=1, dim=-1) | |
| # append to the sequence and continue | |
| x = torch.cat((x, ix), dim=1) | |
| return x | |