mirror of
https://github.com/karpathy/minGPT
synced 2024-06-09 00:06:07 +02:00
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import random
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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def set_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def top_k_logits(logits, k):
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v, ix = torch.topk(logits, k)
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out = logits.clone()
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out[out < v[:, [-1]]] = -float('Inf')
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return out
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@torch.no_grad()
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def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
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"""
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take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
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the sequence, feeding the predictions back into the model each time. Clearly the sampling
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has quadratic complexity unlike an RNN that is only linear, and has a finite context window
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of block_size, unlike an RNN that has an infinite context window.
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"""
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block_size = model.get_block_size()
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model.eval()
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for k in range(steps):
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x_cond = x if x.size(1) <= block_size else x[:, -block_size:] # crop context if needed
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logits, _ = model(x_cond)
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# pluck the logits at the final step and scale by temperature
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logits = logits[:, -1, :] / temperature
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# optionally crop probabilities to only the top k options
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if top_k is not None:
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logits = top_k_logits(logits, top_k)
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# apply softmax to convert to probabilities
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probs = F.softmax(logits, dim=-1)
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# sample from the distribution or take the most likely
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if sample:
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ix = torch.multinomial(probs, num_samples=1)
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else:
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_, ix = torch.topk(probs, k=1, dim=-1)
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# append to the sequence and continue
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x = torch.cat((x, ix), dim=1)
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return x
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