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https://github.com/rasbt/LLMs-from-scratch.git
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64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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#
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# This file collects all the relevant code that we covered thus far
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# throughout Chapters 2-4.
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# This file can be run as a standalone script.
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import torch
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#####################################
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# Chapter 5
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#####################################
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def text_to_token_ids(text, tokenizer):
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encoded = tokenizer.encode(text)
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encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
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return encoded_tensor
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def token_ids_to_text(token_ids, tokenizer):
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flat = token_ids.squeeze(0) # remove batch dimension
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return tokenizer.decode(flat.tolist())
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def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
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# For-loop is the same as before: Get logits, and only focus on last time step
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for _ in range(max_new_tokens):
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idx_cond = idx[:, -context_size:]
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with torch.no_grad():
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logits = model(idx_cond)
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logits = logits[:, -1, :]
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# New: Filter logits with top_k sampling
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if top_k is not None:
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# Keep only top_k values
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top_logits, _ = torch.topk(logits, top_k)
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min_val = top_logits[:, -1]
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logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
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# New: Apply temperature scaling
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if temperature > 0.0:
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logits = logits / temperature
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# Apply softmax to get probabilities
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probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
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# Sample from the distribution
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idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
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# Otherwise same as before: get idx of the vocab entry with the highest logits value
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else:
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idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
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if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
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break
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# Same as before: append sampled index to the running sequence
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idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
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return idx
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