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7bd263144e
* Switch from urllib to requests to improve reliability * Keep ruff linter-specific * update * update * update
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
# 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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from llms_from_scratch.ch02 import create_dataloader_v1
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import os
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import requests
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import pytest
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import torch
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@pytest.mark.parametrize("file_name", ["the-verdict.txt"])
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def test_dataloader(tmp_path, file_name):
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if not os.path.exists("the-verdict.txt"):
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url = (
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"https://raw.githubusercontent.com/rasbt/"
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"LLMs-from-scratch/main/ch02/01_main-chapter-code/"
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"the-verdict.txt"
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)
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file_path = "the-verdict.txt"
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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with open(file_path, "wb") as f:
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f.write(response.content)
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with open("the-verdict.txt", "r", encoding="utf-8") as f:
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raw_text = f.read()
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vocab_size = 50257
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output_dim = 256
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context_length = 1024
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token_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)
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pos_embedding_layer = torch.nn.Embedding(context_length, output_dim)
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batch_size = 8
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max_length = 4
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dataloader = create_dataloader_v1(
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raw_text,
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batch_size=batch_size,
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max_length=max_length,
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stride=max_length
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)
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for batch in dataloader:
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x, y = batch
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token_embeddings = token_embedding_layer(x)
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pos_embeddings = pos_embedding_layer(torch.arange(max_length))
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input_embeddings = token_embeddings + pos_embeddings
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break
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input_embeddings.shape == torch.Size([8, 4, 256])
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