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7bd263144e
* Switch from urllib to requests to improve reliability * Keep ruff linter-specific * update * update * update
122 lines
4.6 KiB
Python
122 lines
4.6 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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import os
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import json
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import numpy as np
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import requests
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import tensorflow as tf
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from tqdm import tqdm
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def download_and_load_gpt2(model_size, models_dir):
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# Validate model size
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allowed_sizes = ("124M", "355M", "774M", "1558M")
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if model_size not in allowed_sizes:
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raise ValueError(f"Model size not in {allowed_sizes}")
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# Define paths
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model_dir = os.path.join(models_dir, model_size)
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base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
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backup_base_url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/gpt2"
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filenames = [
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"checkpoint", "encoder.json", "hparams.json",
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"model.ckpt.data-00000-of-00001", "model.ckpt.index",
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"model.ckpt.meta", "vocab.bpe"
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]
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# Download files
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os.makedirs(model_dir, exist_ok=True)
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for filename in filenames:
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file_url = os.path.join(base_url, model_size, filename)
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backup_url = os.path.join(backup_base_url, model_size, filename)
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file_path = os.path.join(model_dir, filename)
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download_file(file_url, file_path, backup_url)
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# Load settings and params
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tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
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settings = json.load(open(os.path.join(model_dir, "hparams.json"), "r", encoding="utf-8"))
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params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
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return settings, params
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def download_file(url, destination, backup_url=None):
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def _attempt_download(download_url):
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response = requests.get(download_url, stream=True, timeout=60)
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response.raise_for_status()
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file_size = int(response.headers.get("Content-Length", 0))
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# Check if file exists and has same size
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if os.path.exists(destination):
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file_size_local = os.path.getsize(destination)
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if file_size and file_size == file_size_local:
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print(f"File already exists and is up-to-date: {destination}")
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return True
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block_size = 1024 # 1 KB
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desc = os.path.basename(download_url)
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with tqdm(total=file_size, unit="iB", unit_scale=True, desc=desc) as progress_bar:
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with open(destination, "wb") as file:
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for chunk in response.iter_content(chunk_size=block_size):
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if chunk:
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file.write(chunk)
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progress_bar.update(len(chunk))
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return True
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try:
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if _attempt_download(url):
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return
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except requests.exceptions.RequestException:
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if backup_url is not None:
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print(f"Primary URL ({url}) failed. Attempting backup URL: {backup_url}")
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try:
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if _attempt_download(backup_url):
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return
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except requests.exceptions.RequestException:
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pass
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error_message = (
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f"Failed to download from both primary URL ({url})"
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f"{' and backup URL (' + backup_url + ')' if backup_url else ''}."
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"\nCheck your internet connection or the file availability.\n"
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"For help, visit: https://github.com/rasbt/LLMs-from-scratch/discussions/273"
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)
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print(error_message)
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except Exception as e:
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print(f"An unexpected error occurred: {e}")
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def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
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# Initialize parameters dictionary with empty blocks for each layer
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params = {"blocks": [{} for _ in range(settings["n_layer"])]}
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# Iterate over each variable in the checkpoint
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for name, _ in tf.train.list_variables(ckpt_path):
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# Load the variable and remove singleton dimensions
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variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
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# Process the variable name to extract relevant parts
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variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
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# Identify the target dictionary for the variable
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target_dict = params
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if variable_name_parts[0].startswith("h"):
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layer_number = int(variable_name_parts[0][1:])
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target_dict = params["blocks"][layer_number]
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# Recursively access or create nested dictionaries
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for key in variable_name_parts[1:-1]:
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target_dict = target_dict.setdefault(key, {})
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# Assign the variable array to the last key
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last_key = variable_name_parts[-1]
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target_dict[last_key] = variable_array
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return params
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