mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-10-30 17:29:59 +00:00
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
|
|
# Source for "Build a Large Language Model From Scratch"
|
|
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
|
|
# Code: https://github.com/rasbt/LLMs-from-scratch
|
|
|
|
|
|
import os
|
|
import requests
|
|
import json
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from tqdm import tqdm
|
|
|
|
|
|
def download_and_load_gpt2(model_size, models_dir):
|
|
# Validate model size
|
|
allowed_sizes = ("124M", "355M", "774M", "1558M")
|
|
if model_size not in allowed_sizes:
|
|
raise ValueError(f"Model size not in {allowed_sizes}")
|
|
|
|
# Define paths
|
|
model_dir = os.path.join(models_dir, model_size)
|
|
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
|
|
filenames = [
|
|
"checkpoint", "encoder.json", "hparams.json",
|
|
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
|
|
"model.ckpt.meta", "vocab.bpe"
|
|
]
|
|
|
|
# Download files
|
|
os.makedirs(model_dir, exist_ok=True)
|
|
for filename in filenames:
|
|
file_url = os.path.join(base_url, model_size, filename)
|
|
file_path = os.path.join(model_dir, filename)
|
|
download_file(file_url, file_path)
|
|
|
|
# Load settings and params
|
|
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
|
|
settings = json.load(open(os.path.join(model_dir, "hparams.json")))
|
|
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
|
|
|
|
return settings, params
|
|
|
|
|
|
def download_file(url, destination):
|
|
# Send a GET request to download the file in streaming mode
|
|
response = requests.get(url, stream=True)
|
|
|
|
# Get the total file size from headers, defaulting to 0 if not present
|
|
file_size = int(response.headers.get("content-length", 0))
|
|
|
|
# Check if file exists and has the same size
|
|
if os.path.exists(destination):
|
|
file_size_local = os.path.getsize(destination)
|
|
if file_size == file_size_local:
|
|
print(f"File already exists and is up-to-date: {destination}")
|
|
return
|
|
|
|
# Define the block size for reading the file
|
|
block_size = 1024 # 1 Kilobyte
|
|
|
|
# Initialize the progress bar with total file size
|
|
progress_bar_description = url.split("/")[-1] # Extract filename from URL
|
|
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
|
|
# Open the destination file in binary write mode
|
|
with open(destination, "wb") as file:
|
|
# Iterate over the file data in chunks
|
|
for chunk in response.iter_content(block_size):
|
|
progress_bar.update(len(chunk)) # Update progress bar
|
|
file.write(chunk) # Write the chunk to the file
|
|
|
|
|
|
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
|
|
# Initialize parameters dictionary with empty blocks for each layer
|
|
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
|
|
|
|
# Iterate over each variable in the checkpoint
|
|
for name, _ in tf.train.list_variables(ckpt_path):
|
|
# Load the variable and remove singleton dimensions
|
|
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
|
|
|
|
# Process the variable name to extract relevant parts
|
|
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
|
|
|
|
# Identify the target dictionary for the variable
|
|
target_dict = params
|
|
if variable_name_parts[0].startswith("h"):
|
|
layer_number = int(variable_name_parts[0][1:])
|
|
target_dict = params["blocks"][layer_number]
|
|
|
|
# Recursively access or create nested dictionaries
|
|
for key in variable_name_parts[1:-1]:
|
|
target_dict = target_dict.setdefault(key, {})
|
|
|
|
# Assign the variable array to the last key
|
|
last_key = variable_name_parts[-1]
|
|
target_dict[last_key] = variable_array
|
|
|
|
return params
|