Merge branch 'main' into main

ch06/03_bonus_imdb-classification/gpt_download.py

@@ -0,0 +1,99 @@
+# 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