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Alternative weight loading via .safetensors (#507)
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Sebastian Raschka
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@ -31,6 +31,7 @@ appendix-E/01_main-chapter-code/gpt2
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ch05/01_main-chapter-code/gpt2/
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ch05/02_alternative_weight_loading/checkpoints
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ch05/02_alternative_weight_loading/*.safetensors
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ch05/01_main-chapter-code/model.pth
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ch05/01_main-chapter-code/model_and_optimizer.pth
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ch05/03_bonus_pretraining_on_gutenberg/model_checkpoints
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@ -2103,7 +2103,20 @@
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"id": "127ddbdb-3878-4669-9a39-d231fbdfb834",
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"metadata": {},
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"source": [
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"- For an alternative way to load the weights from the Hugging Face Hub, see [../02_alternative_weight_loading](../02_alternative_weight_loading)"
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"<span style=\"color:darkred\">\n",
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" <ul>\n",
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" <li>For an alternative way to load the weights from the Hugging Face Hub, see <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a></li>\n",
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" <ul>\n",
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" <li>This is useful if:</li>\n",
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" <ul>\n",
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" <li>the weights are temporarily unavailable</li>\n",
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" <li>a company VPN only permits downloads from the Hugging Face Hub but not from the OpenAI CDN, for example</li>\n",
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" <li>you are having trouble with the TensorFlow installation (the original weights are stored in TensorFlow files)</li>\n",
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" </ul>\n",
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" </ul>\n",
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" <li>The <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a> code notebooks are replacements for the remainder of this section 5.5</li>\n",
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" </ul>\n",
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"</span>\n"
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]
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},
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{
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@ -2505,7 +2518,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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@ -155,8 +155,8 @@ def assign(left, right):
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def load_weights_into_gpt(gpt, params):
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gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
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gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
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gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params["wpe"])
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gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params["wte"])
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for b in range(len(params["blocks"])):
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q_w, k_w, v_w = np.split(
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@ -229,7 +229,7 @@ def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=No
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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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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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@ -3,3 +3,5 @@
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This folder contains alternative weight loading strategies in case the weights become unavailable from OpenAI.
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- [weight-loading-hf-transformers.ipynb](weight-loading-hf-transformers.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `transformers` library
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- [weight-loading-hf-safetensors.ipynb](weight-loading-hf-safetensors.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `safetensors` library directly (skipping the instantiation of a Hugging Face transformer model)
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@ -0,0 +1,314 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6d6bc54f-2b16-4b0f-be69-957eed5d112f",
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"metadata": {},
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"source": [
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"<table style=\"width:100%\">\n",
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"<tr>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<font size=\"2\">\n",
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"Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
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"<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
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"</font>\n",
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"</td>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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"</td>\n",
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"</tr>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72953590-5363-4398-85ce-54bde07f3d8a",
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"metadata": {},
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"source": [
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"# Bonus Code for Chapter 5"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a4ab5ee-e7b9-45d3-a82b-a12bcfc0945a",
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"metadata": {},
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"source": [
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"## Alternative Weight Loading from Hugging Face Model Hub Via `safetensors`"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2feea87-49f0-48b9-b925-b8f0dda4096f",
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"metadata": {},
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"source": [
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"- In the main chapter, we loaded the GPT model weights directly from OpenAI\n",
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"- This notebook provides alternative weight loading code to load the model weights from the [Hugging Face Model Hub](https://huggingface.co/docs/hub/en/models-the-hub) using `.safetensors` files\n",
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"- This is conceptually the same as loading weights of a PyTorch model from via the state-dict method described in chapter 5:\n",
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"\n",
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"```python\n",
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"state_dict = torch.load(\"model_state_dict.pth\")\n",
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"model.load_state_dict(state_dict) \n",
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"```\n",
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"\n",
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"- The appeal of `.safetensors` files lies in their secure design, as they only store tensor data and avoid the execution of potentially malicious code during loading\n",
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"- In newer versions of PyTorch (e.g., 2.0 and newer), a `weights_only=True` argument can be used with `torch.load` (e.g., `torch.load(\"model_state_dict.pth\", weights_only=True)`) to improve safety by skipping the execution of code and loading only the weights (this is now enabled by default in PyTorch 2.6 and newer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "99b77109-5215-4d07-a618-4d10eff1a488",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install safetensors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b0467eff-b43c-4a38-93e8-5ed87a5fc2b1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"numpy version: 1.26.4\n",
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"torch version: 2.5.1\n",
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"safetensors version: 0.4.4\n"
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]
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}
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],
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"source": [
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"from importlib.metadata import version\n",
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"\n",
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"pkgs = [\"numpy\", \"torch\", \"safetensors\"]\n",
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"for p in pkgs:\n",
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" print(f\"{p} version: {version(p)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d1cb0023-8a47-4b1a-9bde-54ab7eac476b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from previous_chapters import GPTModel, generate_text_simple"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9ea9b1bc-7881-46ad-9555-27a9cf23faa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"BASE_CONFIG = {\n",
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" \"vocab_size\": 50257, # Vocabulary size\n",
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" \"context_length\": 1024, # Context length\n",
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" \"drop_rate\": 0.0, # Dropout rate\n",
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" \"qkv_bias\": True # Query-key-value bias\n",
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"}\n",
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"\n",
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"model_configs = {\n",
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" \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
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" \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
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" \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
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" \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
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"}\n",
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"\n",
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"\n",
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"CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
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"BASE_CONFIG.update(model_configs[CHOOSE_MODEL])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "e7b22375-6fac-4e90-9063-daa4de86c778",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import urllib.request\n",
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"from safetensors.torch import load_file\n",
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"\n",
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"URL_DIR = {\n",
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" \"gpt2-small (124M)\": \"gpt2\", # works ok\n",
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" \"gpt2-medium (355M)\": \"gpt2-medium\", # this file seems to have issues via `generate`\n",
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" \"gpt2-large (774M)\": \"gpt2-large\", # works ok\n",
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" \"gpt2-xl (1558M)\": \"gpt2-xl\" # works ok\n",
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"}\n",
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"\n",
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"url = f\"https://huggingface.co/openai-community/{URL_DIR[CHOOSE_MODEL]}/resolve/main/model.safetensors\"\n",
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"output_file = f\"model-{URL_DIR[CHOOSE_MODEL]}.safetensors\"\n",
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"\n",
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"# Download file\n",
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"if not os.path.exists(output_file):\n",
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" urllib.request.urlretrieve(url, output_file)\n",
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"\n",
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"# Load file\n",
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"state_dict = load_file(output_file)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "4e2a4cf4-a54e-4307-9141-fb9f288e4dfa",
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"metadata": {},
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"outputs": [],
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"source": [
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"def assign(left, right):\n",
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" if left.shape != right.shape:\n",
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" raise ValueError(f\"Shape mismatch. Left: {left.shape}, Right: {right.shape}\")\n",
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" return torch.nn.Parameter(right.detach())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "75be3077-f141-44bb-af88-62580ffd224c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_weights_into_gpt(gpt, params):\n",
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" gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params[\"wpe.weight\"])\n",
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" gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params[\"wte.weight\"])\n",
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"\n",
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" for b in range(len(gpt.trf_blocks)):\n",
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" q_w, k_w, v_w = torch.chunk(\n",
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" params[f\"h.{b}.attn.c_attn.weight\"], 3, axis=-1)\n",
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" gpt.trf_blocks[b].att.W_query.weight = assign(\n",
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" gpt.trf_blocks[b].att.W_query.weight, q_w.T)\n",
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" gpt.trf_blocks[b].att.W_key.weight = assign(\n",
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" gpt.trf_blocks[b].att.W_key.weight, k_w.T)\n",
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" gpt.trf_blocks[b].att.W_value.weight = assign(\n",
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" gpt.trf_blocks[b].att.W_value.weight, v_w.T)\n",
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"\n",
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" q_b, k_b, v_b = torch.chunk(\n",
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" params[f\"h.{b}.attn.c_attn.bias\"], 3, axis=-1)\n",
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" gpt.trf_blocks[b].att.W_query.bias = assign(\n",
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" gpt.trf_blocks[b].att.W_query.bias, q_b)\n",
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" gpt.trf_blocks[b].att.W_key.bias = assign(\n",
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" gpt.trf_blocks[b].att.W_key.bias, k_b)\n",
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" gpt.trf_blocks[b].att.W_value.bias = assign(\n",
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" gpt.trf_blocks[b].att.W_value.bias, v_b)\n",
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"\n",
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" gpt.trf_blocks[b].att.out_proj.weight = assign(\n",
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" gpt.trf_blocks[b].att.out_proj.weight,\n",
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" params[f\"h.{b}.attn.c_proj.weight\"].T)\n",
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" gpt.trf_blocks[b].att.out_proj.bias = assign(\n",
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" gpt.trf_blocks[b].att.out_proj.bias,\n",
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" params[f\"h.{b}.attn.c_proj.bias\"])\n",
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"\n",
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" gpt.trf_blocks[b].ff.layers[0].weight = assign(\n",
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" gpt.trf_blocks[b].ff.layers[0].weight,\n",
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" params[f\"h.{b}.mlp.c_fc.weight\"].T)\n",
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" gpt.trf_blocks[b].ff.layers[0].bias = assign(\n",
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" gpt.trf_blocks[b].ff.layers[0].bias,\n",
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" params[f\"h.{b}.mlp.c_fc.bias\"])\n",
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" gpt.trf_blocks[b].ff.layers[2].weight = assign(\n",
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" gpt.trf_blocks[b].ff.layers[2].weight,\n",
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" params[f\"h.{b}.mlp.c_proj.weight\"].T)\n",
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" gpt.trf_blocks[b].ff.layers[2].bias = assign(\n",
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" gpt.trf_blocks[b].ff.layers[2].bias,\n",
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" params[f\"h.{b}.mlp.c_proj.bias\"])\n",
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"\n",
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" gpt.trf_blocks[b].norm1.scale = assign(\n",
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" gpt.trf_blocks[b].norm1.scale,\n",
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" params[f\"h.{b}.ln_1.weight\"])\n",
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" gpt.trf_blocks[b].norm1.shift = assign(\n",
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" gpt.trf_blocks[b].norm1.shift,\n",
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" params[f\"h.{b}.ln_1.bias\"])\n",
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" gpt.trf_blocks[b].norm2.scale = assign(\n",
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" gpt.trf_blocks[b].norm2.scale,\n",
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" params[f\"h.{b}.ln_2.weight\"])\n",
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" gpt.trf_blocks[b].norm2.shift = assign(\n",
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" gpt.trf_blocks[b].norm2.shift,\n",
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" params[f\"h.{b}.ln_2.bias\"])\n",
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"\n",
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" gpt.final_norm.scale = assign(gpt.final_norm.scale, params[\"ln_f.weight\"])\n",
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" gpt.final_norm.shift = assign(gpt.final_norm.shift, params[\"ln_f.bias\"])\n",
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" gpt.out_head.weight = assign(gpt.out_head.weight, params[\"wte.weight\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "cda44d37-92c0-4c19-a70a-15711513afce",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from previous_chapters import GPTModel\n",
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"\n",
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"\n",
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"gpt = GPTModel(BASE_CONFIG)\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"load_weights_into_gpt(gpt, state_dict)\n",
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"gpt.to(device);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "4ddd0d51-3ade-4890-9bab-d63f141d095f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Output text:\n",
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" Every effort moves forward, but it's not enough.\n",
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"\n",
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"\"I'm not going to sit here and say, 'I'm not going to do this,'\n"
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]
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}
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],
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"source": [
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"import tiktoken\n",
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"from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
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"\n",
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"torch.manual_seed(123)\n",
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"\n",
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"tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
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"\n",
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"token_ids = generate(\n",
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" model=gpt.to(device),\n",
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" idx=text_to_token_ids(\"Every effort moves\", tokenizer).to(device),\n",
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" max_new_tokens=30,\n",
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" context_size=BASE_CONFIG[\"context_length\"],\n",
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" top_k=1,\n",
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" temperature=1.0\n",
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")\n",
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"\n",
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"print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -293,7 +293,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user