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https://github.com/rasbt/LLMs-from-scratch.git
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a08d7aaa84
* Uv workflow improvements * Uv workflow improvements * linter improvements * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * windows fixes * windows fixes * windows fixes * windows fixes * windows fixes * windows fixes * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix
460 lines
14 KiB
Plaintext
460 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ba450fb1-8a26-4894-ab7a-5d7bfefe90ce",
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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": "51c9672d-8d0c-470d-ac2d-1271f8ec3f14",
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"metadata": {},
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"source": [
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"# Chapter 4 Exercise solutions"
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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": "5b2fac7a-fdcd-437c-b1c4-0b35a31cd489",
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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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"torch version: 2.4.0\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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"print(\"torch version:\", version(\"torch\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fea8be3-30a1-4623-a6d7-b095c6c1092e",
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"metadata": {},
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"source": [
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"# Exercise 4.1: Parameters in the feed forward versus attention module"
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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": "2751b0e5-ffd3-4be2-8db3-e20dd4d61d69",
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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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"TransformerBlock(\n",
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" (att): MultiHeadAttention(\n",
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" (W_query): Linear(in_features=768, out_features=768, bias=False)\n",
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" (W_key): Linear(in_features=768, out_features=768, bias=False)\n",
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" (W_value): Linear(in_features=768, out_features=768, bias=False)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (ff): FeedForward(\n",
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" (layers): Sequential(\n",
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" (0): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (1): GELU()\n",
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" (2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" )\n",
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" )\n",
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" (norm1): LayerNorm()\n",
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" (norm2): LayerNorm()\n",
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" (drop_shortcut): Dropout(p=0.1, inplace=False)\n",
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")\n"
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]
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}
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],
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"source": [
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"from gpt import TransformerBlock\n",
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"\n",
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"GPT_CONFIG_124M = {\n",
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" \"vocab_size\": 50257,\n",
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" \"context_length\": 1024,\n",
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" \"emb_dim\": 768,\n",
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" \"n_heads\": 12,\n",
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" \"n_layers\": 12,\n",
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" \"drop_rate\": 0.1,\n",
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" \"qkv_bias\": False\n",
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"}\n",
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"\n",
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"block = TransformerBlock(GPT_CONFIG_124M)\n",
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"print(block)"
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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": "1bcaffd1-0cf6-4f8f-bd53-ab88a37f443e",
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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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"Total number of parameters in feed forward module: 4,722,432\n"
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]
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}
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],
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"source": [
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"total_params = sum(p.numel() for p in block.ff.parameters())\n",
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"print(f\"Total number of parameters in feed forward module: {total_params:,}\")"
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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": "c1dd06c1-ab6c-4df7-ba73-f9cd54b31138",
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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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"Total number of parameters in attention module: 2,360,064\n"
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]
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}
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],
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"source": [
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"total_params = sum(p.numel() for p in block.att.parameters())\n",
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"print(f\"Total number of parameters in attention module: {total_params:,}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "15463dec-520a-47b4-b3ad-e180394fd076",
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"metadata": {},
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"source": [
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"- The results above are for a single transformer block\n",
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"- Optionally multiply by 12 to capture all transformer blocks in the 124M GPT model"
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]
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},
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{
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"cell_type": "markdown",
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"id": "597e9251-e0a9-4972-8df6-f280f35939f9",
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"metadata": {},
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"source": [
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"**Bonus: Mathematical breakdown**\n",
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"\n",
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"- For those interested in how these parameter counts are calculated mathematically, you can find the breakdown below (assuming `emb_dim=768`):\n",
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"\n",
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"\n",
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"Feed forward module:\n",
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"\n",
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"- 1st `Linear` layer: 768 inputs × 4×768 outputs + 4×768 bias units = 2,362,368\n",
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"- 2nd `Linear` layer: 4×768 inputs × 768 outputs + 768 bias units = 2,360,064\n",
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"- Total: 1st `Linear` layer + 2nd `Linear` layer = 2,362,368 + 2,360,064 = 4,722,432\n",
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"\n",
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"Attention module:\n",
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"\n",
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"- `W_query`: 768 inputs × 768 outputs = 589,824 \n",
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"- `W_key`: 768 inputs × 768 outputs = 589,824\n",
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"- `W_value`: 768 inputs × 768 outputs = 589,824 \n",
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"- `out_proj`: 768 inputs × 768 outputs + 768 bias units = 590,592\n",
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"- Total: `W_query` + `W_key` + `W_value` + `out_proj` = 3×589,824 + 590,592 = 2,360,064 "
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f7b7c7f-0fa1-4d30-ab44-e499edd55b6d",
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"metadata": {},
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"source": [
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"# Exercise 4.2: Initialize larger GPT models"
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]
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},
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{
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"cell_type": "markdown",
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"id": "310b2e05-3ec8-47fc-afd9-83bf03d4aad8",
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"metadata": {},
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"source": [
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"- **GPT2-small** (the 124M configuration we already implemented):\n",
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" - \"emb_dim\" = 768\n",
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" - \"n_layers\" = 12\n",
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" - \"n_heads\" = 12\n",
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"\n",
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"- **GPT2-medium:**\n",
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" - \"emb_dim\" = 1024\n",
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" - \"n_layers\" = 24\n",
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" - \"n_heads\" = 16\n",
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"\n",
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"- **GPT2-large:**\n",
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" - \"emb_dim\" = 1280\n",
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" - \"n_layers\" = 36\n",
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" - \"n_heads\" = 20\n",
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"\n",
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"- **GPT2-XL:**\n",
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" - \"emb_dim\" = 1600\n",
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" - \"n_layers\" = 48\n",
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" - \"n_heads\" = 25"
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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": "90185dea-81ca-4cdc-aef7-4aaf95cba946",
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"metadata": {},
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"outputs": [],
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"source": [
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"GPT_CONFIG_124M = {\n",
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" \"vocab_size\": 50257,\n",
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" \"context_length\": 1024,\n",
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" \"emb_dim\": 768,\n",
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" \"n_heads\": 12,\n",
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" \"n_layers\": 12,\n",
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" \"drop_rate\": 0.1,\n",
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" \"qkv_bias\": False\n",
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"}\n",
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"\n",
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"\n",
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"def get_config(base_config, model_name=\"gpt2-small\"):\n",
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" GPT_CONFIG = base_config.copy()\n",
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"\n",
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" if model_name == \"gpt2-small\":\n",
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" GPT_CONFIG[\"emb_dim\"] = 768\n",
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" GPT_CONFIG[\"n_layers\"] = 12\n",
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" GPT_CONFIG[\"n_heads\"] = 12\n",
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"\n",
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" elif model_name == \"gpt2-medium\":\n",
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" GPT_CONFIG[\"emb_dim\"] = 1024\n",
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" GPT_CONFIG[\"n_layers\"] = 24\n",
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" GPT_CONFIG[\"n_heads\"] = 16\n",
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"\n",
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" elif model_name == \"gpt2-large\":\n",
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" GPT_CONFIG[\"emb_dim\"] = 1280\n",
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" GPT_CONFIG[\"n_layers\"] = 36\n",
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" GPT_CONFIG[\"n_heads\"] = 20\n",
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"\n",
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" elif model_name == \"gpt2-xl\":\n",
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" GPT_CONFIG[\"emb_dim\"] = 1600\n",
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" GPT_CONFIG[\"n_layers\"] = 48\n",
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" GPT_CONFIG[\"n_heads\"] = 25\n",
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"\n",
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" else:\n",
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" raise ValueError(f\"Incorrect model name {model_name}\")\n",
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"\n",
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" return GPT_CONFIG\n",
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"\n",
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"\n",
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"def calculate_size(model): # based on chapter code\n",
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" \n",
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" total_params = sum(p.numel() for p in model.parameters())\n",
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" print(f\"Total number of parameters: {total_params:,}\")\n",
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"\n",
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" total_params_gpt2 = total_params - sum(p.numel() for p in model.out_head.parameters())\n",
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" print(f\"Number of trainable parameters considering weight tying: {total_params_gpt2:,}\")\n",
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" \n",
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" # Calculate the total size in bytes (assuming float32, 4 bytes per parameter)\n",
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" total_size_bytes = total_params * 4\n",
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" \n",
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" # Convert to megabytes\n",
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" total_size_mb = total_size_bytes / (1024 * 1024)\n",
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" \n",
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" print(f\"Total size of the model: {total_size_mb:.2f} MB\")"
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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": "2587e011-78a4-479c-a8fd-961cc40a5fd4",
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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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"\n",
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"\n",
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"gpt2-small:\n",
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"Total number of parameters: 163,009,536\n",
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"Number of trainable parameters considering weight tying: 124,412,160\n",
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"Total size of the model: 621.83 MB\n",
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"\n",
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"\n",
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"gpt2-medium:\n",
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"Total number of parameters: 406,212,608\n",
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"Number of trainable parameters considering weight tying: 354,749,440\n",
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"Total size of the model: 1549.58 MB\n",
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"\n",
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"\n",
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"gpt2-large:\n",
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"Total number of parameters: 838,220,800\n",
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"Number of trainable parameters considering weight tying: 773,891,840\n",
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"Total size of the model: 3197.56 MB\n",
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"\n",
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"\n",
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"gpt2-xl:\n",
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"Total number of parameters: 1,637,792,000\n",
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"Number of trainable parameters considering weight tying: 1,557,380,800\n",
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"Total size of the model: 6247.68 MB\n"
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]
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}
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],
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"source": [
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"from gpt import GPTModel\n",
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"\n",
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"\n",
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"for model_abbrev in (\"small\", \"medium\", \"large\", \"xl\"):\n",
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" model_name = f\"gpt2-{model_abbrev}\"\n",
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" CONFIG = get_config(GPT_CONFIG_124M, model_name=model_name)\n",
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" model = GPTModel(CONFIG)\n",
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" print(f\"\\n\\n{model_name}:\")\n",
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" calculate_size(model)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5f2306e-5dc8-498e-92ee-70ae7ec37ac1",
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"metadata": {},
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"source": [
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"# Exercise 4.3: Using separate dropout parameters"
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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": "5fee2cf5-61c3-4167-81b5-44ea155bbaf2",
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"metadata": {},
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"outputs": [],
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"source": [
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"GPT_CONFIG_124M = {\n",
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" \"vocab_size\": 50257,\n",
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" \"context_length\": 1024,\n",
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" \"emb_dim\": 768,\n",
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" \"n_heads\": 12,\n",
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" \"n_layers\": 12,\n",
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" \"drop_rate_emb\": 0.1, # NEW: dropout for embedding layers\n",
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" \"drop_rate_attn\": 0.1, # NEW: dropout for multi-head attention \n",
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" \"drop_rate_shortcut\": 0.1, # NEW: dropout for shortcut connections \n",
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" \"qkv_bias\": False\n",
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"}"
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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": "5aa1b0c1-d78a-48fc-ad08-4802458b43f7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch.nn as nn\n",
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"from gpt import MultiHeadAttention, LayerNorm, FeedForward\n",
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"\n",
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"\n",
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"class TransformerBlock(nn.Module):\n",
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" def __init__(self, cfg):\n",
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" super().__init__()\n",
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" self.att = MultiHeadAttention(\n",
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" d_in=cfg[\"emb_dim\"],\n",
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" d_out=cfg[\"emb_dim\"],\n",
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" context_length=cfg[\"context_length\"],\n",
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" num_heads=cfg[\"n_heads\"], \n",
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" dropout=cfg[\"drop_rate_attn\"], # NEW: dropout for multi-head attention\n",
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" qkv_bias=cfg[\"qkv_bias\"])\n",
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" self.ff = FeedForward(cfg)\n",
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" self.norm1 = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.norm2 = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.drop_shortcut = nn.Dropout(cfg[\"drop_rate_shortcut\"])\n",
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"\n",
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" def forward(self, x):\n",
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" # Shortcut connection for attention block\n",
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" shortcut = x\n",
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" x = self.norm1(x)\n",
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" x = self.att(x) # Shape [batch_size, num_tokens, emb_size]\n",
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" x = self.drop_shortcut(x)\n",
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" x = x + shortcut # Add the original input back\n",
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"\n",
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" # Shortcut connection for feed-forward block\n",
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" shortcut = x\n",
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" x = self.norm2(x)\n",
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" x = self.ff(x)\n",
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" x = self.drop_shortcut(x)\n",
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" x = x + shortcut # Add the original input back\n",
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"\n",
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" return x\n",
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"\n",
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"\n",
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"class GPTModel(nn.Module):\n",
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" def __init__(self, cfg):\n",
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" super().__init__()\n",
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" self.tok_emb = nn.Embedding(cfg[\"vocab_size\"], cfg[\"emb_dim\"])\n",
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" self.pos_emb = nn.Embedding(cfg[\"context_length\"], cfg[\"emb_dim\"])\n",
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" self.drop_emb = nn.Dropout(cfg[\"drop_rate_emb\"]) # NEW: dropout for embedding layers\n",
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"\n",
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" self.trf_blocks = nn.Sequential(\n",
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" *[TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])])\n",
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"\n",
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" self.final_norm = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False)\n",
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"\n",
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" def forward(self, in_idx):\n",
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" batch_size, seq_len = in_idx.shape\n",
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" tok_embeds = self.tok_emb(in_idx)\n",
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" pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))\n",
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" x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]\n",
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" x = self.drop_emb(x)\n",
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" x = self.trf_blocks(x)\n",
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" x = self.final_norm(x)\n",
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" logits = self.out_head(x)\n",
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" return logits"
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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": "1d013d32-c275-4f42-be21-9010f1537227",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"\n",
|
||
"torch.manual_seed(123)\n",
|
||
"model = GPTModel(GPT_CONFIG_124M)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.16"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|