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ch4 exercise solutions
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@ -36,7 +36,7 @@ Alternatively, you can view this and other files on GitHub at [https://github.co
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| Ch 1: Understanding Large Language Models | No code | No code |
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| Ch 2: Working with Text Data | - [ch02.ipynb](ch02/01_main-chapter-code/ch02.ipynb)<br/>- [dataloader.ipynb](ch02/01_main-chapter-code/dataloader.ipynb) (summary)<br/>- [exercise-solutions.ipynb](ch02/01_main-chapter-code/exercise-solutions.ipynb) | [./ch02](./ch02) |
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| Ch 3: Coding Attention Mechanisms | - [ch03.ipynb](ch03/01_main-chapter-code/ch03.ipynb)<br/>- [multihead-attention.ipynb](ch03/01_main-chapter-code/multihead-attention.ipynb) (summary) <br/>- [exercise-solutions.ipynb](ch03/01_main-chapter-code/exercise-solutions.ipynb)| [./ch03](./ch03) |
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| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary) | [./ch04](./ch04) |
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| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary)<br/>- [exercise-solutions.ipynb](ch04/01_main-chapter-code/exercise-solutions.ipynb) | [./ch04](./ch04) |
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| Ch 5: Pretraining on Unlabeled Data | Q1 2024 | ... |
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| Ch 6: Finetuning for Text Classification | Q2 2024 | ... |
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| Ch 7: Finetuning with Human Feedback | Q2 2024 | ... |
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@ -942,12 +942,11 @@
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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[\"ctx_len\"], cfg[\"emb_dim\"])\n",
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" self.drop_emb = nn.Dropout(cfg[\"drop_rate\"])\n",
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" \n",
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" # Use a placeholder for TransformerBlock\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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" # Use a placeholder for LayerNorm\n",
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" self.final_norm = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.out_head = nn.Linear(\n",
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" cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False\n",
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@ -1210,7 +1209,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 26,
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"id": "c9b428a9-8764-4b36-80cd-7d4e00595ba6",
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"metadata": {},
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"outputs": [],
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@ -1264,7 +1263,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"execution_count": 27,
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"id": "bb3ffc8e-f95f-4a24-a978-939b8953ea3e",
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"metadata": {},
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"outputs": [
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@ -1282,7 +1281,7 @@
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" 0.0000], grad_fn=<SoftmaxBackward0>)"
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]
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},
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"execution_count": 54,
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1299,7 +1298,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 28,
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"id": "3d7e3e94-df0f-4c0f-a6a1-423f500ac1d3",
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"metadata": {},
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"outputs": [
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@ -1324,7 +1323,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 29,
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"id": "a72a9b60-de66-44cf-b2f9-1e638934ada4",
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"metadata": {},
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"outputs": [
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@ -1332,9 +1331,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Output: tensor([[15496, 11, 314, 716, 27018, 24086, 47843, 30961, 42348, 7267,\n",
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" 49706, 43231, 47062, 34657]])\n",
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"Output length: 14\n"
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"Output: tensor([[15496, 11, 314, 716, 27018, 24086, 47843, 30961, 42348, 7267]])\n",
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"Output length: 10\n"
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]
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}
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],
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@ -1344,7 +1342,7 @@
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"out = generate_text_simple(\n",
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" model=model,\n",
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" idx=encoded_tensor, \n",
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" max_new_tokens=10, \n",
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" max_new_tokens=6, \n",
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" context_size=GPT_CONFIG_124M[\"ctx_len\"]\n",
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")\n",
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"\n",
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@ -1362,7 +1360,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 30,
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"id": "053d99f6-5710-4446-8d52-117fb34ea9f6",
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"metadata": {},
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"outputs": [
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@ -1370,7 +1368,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Hello, I am Featureiman Byeswickattribute argue logger Normandy Compton analogous\n"
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"Hello, I am Featureiman Byeswickattribute argue\n"
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]
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}
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],
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381
ch04/01_main-chapter-code/exercise-solutions.ipynb
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381
ch04/01_main-chapter-code/exercise-solutions.ipynb
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@ -0,0 +1,381 @@
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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": "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": "markdown",
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"id": "33dfa199-9aee-41d4-a64b-7e3811b9a616",
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"metadata": {},
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"source": [
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"# Exercise 4.1: 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": 1,
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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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" \"ctx_len\": 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_ffn\": 0.1, # NEW: dropout for feed forward module\n",
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" \"drop_rate_attn\": 0.1, # NEW: dropout for multi-head attention \n",
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" \"drop_rate_resid\": 0.1, # NEW: dropout for residual 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": 2,
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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, GELU\n",
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"\n",
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"class FeedForward(nn.Module):\n",
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" def __init__(self, cfg):\n",
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" super().__init__()\n",
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" self.layers = nn.Sequential(\n",
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" nn.Linear(cfg[\"emb_dim\"], 4 * cfg[\"emb_dim\"]),\n",
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" GELU(),\n",
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" nn.Linear(4 * cfg[\"emb_dim\"], cfg[\"emb_dim\"]),\n",
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" nn.Dropout(cfg[\"drop_rate_ffn\"]) # NEW: dropout for feed forward module\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.layers(x)\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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" block_size=cfg[\"ctx_len\"],\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_resid = nn.Dropout(cfg[\"drop_rate_resid\"])\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_resid(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_resid(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[\"ctx_len\"], 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.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": 5,
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"id": "1d013d32-c275-4f42-be21-9010f1537227",
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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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"import tiktoken\n",
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"\n",
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"torch.manual_seed(123)\n",
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"model = GPTModel(GPT_CONFIG_124M)"
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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 4.2: 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": 24,
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"id": "2751b0e5-ffd3-4be2-8db3-e20dd4d61d69",
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"metadata": {},
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"outputs": [],
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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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" \"ctx_len\": 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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"model = TransformerBlock(GPT_CONFIG_124M)"
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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": 26,
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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": 27,
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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 feed forward 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": "0f7b7c7f-0fa1-4d30-ab44-e499edd55b6d",
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"metadata": {},
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"source": [
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"# Exercise 4.3: 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": 12,
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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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" \"ctx_len\": 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",
|
||||
"\n",
|
||||
" return GPT_CONFIG\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def calculate_size(model): # based on chapter code\n",
|
||||
" \n",
|
||||
" total_params = sum(p.numel() for p in model.parameters())\n",
|
||||
" print(f\"Total number of parameters: {total_params:,}\")\n",
|
||||
"\n",
|
||||
" total_params_gpt2 = total_params - sum(p.numel() for p in model.out_head.parameters())\n",
|
||||
" print(f\"Number of trainable parameters considering weight tying: {total_params_gpt2:,}\")\n",
|
||||
" \n",
|
||||
" # Calculate the total size in bytes (assuming float32, 4 bytes per parameter)\n",
|
||||
" total_size_bytes = total_params * 4\n",
|
||||
" \n",
|
||||
" # Convert to megabytes\n",
|
||||
" total_size_mb = total_size_bytes / (1024 * 1024)\n",
|
||||
" \n",
|
||||
" print(f\"Total size of the model: {total_size_mb:.2f} MB\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "2587e011-78a4-479c-a8fd-961cc40a5fd4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"gpt2-small:\n",
|
||||
"Total number of parameters: 163,009,536\n",
|
||||
"Number of trainable parameters considering weight tying: 124,412,160\n",
|
||||
"Total size of the model: 621.83 MB\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"gpt2-medium:\n",
|
||||
"Total number of parameters: 406,212,608\n",
|
||||
"Number of trainable parameters considering weight tying: 354,749,440\n",
|
||||
"Total size of the model: 1549.58 MB\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"gpt2-large:\n",
|
||||
"Total number of parameters: 838,220,800\n",
|
||||
"Number of trainable parameters considering weight tying: 773,891,840\n",
|
||||
"Total size of the model: 3197.56 MB\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"gpt2-xl:\n",
|
||||
"Total number of parameters: 1,637,792,000\n",
|
||||
"Number of trainable parameters considering weight tying: 1,557,380,800\n",
|
||||
"Total size of the model: 6247.68 MB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from gpt import GPTModel\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for model_abbrev in (\"small\", \"medium\", \"large\", \"xl\"):\n",
|
||||
" model_name = f\"gpt2-{model_abbrev}\"\n",
|
||||
" CONFIG = get_config(GPT_CONFIG_124M, model_name=model_name)\n",
|
||||
" model = GPTModel(CONFIG)\n",
|
||||
" print(f\"\\n\\n{model_name}:\")\n",
|
||||
" calculate_size(model)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -187,12 +187,11 @@ class GPTModel(nn.Module):
|
||||
super().__init__()
|
||||
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
|
||||
self.pos_emb = nn.Embedding(cfg["ctx_len"], cfg["emb_dim"])
|
||||
self.drop_emb = nn.Dropout(cfg["drop_rate"])
|
||||
|
||||
# Use a placeholder for TransformerBlock
|
||||
self.trf_blocks = nn.Sequential(
|
||||
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
|
||||
|
||||
# Use a placeholder for LayerNorm
|
||||
self.final_norm = LayerNorm(cfg["emb_dim"])
|
||||
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user