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Add PyPI package (#576)

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* Add PyPI package

* fixes

* fixes
This commit is contained in:
Sebastian Raschka
2025-03-23 19:28:49 -05:00
committed by GitHub
parent 7757c3d308
commit c21bfe4a23
52 changed files with 2173 additions and 2585 deletions
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6
.github/workflows/basic-tests-linux-uv.yml vendored
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@@ -66,3 +66,9 @@ jobs:
run: |
source .venv/bin/activate
pytest ch02/05_bpe-from-scratch/tests/tests.py
- name: Test Selected Bonus Materials
shell: bash
run: |
source .venv/bin/activate
pytest pkg/llms_from_scratch/tests/

19
appendix-D/01_main-chapter-code/appendix-D.ipynb
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@@ -68,6 +68,11 @@
"\n",
"\n",
"from previous_chapters import GPTModel\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"\n",
"GPT_CONFIG_124M = {\n",
" \"vocab_size\": 50257, # Vocabulary size\n",
@@ -139,6 +144,9 @@
"outputs": [],
"source": [
"from previous_chapters import create_dataloader_v1\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch02 import create_dataloader_v1\n",
"\n",
"\n",
"# Train/validation ratio\n",
"train_ratio = 0.90\n",
@@ -454,6 +462,9 @@
"outputs": [],
"source": [
"from previous_chapters import calc_loss_batch\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import calc_loss_batch\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
"model = GPTModel(GPT_CONFIG_124M)\n",
@@ -551,6 +562,9 @@
"outputs": [],
"source": [
"from previous_chapters import evaluate_model, generate_and_print_sample\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import evaluate_model, generate_and_print_samplee\n",
"\n",
"\n",
"ORIG_BOOK_VERSION = False\n",
"\n",
@@ -790,6 +804,9 @@
],
"source": [
"from previous_chapters import plot_losses\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import plot_losses\n",
"\n",
"\n",
"epochs_tensor = torch.linspace(1, n_epochs, len(train_losses))\n",
"plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)\n",
@@ -823,7 +840,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
}
},
"nbformat": 4,

21
appendix-E/01_main-chapter-code/appendix-E.ipynb
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@@ -198,6 +198,16 @@
" create_balanced_dataset,\n",
" random_split\n",
")\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch06 import (\n",
"# download_and_unzip_spam_data,\n",
"# create_balanced_dataset,\n",
"# random_split\n",
"# )\n",
"\n",
"\n",
"\n",
"url = \"https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip\"\n",
@@ -409,6 +419,10 @@
"source": [
"from gpt_download import download_and_load_gpt2\n",
"from previous_chapters import GPTModel, load_weights_into_gpt\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"# from llms_from_scratch.ch05 import load_weights_into_gpt\n",
"\n",
"\n",
"\n",
"CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
@@ -577,6 +591,9 @@
],
"source": [
"from previous_chapters import calc_accuracy_loader\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch06 import calc_accuracy_loader\n",
"\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
@@ -1387,6 +1404,8 @@
"source": [
"import time\n",
"from previous_chapters import train_classifier_simple\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch06 import train_classifier_simple\n",
"\n",
"\n",
"start_time = time.time()\n",
@@ -1442,6 +1461,8 @@
],
"source": [
"from previous_chapters import plot_values\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch06 import plot_values\n",
"\n",
"epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))\n",
"examples_seen_tensor = torch.linspace(0, examples_seen, len(train_losses))\n",

6
ch04/01_main-chapter-code/ch04.ipynb
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@@ -970,6 +970,12 @@
"metadata": {},
"outputs": [],
"source": [
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch03 import MultiHeadAttention\n",
"\n",
"from previous_chapters import MultiHeadAttention\n",
"\n",
"\n",

1
ch04/02_performance-analysis/README.md
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@@ -1,5 +1,4 @@
# Chapter 4: Implementing a GPT Model from Scratch To Generate Text
- [flops-analysis.ipynb](flops-analysis.ipynb) analyses the floating point operations per second (FLOPS) of the GPT model(s) implemented in the main chapter.
- [previous_chapters.py](previous_chapters.py) is a Python module containing the `GPTModel` code we implemented in chapter 4 and other code implemented in previous chapters, which we import in the analysis notebook.
- `requirements-extra.txt` includes additional Python libraries that need to be installed (via `pip install -r requirements-extra.txt`.

6
ch04/02_performance-analysis/flops-analysis.ipynb
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@@ -127,7 +127,9 @@
"import torch\n",
"from thop import profile\n",
"\n",
"from previous_chapters import GPTModel\n",
"# For installation instructions, see:\n",
"# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"from llms_from_scratch.ch04 import GPTModel\n",
"\n",
"\n",
"BASE_CONFIG = {\n",
@@ -550,7 +552,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
}
},
"nbformat": 4,

12
ch05/01_main-chapter-code/ch05.ipynb
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@@ -147,6 +147,11 @@
"source": [
"import torch\n",
"from previous_chapters import GPTModel\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"\n",
"GPT_CONFIG_124M = {\n",
" \"vocab_size\": 50257, # Vocabulary size\n",
@@ -212,6 +217,9 @@
"import tiktoken\n",
"from previous_chapters import generate_text_simple\n",
"\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch04 import generate_text_simple\n",
"\n",
"def text_to_token_ids(text, tokenizer):\n",
" encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})\n",
" encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension\n",
@@ -924,6 +932,8 @@
"outputs": [],
"source": [
"from previous_chapters import create_dataloader_v1\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch02 import create_dataloader_v1\n",
"\n",
"# Train/validation ratio\n",
"train_ratio = 0.90\n",
@@ -2548,7 +2558,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
"version": "3.10.16"
}
},
"nbformat": 4,

293
ch05/02_alternative_weight_loading/previous_chapters.py
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@@ -1,293 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-4.
# This file can be run as a standalone script.
import tiktoken
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
#####################################
# Chapter 2
#####################################
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.input_ids = []
self.target_ids = []
# Tokenize the entire text
token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
# Use a sliding window to chunk the book into overlapping sequences of max_length
for i in range(0, len(token_ids) - max_length, stride):
input_chunk = token_ids[i:i + max_length]
target_chunk = token_ids[i + 1: i + max_length + 1]
self.input_ids.append(torch.tensor(input_chunk))
self.target_ids.append(torch.tensor(target_chunk))
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True, num_workers=0):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
# Create dataset
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
# Create dataloader
dataloader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
return dataloader
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
def generate_text_simple(model, idx, max_new_tokens, context_size):
# idx is (B, T) array of indices in the current context
for _ in range(max_new_tokens):
# Crop current context if it exceeds the supported context size
# E.g., if LLM supports only 5 tokens, and the context size is 10
# then only the last 5 tokens are used as context
idx_cond = idx[:, -context_size:]
# Get the predictions
with torch.no_grad():
logits = model(idx_cond)
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
# Get the idx of the vocab entry with the highest logits value
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
# Append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
return idx
#####################################
# Chapter 5
#####################################
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text)
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
# For-loop is the same as before: Get logits, and only focus on last time step
for _ in range(max_new_tokens):
idx_cond = idx[:, -context_size:]
with torch.no_grad():
logits = model(idx_cond)
logits = logits[:, -1, :]
# New: Filter logits with top_k sampling
if top_k is not None:
# Keep only top_k values
top_logits, _ = torch.topk(logits, top_k)
min_val = top_logits[:, -1]
logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
# New: Apply temperature scaling
if temperature > 0.0:
logits = logits / temperature
# Apply softmax to get probabilities
probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
# Sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
# Otherwise same as before: get idx of the vocab entry with the highest logits value
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
break
# Same as before: append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
return idx

7
ch05/02_alternative_weight_loading/weight-loading-hf-safetensors.ipynb
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@@ -95,7 +95,9 @@
"metadata": {},
"outputs": [],
"source": [
"from previous_chapters import GPTModel"
"from llms_from_scratch.ch04 import GPTModel\n",
"# For llms_from_scratch installation instructions, see:\n",
"# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg"
]
},
{
@@ -270,7 +272,8 @@
],
"source": [
"import tiktoken\n",
"from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
"from llms_from_scratch.ch05 import generate, text_to_token_ids, token_ids_to_text\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
"\n",

7
ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb
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@@ -230,7 +230,9 @@
"outputs": [],
"source": [
"import torch\n",
"from previous_chapters import GPTModel\n",
"from llms_from_scratch.ch04 import GPTModel\n",
"# For llms_from_scratch installation instructions, see:\n",
"# https://github.com/rasbt/LLMs-from-scratch/tree/main/\n",
"\n",
"\n",
"gpt = GPTModel(BASE_CONFIG)\n",
@@ -258,7 +260,8 @@
],
"source": [
"import tiktoken\n",
"from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
"from llms_from_scratch.ch05 import generate, text_to_token_ids, token_ids_to_text\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
"\n",

14
ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py
View File

@@ -17,14 +17,12 @@ from pathlib import Path
import time
import tiktoken
import torch
from previous_chapters import (
create_dataloader_v1,
GPTModel,
generate_and_print_sample,
calc_loss_batch,
evaluate_model,
plot_losses
)
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch02 import create_dataloader_v1
from llms_from_scratch.ch04 import GPTModel, generate_and_print_sample
from llms_from_scratch.ch05 import calc_loss_batch, evaluate_model, plot_losses
def read_text_file(file_path):

317
ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
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@@ -1,317 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-4.
# This file can be run as a standalone script.
import tiktoken
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
#####################################
# Chapter 2
#####################################
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.input_ids = []
self.target_ids = []
token_ids = tokenizer.encode(txt, allowed_special={'<|endoftext|>'})
for i in range(0, len(token_ids) - max_length, stride):
input_chunk = token_ids[i:i + max_length]
target_chunk = token_ids[i + 1: i + max_length + 1]
self.input_ids.append(torch.tensor(input_chunk))
self.target_ids.append(torch.tensor(target_chunk))
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True, num_workers=0):
tokenizer = tiktoken.get_encoding("gpt2")
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
dataloader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
return dataloader
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
def generate_text_simple(model, idx, max_new_tokens, context_size):
# idx is (B, T) array of indices in the current context
for _ in range(max_new_tokens):
# Crop current context if it exceeds the supported context size
# E.g., if LLM supports only 5 tokens, and the context size is 10
# then only the last 5 tokens are used as context
idx_cond = idx[:, -context_size:]
# Get the predictions
with torch.no_grad():
logits = model(idx_cond)
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
# Get the idx of the vocab entry with the highest logits value
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
# Append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
return idx
#####################################
# Chapter 5
####################################
def calc_loss_batch(input_batch, target_batch, model, device):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)
loss = torch.nn.functional.cross_entropy(logits.flatten(0, 1), target_batch.flatten())
return loss
def calc_loss_loader(data_loader, model, device, num_batches=None):
total_loss = 0.
if len(data_loader) == 0:
return float("nan")
elif num_batches is None:
num_batches = len(data_loader)
else:
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
loss = calc_loss_batch(input_batch, target_batch, model, device)
total_loss += loss.item()
else:
break
return total_loss / num_batches
def evaluate_model(model, train_loader, val_loader, device, eval_iter):
model.eval()
with torch.no_grad():
train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
model.train()
return train_loss, val_loss
def generate_and_print_sample(model, tokenizer, device, start_context):
model.eval()
context_size = model.pos_emb.weight.shape[0]
encoded = text_to_token_ids(start_context, tokenizer).to(device)
with torch.no_grad():
token_ids = generate_text_simple(
model=model, idx=encoded,
max_new_tokens=50, context_size=context_size)
decoded_text = token_ids_to_text(token_ids, tokenizer)
print(decoded_text.replace("\n", " ")) # Compact print format
model.train()
def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses, output_dir):
fig, ax1 = plt.subplots()
# Plot training and validation loss against epochs
ax1.plot(epochs_seen, train_losses, label="Training loss")
ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
ax1.set_xlabel("Epochs")
ax1.set_ylabel("Loss")
ax1.legend(loc="upper right")
ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
# Create a second x-axis for tokens seen
ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
ax2.plot(tokens_seen, train_losses, alpha=0) # Invisible plot for aligning ticks
ax2.set_xlabel("Tokens seen")
fig.tight_layout() # Adjust layout to make room
plt.savefig(output_dir / "losses.pdf")
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # Add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # Remove batch dimension
return tokenizer.decode(flat.tolist())

6
ch05/05_bonus_hparam_tuning/hparam_search.py
View File

@@ -8,7 +8,11 @@ import math
import os
import tiktoken
import torch
from previous_chapters import GPTModel, create_dataloader_v1
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch02 import create_dataloader_v1
from llms_from_scratch.ch04 import GPTModel
# Define a grid of hyperparameters to search over

279
ch05/05_bonus_hparam_tuning/previous_chapters.py
View File

@@ -1,279 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-4.
# This file can be run as a standalone script.
import tiktoken
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
#####################################
# Chapter 2
#####################################
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.input_ids = []
self.target_ids = []
# Tokenize the entire text
token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
# Use a sliding window to chunk the book into overlapping sequences of max_length
for i in range(0, len(token_ids) - max_length, stride):
input_chunk = token_ids[i:i + max_length]
target_chunk = token_ids[i + 1: i + max_length + 1]
self.input_ids.append(torch.tensor(input_chunk))
self.target_ids.append(torch.tensor(target_chunk))
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True, num_workers=0):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
# Create dataset
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
# Create dataloader
dataloader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
return dataloader
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
def generate_text_simple(model, idx, max_new_tokens, context_size):
# idx is (B, T) array of indices in the current context
for _ in range(max_new_tokens):
# Crop current context if it exceeds the supported context size
# E.g., if LLM supports only 5 tokens, and the context size is 10
# then only the last 5 tokens are used as context
idx_cond = idx[:, -context_size:]
# Get the predictions
with torch.no_grad():
logits = model(idx_cond)
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
# Get the idx of the vocab entry with the highest logits value
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
# Append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
return idx
if __name__ == "__main__":
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-Key-Value bias
}
torch.manual_seed(123)
model = GPTModel(GPT_CONFIG_124M)
model.eval() # disable dropout
start_context = "Hello, I am"
tokenizer = tiktoken.get_encoding("gpt2")
encoded = tokenizer.encode(start_context)
encoded_tensor = torch.tensor(encoded).unsqueeze(0)
print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
print("\nInput text:", start_context)
print("Encoded input text:", encoded)
print("encoded_tensor.shape:", encoded_tensor.shape)
out = generate_text_simple(
model=model,
idx=encoded_tensor,
max_new_tokens=10,
context_size=GPT_CONFIG_124M["context_length"]
)
decoded_text = tokenizer.decode(out.squeeze(0).tolist())
print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
print("\nOutput:", out)
print("Output length:", len(out[0]))
print("Output text:", decoded_text)

6
ch05/06_user_interface/app_orig.py
View File

@@ -7,10 +7,12 @@ import tiktoken
import torch
import chainlit
from previous_chapters import (
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch05 import (
download_and_load_gpt2,
generate,
GPTModel,
load_weights_into_gpt,
text_to_token_ids,
token_ids_to_text,

7
ch05/06_user_interface/app_own.py
View File

@@ -10,13 +10,16 @@ import tiktoken
import torch
import chainlit
from previous_chapters import (
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch05 import (
generate,
GPTModel,
text_to_token_ids,
token_ids_to_text,
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

384
ch05/06_user_interface/previous_chapters.py
View File

@@ -1,384 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-5.
import json
import os
import urllib
import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
from tqdm import tqdm
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
#####################################
# Chapter 5
#####################################
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text)
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
def download_and_load_gpt2(model_size, models_dir):
# Validate model size
allowed_sizes = ("124M", "355M", "774M", "1558M")
if model_size not in allowed_sizes:
raise ValueError(f"Model size not in {allowed_sizes}")
# Define paths
model_dir = os.path.join(models_dir, model_size)
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
filenames = [
"checkpoint", "encoder.json", "hparams.json",
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
"model.ckpt.meta", "vocab.bpe"
]
# Download files
os.makedirs(model_dir, exist_ok=True)
for filename in filenames:
file_url = os.path.join(base_url, model_size, filename)
file_path = os.path.join(model_dir, filename)
download_file(file_url, file_path)
# Load settings and params
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
settings = json.load(open(os.path.join(model_dir, "hparams.json")))
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
# Initialize parameters dictionary with empty blocks for each layer
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
# Iterate over each variable in the checkpoint
for name, _ in tf.train.list_variables(ckpt_path):
# Load the variable and remove singleton dimensions
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
# Process the variable name to extract relevant parts
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
# Identify the target dictionary for the variable
target_dict = params
if variable_name_parts[0].startswith("h"):
layer_number = int(variable_name_parts[0][1:])
target_dict = params["blocks"][layer_number]
# Recursively access or create nested dictionaries
for key in variable_name_parts[1:-1]:
target_dict = target_dict.setdefault(key, {})
# Assign the variable array to the last key
last_key = variable_name_parts[-1]
target_dict[last_key] = variable_array
return params
def assign(left, right):
if left.shape != right.shape:
raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
return torch.nn.Parameter(torch.tensor(right))
def load_weights_into_gpt(gpt, params):
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
for b in range(len(params["blocks"])):
q_w, k_w, v_w = np.split(
(params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.weight = assign(
gpt.trf_blocks[b].att.W_query.weight, q_w.T)
gpt.trf_blocks[b].att.W_key.weight = assign(
gpt.trf_blocks[b].att.W_key.weight, k_w.T)
gpt.trf_blocks[b].att.W_value.weight = assign(
gpt.trf_blocks[b].att.W_value.weight, v_w.T)
q_b, k_b, v_b = np.split(
(params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.bias = assign(
gpt.trf_blocks[b].att.W_query.bias, q_b)
gpt.trf_blocks[b].att.W_key.bias = assign(
gpt.trf_blocks[b].att.W_key.bias, k_b)
gpt.trf_blocks[b].att.W_value.bias = assign(
gpt.trf_blocks[b].att.W_value.bias, v_b)
gpt.trf_blocks[b].att.out_proj.weight = assign(
gpt.trf_blocks[b].att.out_proj.weight,
params["blocks"][b]["attn"]["c_proj"]["w"].T)
gpt.trf_blocks[b].att.out_proj.bias = assign(
gpt.trf_blocks[b].att.out_proj.bias,
params["blocks"][b]["attn"]["c_proj"]["b"])
gpt.trf_blocks[b].ff.layers[0].weight = assign(
gpt.trf_blocks[b].ff.layers[0].weight,
params["blocks"][b]["mlp"]["c_fc"]["w"].T)
gpt.trf_blocks[b].ff.layers[0].bias = assign(
gpt.trf_blocks[b].ff.layers[0].bias,
params["blocks"][b]["mlp"]["c_fc"]["b"])
gpt.trf_blocks[b].ff.layers[2].weight = assign(
gpt.trf_blocks[b].ff.layers[2].weight,
params["blocks"][b]["mlp"]["c_proj"]["w"].T)
gpt.trf_blocks[b].ff.layers[2].bias = assign(
gpt.trf_blocks[b].ff.layers[2].bias,
params["blocks"][b]["mlp"]["c_proj"]["b"])
gpt.trf_blocks[b].norm1.scale = assign(
gpt.trf_blocks[b].norm1.scale,
params["blocks"][b]["ln_1"]["g"])
gpt.trf_blocks[b].norm1.shift = assign(
gpt.trf_blocks[b].norm1.shift,
params["blocks"][b]["ln_1"]["b"])
gpt.trf_blocks[b].norm2.scale = assign(
gpt.trf_blocks[b].norm2.scale,
params["blocks"][b]["ln_2"]["g"])
gpt.trf_blocks[b].norm2.shift = assign(
gpt.trf_blocks[b].norm2.shift,
params["blocks"][b]["ln_2"]["b"])
gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
# For-loop is the same as before: Get logits, and only focus on last time step
for _ in range(max_new_tokens):
idx_cond = idx[:, -context_size:]
with torch.no_grad():
logits = model(idx_cond)
logits = logits[:, -1, :]
# New: Filter logits with top_k sampling
if top_k is not None:
# Keep only top_k values
top_logits, _ = torch.topk(logits, top_k)
min_val = top_logits[:, -1]
logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
# New: Apply temperature scaling
if temperature > 0.0:
logits = logits / temperature
# Apply softmax to get probabilities
probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
# Sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
# Otherwise same as before: get idx of the vocab entry with the highest logits value
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
break
# Same as before: append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
return idx

8
ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb
View File

@@ -1264,6 +1264,12 @@
],
"source": [
"from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch05 import generate, text_to_token_ids, token_ids_to_text\n",
"\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
@@ -1691,7 +1697,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {

5
ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
View File

@@ -1324,6 +1324,11 @@
],
"source": [
"from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch05 import generate, text_to_token_ids, token_ids_to_text\n",
"\n",
"\n",
"torch.manual_seed(123)\n",

8
ch05/08_memory_efficient_weight_loading/memory-efficient-state-dict.ipynb
View File

@@ -161,6 +161,12 @@
"outputs": [],
"source": [
"from previous_chapters import GPTModel\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"\n",
"\n",
"\n",
"BASE_CONFIG = {\n",
@@ -921,7 +927,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
}
},
"nbformat": 4,

12
ch05/09_extending-tokenizers/extend-tiktoken.ipynb
View File

@@ -301,8 +301,9 @@
}
],
"source": [
"# Relative import from the gpt_download.py contained in this folder\n",
"from gpt_download import download_and_load_gpt2\n",
"from llms_from_scratch.ch05 import download_and_load_gpt2\n",
"# For llms_from_scratch installation instructions, see:\n",
"# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"\n",
"settings, params = download_and_load_gpt2(model_size=\"124M\", models_dir=\"gpt2\")"
]
@@ -314,8 +315,9 @@
"metadata": {},
"outputs": [],
"source": [
"# Relative import from the gpt_download.py contained in this folder\n",
"from previous_chapters import GPTModel\n",
"from llms_from_scratch.ch04 import GPTModel\n",
"# For llms_from_scratch installation instructions, see:\n",
"# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"\n",
"GPT_CONFIG_124M = {\n",
" \"vocab_size\": 50257, # Vocabulary size\n",
@@ -763,7 +765,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
}
},
"nbformat": 4,

157
ch05/09_extending-tokenizers/gpt_download.py
View File

@@ -1,157 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import os
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
from tqdm import tqdm
def download_and_load_gpt2(model_size, models_dir):
# Validate model size
allowed_sizes = ("124M", "355M", "774M", "1558M")
if model_size not in allowed_sizes:
raise ValueError(f"Model size not in {allowed_sizes}")
# Define paths
model_dir = os.path.join(models_dir, model_size)
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
backup_base_url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/gpt2"
filenames = [
"checkpoint", "encoder.json", "hparams.json",
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
"model.ckpt.meta", "vocab.bpe"
]
# Download files
os.makedirs(model_dir, exist_ok=True)
for filename in filenames:
file_url = os.path.join(base_url, model_size, filename)
backup_url = os.path.join(backup_base_url, model_size, filename)
file_path = os.path.join(model_dir, filename)
download_file(file_url, file_path, backup_url)
# Load settings and params
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
settings = json.load(open(os.path.join(model_dir, "hparams.json"), "r", encoding="utf-8"))
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
return settings, params
def download_file(url, destination, backup_url=None):
def _attempt_download(download_url):
with urllib.request.urlopen(download_url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return True # Indicate success without re-downloading
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(download_url)
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
with open(destination, "wb") as file:
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk))
return True
try:
if _attempt_download(url):
return
except (urllib.error.HTTPError, urllib.error.URLError):
if backup_url is not None:
print(f"Primary URL ({url}) failed. Attempting backup URL: {backup_url}")
try:
if _attempt_download(backup_url):
return
except urllib.error.HTTPError:
pass
# If we reach here, both attempts have failed
error_message = (
f"Failed to download from both primary URL ({url})"
f"{' and backup URL (' + backup_url + ')' if backup_url else ''}."
"\nCheck your internet connection or the file availability.\n"
"For help, visit: https://github.com/rasbt/LLMs-from-scratch/discussions/273"
)
print(error_message)
except Exception as e:
print(f"An unexpected error occurred: {e}")
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("content-length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = url.split("/")[-1] # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Iterate over the file data in chunks
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
# Initialize parameters dictionary with empty blocks for each layer
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
# Iterate over each variable in the checkpoint
for name, _ in tf.train.list_variables(ckpt_path):
# Load the variable and remove singleton dimensions
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
# Process the variable name to extract relevant parts
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
# Identify the target dictionary for the variable
target_dict = params
if variable_name_parts[0].startswith("h"):
layer_number = int(variable_name_parts[0][1:])
target_dict = params["blocks"][layer_number]
# Recursively access or create nested dictionaries
for key in variable_name_parts[1:-1]:
target_dict = target_dict.setdefault(key, {})
# Assign the variable array to the last key
last_key = variable_name_parts[-1]
target_dict[last_key] = variable_array
return params

279
ch05/09_extending-tokenizers/previous_chapters.py
View File

@@ -1,279 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-4.
# This file can be run as a standalone script.
import tiktoken
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
#####################################
# Chapter 2
#####################################
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.input_ids = []
self.target_ids = []
# Tokenize the entire text
token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
# Use a sliding window to chunk the book into overlapping sequences of max_length
for i in range(0, len(token_ids) - max_length, stride):
input_chunk = token_ids[i:i + max_length]
target_chunk = token_ids[i + 1: i + max_length + 1]
self.input_ids.append(torch.tensor(input_chunk))
self.target_ids.append(torch.tensor(target_chunk))
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True, num_workers=0):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
# Create dataset
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
# Create dataloader
dataloader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
return dataloader
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
def generate_text_simple(model, idx, max_new_tokens, context_size):
# idx is (B, T) array of indices in the current context
for _ in range(max_new_tokens):
# Crop current context if it exceeds the supported context size
# E.g., if LLM supports only 5 tokens, and the context size is 10
# then only the last 5 tokens are used as context
idx_cond = idx[:, -context_size:]
# Get the predictions
with torch.no_grad():
logits = model(idx_cond)
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
# Get the idx of the vocab entry with the highest logits value
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
# Append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
return idx
if __name__ == "__main__":
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-Key-Value bias
}
torch.manual_seed(123)
model = GPTModel(GPT_CONFIG_124M)
model.eval() # disable dropout
start_context = "Hello, I am"
tokenizer = tiktoken.get_encoding("gpt2")
encoded = tokenizer.encode(start_context)
encoded_tensor = torch.tensor(encoded).unsqueeze(0)
print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
print("\nInput text:", start_context)
print("Encoded input text:", encoded)
print("encoded_tensor.shape:", encoded_tensor.shape)
out = generate_text_simple(
model=model,
idx=encoded_tensor,
max_new_tokens=10,
context_size=GPT_CONFIG_124M["context_length"]
)
decoded_text = tokenizer.decode(out.squeeze(0).tolist())
print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
print("\nOutput:", out)
print("Output length:", len(out[0]))
print("Output text:", decoded_text)

13
ch06/01_main-chapter-code/ch06.ipynb
View File

@@ -1035,6 +1035,12 @@
"source": [
"from gpt_download import download_and_load_gpt2\n",
"from previous_chapters import GPTModel, load_weights_into_gpt\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"# from llms_from_scratch.ch05 import download_and_load_gpt2, load_weights_into_gpt\n",
"\n",
"model_size = CHOOSE_MODEL.split(\" \")[-1].lstrip(\"(\").rstrip(\")\")\n",
"settings, params = download_and_load_gpt2(model_size=model_size, models_dir=\"gpt2\")\n",
@@ -1075,6 +1081,13 @@
" token_ids_to_text\n",
")\n",
"\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import (\n",
"# generate_text_simple,\n",
"# text_to_token_ids,\n",
"# token_ids_to_text\n",
"# )\n",
"\n",
"\n",
"text_1 = \"Every effort moves you\"\n",
"\n",

2
ch06/02_bonus_additional-experiments/README.md
View File

@@ -55,7 +55,7 @@ You can use the following code to reproduce the experiments:
- Row 16: `python additional_experiments.py --trainable_token_pos "flexible"`
- Row 17: `python additional_experiments.py --disable_causal_mask`
- Row 18: `python additional_experiments.py --ignore_index 50256`
- Row 19: `python additional_experiments.py --average embeddings`
- Row 19: `python additional_experiments.py --average_embeddings`
I've kept the LLM and dataset small on purpose, so you can run the training on a regular laptop like a MacBook Air M3 in about 15 minutes (for the default setting) in case you don't have access to a GPU.

8
ch06/02_bonus_additional-experiments/additional_experiments.py
View File

@@ -21,6 +21,14 @@ from gpt_download import download_and_load_gpt2
from previous_chapters import GPTModel, load_weights_into_gpt
# If the `previous_chapters.py` file is not available locally,
# you can import it from the `llms-from-scratch` PyPI package.
# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
# E.g.,
# from llms_from_scratch.ch04 import GPTModel
# from llms_from_scratch.ch05 import download_and_load_gpt2, load_weights_into_gpt
class LoRALayer(torch.nn.Module):
def __init__(self, in_dim, out_dim, rank, alpha):
super().__init__()

9
ch06/04_user_interface/app.py
View File

@@ -10,10 +10,11 @@ import tiktoken
import torch
import chainlit
from previous_chapters import (
classify_review,
GPTModel
)
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch06 import classify_review
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

371
ch06/04_user_interface/previous_chapters.py
View File

@@ -1,371 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-5.
import json
import os
import urllib
import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
from tqdm import tqdm
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
#####################################
# Chapter 5
#####################################
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text)
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
def download_and_load_gpt2(model_size, models_dir):
# Validate model size
allowed_sizes = ("124M", "355M", "774M", "1558M")
if model_size not in allowed_sizes:
raise ValueError(f"Model size not in {allowed_sizes}")
# Define paths
model_dir = os.path.join(models_dir, model_size)
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
filenames = [
"checkpoint", "encoder.json", "hparams.json",
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
"model.ckpt.meta", "vocab.bpe"
]
# Download files
os.makedirs(model_dir, exist_ok=True)
for filename in filenames:
file_url = os.path.join(base_url, model_size, filename)
file_path = os.path.join(model_dir, filename)
download_file(file_url, file_path)
# Load settings and params
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
settings = json.load(open(os.path.join(model_dir, "hparams.json")))
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
# Initialize parameters dictionary with empty blocks for each layer
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
# Iterate over each variable in the checkpoint
for name, _ in tf.train.list_variables(ckpt_path):
# Load the variable and remove singleton dimensions
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
# Process the variable name to extract relevant parts
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
# Identify the target dictionary for the variable
target_dict = params
if variable_name_parts[0].startswith("h"):
layer_number = int(variable_name_parts[0][1:])
target_dict = params["blocks"][layer_number]
# Recursively access or create nested dictionaries
for key in variable_name_parts[1:-1]:
target_dict = target_dict.setdefault(key, {})
# Assign the variable array to the last key
last_key = variable_name_parts[-1]
target_dict[last_key] = variable_array
return params
def assign(left, right):
if left.shape != right.shape:
raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
return torch.nn.Parameter(torch.tensor(right))
def load_weights_into_gpt(gpt, params):
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
for b in range(len(params["blocks"])):
q_w, k_w, v_w = np.split(
(params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.weight = assign(
gpt.trf_blocks[b].att.W_query.weight, q_w.T)
gpt.trf_blocks[b].att.W_key.weight = assign(
gpt.trf_blocks[b].att.W_key.weight, k_w.T)
gpt.trf_blocks[b].att.W_value.weight = assign(
gpt.trf_blocks[b].att.W_value.weight, v_w.T)
q_b, k_b, v_b = np.split(
(params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.bias = assign(
gpt.trf_blocks[b].att.W_query.bias, q_b)
gpt.trf_blocks[b].att.W_key.bias = assign(
gpt.trf_blocks[b].att.W_key.bias, k_b)
gpt.trf_blocks[b].att.W_value.bias = assign(
gpt.trf_blocks[b].att.W_value.bias, v_b)
gpt.trf_blocks[b].att.out_proj.weight = assign(
gpt.trf_blocks[b].att.out_proj.weight,
params["blocks"][b]["attn"]["c_proj"]["w"].T)
gpt.trf_blocks[b].att.out_proj.bias = assign(
gpt.trf_blocks[b].att.out_proj.bias,
params["blocks"][b]["attn"]["c_proj"]["b"])
gpt.trf_blocks[b].ff.layers[0].weight = assign(
gpt.trf_blocks[b].ff.layers[0].weight,
params["blocks"][b]["mlp"]["c_fc"]["w"].T)
gpt.trf_blocks[b].ff.layers[0].bias = assign(
gpt.trf_blocks[b].ff.layers[0].bias,
params["blocks"][b]["mlp"]["c_fc"]["b"])
gpt.trf_blocks[b].ff.layers[2].weight = assign(
gpt.trf_blocks[b].ff.layers[2].weight,
params["blocks"][b]["mlp"]["c_proj"]["w"].T)
gpt.trf_blocks[b].ff.layers[2].bias = assign(
gpt.trf_blocks[b].ff.layers[2].bias,
params["blocks"][b]["mlp"]["c_proj"]["b"])
gpt.trf_blocks[b].norm1.scale = assign(
gpt.trf_blocks[b].norm1.scale,
params["blocks"][b]["ln_1"]["g"])
gpt.trf_blocks[b].norm1.shift = assign(
gpt.trf_blocks[b].norm1.shift,
params["blocks"][b]["ln_1"]["b"])
gpt.trf_blocks[b].norm2.scale = assign(
gpt.trf_blocks[b].norm2.scale,
params["blocks"][b]["ln_2"]["g"])
gpt.trf_blocks[b].norm2.shift = assign(
gpt.trf_blocks[b].norm2.shift,
params["blocks"][b]["ln_2"]["b"])
gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
#####################################
# Chapter 6
#####################################
def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):
model.eval()
# Prepare inputs to the model
input_ids = tokenizer.encode(text)
supported_context_length = model.pos_emb.weight.shape[0]
# Truncate sequences if they too long
input_ids = input_ids[:min(max_length, supported_context_length)]
# Pad sequences to the longest sequence
input_ids += [pad_token_id] * (max_length - len(input_ids))
input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension
# Model inference
with torch.no_grad():
logits = model(input_tensor.to(device))[:, -1, :] # Logits of the last output token
predicted_label = torch.argmax(logits, dim=-1).item()
# Return the classified result
return "spam" if predicted_label == 1 else "not spam"

22
ch07/01_main-chapter-code/ch07.ipynb
View File

@@ -1541,6 +1541,12 @@
"source": [
"from gpt_download import download_and_load_gpt2\n",
"from previous_chapters import GPTModel, load_weights_into_gpt\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"# from llms_from_scratch.ch05 import download_and_load_gpt2, load_weights_into_gpt\n",
"\n",
"\n",
"BASE_CONFIG = {\n",
@@ -1626,6 +1632,13 @@
" text_to_token_ids,\n",
" token_ids_to_text\n",
")\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import (\n",
"# generate_text_simple,\n",
"# text_to_token_ids,\n",
"# token_ids_to_text\n",
"# )\n",
"\n",
"\n",
"token_ids = generate(\n",
" model=model,\n",
@@ -1727,7 +1740,12 @@
"from previous_chapters import (\n",
" calc_loss_loader,\n",
" train_model_simple\n",
")"
")\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import (\n",
"# calc_loss_loader,\n",
"# train_model_simple,\n",
"# )\n"
]
},
{
@@ -1939,6 +1957,8 @@
],
"source": [
"from previous_chapters import plot_losses\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import plot_losses\n",
"\n",
"epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))\n",
"plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)"

37
ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb
View File

@@ -1613,6 +1613,11 @@
"outputs": [],
"source": [
"from previous_chapters import GPTModel\n",
"# If the `previous_chapters.py` file is not available locally,\n",
"# you can import it from the `llms-from-scratch` PyPI package.\n",
"# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
"# E.g.,\n",
"# from llms_from_scratch.ch04 import GPTModel\n",
"\n",
"\n",
"BASE_CONFIG = {\n",
@@ -1715,6 +1720,12 @@
" text_to_token_ids,\n",
" token_ids_to_text\n",
")\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 (\n",
"# generate,\n",
"# text_to_token_ids,\n",
"# token_ids_to_text\n",
"# )\n",
"\n",
"torch.manual_seed(123)\n",
"\n",
@@ -2382,6 +2393,16 @@
" - we also track the rewards and reward margins, which are commonly used in RLHF and DPO contexts to track the training progress\n"
]
},
{
"cell_type": "markdown",
"id": "820d4904-f819-4d62-bfb4-85cf28863683",
"metadata": {
"id": "820d4904-f819-4d62-bfb4-85cf28863683"
},
"source": [
"- Before we start the training, let's print the initial losses and rewards:"
]
},
{
"cell_type": "code",
"execution_count": 47,
@@ -2392,6 +2413,8 @@
"outputs": [],
"source": [
"from previous_chapters import generate_and_print_sample\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch04 import generate_text_simple\n",
"\n",
"\n",
"def train_model_dpo_simple(\n",
@@ -2471,16 +2494,6 @@
" return tracking"
]
},
{
"cell_type": "markdown",
"id": "820d4904-f819-4d62-bfb4-85cf28863683",
"metadata": {
"id": "820d4904-f819-4d62-bfb4-85cf28863683"
},
"source": [
"- Before we start the training, let's print the initial losses and rewards:"
]
},
{
"cell_type": "code",
"execution_count": 48,
@@ -2776,6 +2789,8 @@
],
"source": [
"from previous_chapters import plot_losses\n",
"# Alternatively:\n",
"# from llms_from_scratch.ch05 import plot_losses\n",
"\n",
"\n",
"epochs_tensor = torch.linspace(0, num_epochs, len(tracking[\"train_losses\"]))\n",
@@ -3112,7 +3127,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.16"
}
},
"nbformat": 4,

7
ch07/06_user_interface/app.py
View File

@@ -10,9 +10,12 @@ import tiktoken
import torch
import chainlit
from previous_chapters import (
# For llms_from_scratch installation instructions, see:
# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch05 import (
generate,
GPTModel,
text_to_token_ids,
token_ids_to_text,
)

384
ch07/06_user_interface/previous_chapters.py
View File

@@ -1,384 +0,0 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-5.
import json
import os
import urllib
import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
from tqdm import tqdm
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut # Add the original input back
return x
class GPTModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(
*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
def forward(self, in_idx):
batch_size, seq_len = in_idx.shape
tok_embeds = self.tok_emb(in_idx)
pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
#####################################
# Chapter 5
#####################################
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text)
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
def download_and_load_gpt2(model_size, models_dir):
# Validate model size
allowed_sizes = ("124M", "355M", "774M", "1558M")
if model_size not in allowed_sizes:
raise ValueError(f"Model size not in {allowed_sizes}")
# Define paths
model_dir = os.path.join(models_dir, model_size)
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
filenames = [
"checkpoint", "encoder.json", "hparams.json",
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
"model.ckpt.meta", "vocab.bpe"
]
# Download files
os.makedirs(model_dir, exist_ok=True)
for filename in filenames:
file_url = os.path.join(base_url, model_size, filename)
file_path = os.path.join(model_dir, filename)
download_file(file_url, file_path)
# Load settings and params
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
settings = json.load(open(os.path.join(model_dir, "hparams.json")))
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
# Initialize parameters dictionary with empty blocks for each layer
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
# Iterate over each variable in the checkpoint
for name, _ in tf.train.list_variables(ckpt_path):
# Load the variable and remove singleton dimensions
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
# Process the variable name to extract relevant parts
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
# Identify the target dictionary for the variable
target_dict = params
if variable_name_parts[0].startswith("h"):
layer_number = int(variable_name_parts[0][1:])
target_dict = params["blocks"][layer_number]
# Recursively access or create nested dictionaries
for key in variable_name_parts[1:-1]:
target_dict = target_dict.setdefault(key, {})
# Assign the variable array to the last key
last_key = variable_name_parts[-1]
target_dict[last_key] = variable_array
return params
def assign(left, right):
if left.shape != right.shape:
raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
return torch.nn.Parameter(torch.tensor(right))
def load_weights_into_gpt(gpt, params):
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
for b in range(len(params["blocks"])):
q_w, k_w, v_w = np.split(
(params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.weight = assign(
gpt.trf_blocks[b].att.W_query.weight, q_w.T)
gpt.trf_blocks[b].att.W_key.weight = assign(
gpt.trf_blocks[b].att.W_key.weight, k_w.T)
gpt.trf_blocks[b].att.W_value.weight = assign(
gpt.trf_blocks[b].att.W_value.weight, v_w.T)
q_b, k_b, v_b = np.split(
(params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.bias = assign(
gpt.trf_blocks[b].att.W_query.bias, q_b)
gpt.trf_blocks[b].att.W_key.bias = assign(
gpt.trf_blocks[b].att.W_key.bias, k_b)
gpt.trf_blocks[b].att.W_value.bias = assign(
gpt.trf_blocks[b].att.W_value.bias, v_b)
gpt.trf_blocks[b].att.out_proj.weight = assign(
gpt.trf_blocks[b].att.out_proj.weight,
params["blocks"][b]["attn"]["c_proj"]["w"].T)
gpt.trf_blocks[b].att.out_proj.bias = assign(
gpt.trf_blocks[b].att.out_proj.bias,
params["blocks"][b]["attn"]["c_proj"]["b"])
gpt.trf_blocks[b].ff.layers[0].weight = assign(
gpt.trf_blocks[b].ff.layers[0].weight,
params["blocks"][b]["mlp"]["c_fc"]["w"].T)
gpt.trf_blocks[b].ff.layers[0].bias = assign(
gpt.trf_blocks[b].ff.layers[0].bias,
params["blocks"][b]["mlp"]["c_fc"]["b"])
gpt.trf_blocks[b].ff.layers[2].weight = assign(
gpt.trf_blocks[b].ff.layers[2].weight,
params["blocks"][b]["mlp"]["c_proj"]["w"].T)
gpt.trf_blocks[b].ff.layers[2].bias = assign(
gpt.trf_blocks[b].ff.layers[2].bias,
params["blocks"][b]["mlp"]["c_proj"]["b"])
gpt.trf_blocks[b].norm1.scale = assign(
gpt.trf_blocks[b].norm1.scale,
params["blocks"][b]["ln_1"]["g"])
gpt.trf_blocks[b].norm1.shift = assign(
gpt.trf_blocks[b].norm1.shift,
params["blocks"][b]["ln_1"]["b"])
gpt.trf_blocks[b].norm2.scale = assign(
gpt.trf_blocks[b].norm2.scale,
params["blocks"][b]["ln_2"]["g"])
gpt.trf_blocks[b].norm2.shift = assign(
gpt.trf_blocks[b].norm2.shift,
params["blocks"][b]["ln_2"]["b"])
gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
# For-loop is the same as before: Get logits, and only focus on last time step
for _ in range(max_new_tokens):
idx_cond = idx[:, -context_size:]
with torch.no_grad():
logits = model(idx_cond)
logits = logits[:, -1, :]
# New: Filter logits with top_k sampling
if top_k is not None:
# Keep only top_k values
top_logits, _ = torch.topk(logits, top_k)
min_val = top_logits[:, -1]
logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
# New: Apply temperature scaling
if temperature > 0.0:
logits = logits / temperature
# Apply softmax to get probabilities
probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
# Sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
# Otherwise same as before: get idx of the vocab entry with the highest logits value
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
break
# Same as before: append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
return idx

4
pkg/llms_from_scratch/__init__.py Normal file
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@@ -0,0 +1,4 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch

44
pkg/llms_from_scratch/appendix_a.py Normal file
View File

@@ -0,0 +1,44 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import torch
from torch.utils.data import Dataset
class NeuralNetwork(torch.nn.Module):
def __init__(self, num_inputs, num_outputs):
super().__init__()
self.layers = torch.nn.Sequential(
# 1st hidden layer
torch.nn.Linear(num_inputs, 30),
torch.nn.ReLU(),
# 2nd hidden layer
torch.nn.Linear(30, 20),
torch.nn.ReLU(),
# output layer
torch.nn.Linear(20, num_outputs),
)
def forward(self, x):
logits = self.layers(x)
return logits
class ToyDataset(Dataset):
def __init__(self, X, y):
self.features = X
self.labels = y
def __getitem__(self, index):
one_x = self.features[index]
one_y = self.labels[index]
return one_x, one_y
def __len__(self):
return self.labels.shape[0]

94
pkg/llms_from_scratch/appendix_d.py Normal file
View File

@@ -0,0 +1,94 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from .ch05 import calc_loss_batch, evaluate_model, generate_and_print_sample
import math
import torch
def find_highest_gradient(model):
max_grad = None
for param in model.parameters():
if param.grad is not None:
grad_values = param.grad.data.flatten()
max_grad_param = grad_values.max()
if max_grad is None or max_grad_param > max_grad:
max_grad = max_grad_param
return max_grad
def train_model(model, train_loader, val_loader, optimizer, device,
n_epochs, eval_freq, eval_iter, start_context, tokenizer,
warmup_steps, initial_lr=3e-05, min_lr=1e-6, orig_book_version=False):
train_losses, val_losses, track_tokens_seen, track_lrs = [], [], [], []
tokens_seen, global_step = 0, -1
# Retrieve the maximum learning rate from the optimizer
peak_lr = optimizer.param_groups[0]["lr"]
# Calculate the total number of iterations in the training process
total_training_steps = len(train_loader) * n_epochs
# Calculate the learning rate increment during the warmup phase
lr_increment = (peak_lr - initial_lr) / warmup_steps
for epoch in range(n_epochs):
model.train()
for input_batch, target_batch in train_loader:
optimizer.zero_grad()
global_step += 1
# Adjust the learning rate based on the current phase (warmup or cosine annealing)
if global_step < warmup_steps:
# Linear warmup
lr = initial_lr + global_step * lr_increment
else:
# Cosine annealing after warmup
progress = ((global_step - warmup_steps) /
(total_training_steps - warmup_steps))
lr = min_lr + (peak_lr - min_lr) * 0.5 * (1 + math.cos(math.pi * progress))
# Apply the calculated learning rate to the optimizer
for param_group in optimizer.param_groups:
param_group["lr"] = lr
track_lrs.append(lr) # Store the current learning rate
# Calculate and backpropagate the loss
loss = calc_loss_batch(input_batch, target_batch, model, device)
loss.backward()
# Apply gradient clipping after the warmup phase to avoid exploding gradients
if orig_book_version:
if global_step > warmup_steps:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
else:
if global_step >= warmup_steps: # the book originally used global_step > warmup_steps, which lead to a skipped clipping step after warmup
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
tokens_seen += input_batch.numel()
# Periodically evaluate the model on the training and validation sets
if global_step % eval_freq == 0:
train_loss, val_loss = evaluate_model(
model, train_loader, val_loader,
device, eval_iter
)
train_losses.append(train_loss)
val_losses.append(val_loss)
track_tokens_seen.append(tokens_seen)
# Print the current losses
print(f"Ep {epoch+1} (Iter {global_step:06d}): "
f"Train loss {train_loss:.3f}, "
f"Val loss {val_loss:.3f}")
# Generate and print a sample from the model to monitor progress
generate_and_print_sample(
model, tokenizer, device, start_context
)
return train_losses, val_losses, track_tokens_seen, track_lrs

42
pkg/llms_from_scratch/appendix_e.py Normal file
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@@ -0,0 +1,42 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import torch
import math
class LoRALayer(torch.nn.Module):
def __init__(self, in_dim, out_dim, rank, alpha):
super().__init__()
self.A = torch.nn.Parameter(torch.empty(in_dim, rank))
torch.nn.init.kaiming_uniform_(self.A, a=math.sqrt(5)) # similar to standard weight initialization
self.B = torch.nn.Parameter(torch.zeros(rank, out_dim))
self.alpha = alpha
def forward(self, x):
x = self.alpha * (x @ self.A @ self.B)
return x
class LinearWithLoRA(torch.nn.Module):
def __init__(self, linear, rank, alpha):
super().__init__()
self.linear = linear
self.lora = LoRALayer(
linear.in_features, linear.out_features, rank, alpha
)
def forward(self, x):
return self.linear(x) + self.lora(x)
def replace_linear_with_lora(model, rank, alpha):
for name, module in model.named_children():
if isinstance(module, torch.nn.Linear):
# Replace the Linear layer with LinearWithLoRA
setattr(model, name, LinearWithLoRA(module, rank, alpha))
else:
# Recursively apply the same function to child modules
replace_linear_with_lora(module, rank, alpha)

46
pkg/llms_from_scratch/ch02.py Normal file
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@@ -0,0 +1,46 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import torch
from torch.utils.data import Dataset, DataLoader
import tiktoken
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.tokenizer = tokenizer
self.input_ids = []
self.target_ids = []
# Tokenize the entire text
token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
# Use a sliding window to chunk the book into overlapping sequences of max_length
for i in range(0, len(token_ids) - max_length, stride):
input_chunk = token_ids[i:i + max_length]
target_chunk = token_ids[i + 1: i + max_length + 1]
self.input_ids.append(torch.tensor(input_chunk))
self.target_ids.append(torch.tensor(target_chunk))
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True, num_workers=0):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
# Create dataset
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
# Create dataloader
dataloader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
return dataloader

151
pkg/llms_from_scratch/ch03.py Normal file
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@@ -0,0 +1,151 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import torch
import torch.nn as nn
class SelfAttention_v1(nn.Module):
def __init__(self, d_in, d_out):
super().__init__()
self.W_query = nn.Parameter(torch.rand(d_in, d_out))
self.W_key = nn.Parameter(torch.rand(d_in, d_out))
self.W_value = nn.Parameter(torch.rand(d_in, d_out))
def forward(self, x):
keys = x @ self.W_key
queries = x @ self.W_query
values = x @ self.W_value
attn_scores = queries @ keys.T # omega
attn_weights = torch.softmax(
attn_scores / keys.shape[-1]**0.5, dim=-1
)
context_vec = attn_weights @ values
return context_vec
class SelfAttention_v2(nn.Module):
def __init__(self, d_in, d_out, qkv_bias=False):
super().__init__()
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
def forward(self, x):
keys = self.W_key(x)
queries = self.W_query(x)
values = self.W_value(x)
attn_scores = queries @ keys.T
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
context_vec = attn_weights @ values
return context_vec
class CausalAttention(nn.Module):
def __init__(self, d_in, d_out, context_length,
dropout, qkv_bias=False):
super().__init__()
self.d_out = d_out
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.dropout = nn.Dropout(dropout) # New
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1)) # New
def forward(self, x):
b, num_tokens, d_in = x.shape # New batch dimension b
# For inputs where `num_tokens` exceeds `context_length`, this will result in errors
# in the mask creation further below.
# In practice, this is not a problem since the LLM (chapters 4-7) ensures that inputs
# do not exceed `context_length` before reaching this forward method.
keys = self.W_key(x)
queries = self.W_query(x)
values = self.W_value(x)
attn_scores = queries @ keys.transpose(1, 2) # Changed transpose
attn_scores.masked_fill_( # New, _ ops are in-place
self.mask.bool()[:num_tokens, :num_tokens], -torch.inf) # `:num_tokens` to account for cases where the number of tokens in the batch is smaller than the supported context_size
attn_weights = torch.softmax(
attn_scores / keys.shape[-1]**0.5, dim=-1
)
attn_weights = self.dropout(attn_weights) # New
context_vec = attn_weights @ values
return context_vec
class MultiHeadAttentionWrapper(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
self.heads = nn.ModuleList(
[CausalAttention(d_in, d_out, context_length, dropout, qkv_bias)
for _ in range(num_heads)]
)
def forward(self, x):
return torch.cat([head(x) for head in self.heads], dim=-1)
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec

74
ch04/02_performance-analysis/previous_chapters.py → pkg/llms_from_scratch/ch04.py
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@@ -2,76 +2,12 @@
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# This file collects all the relevant code that we covered thus far
# throughout Chapters 2-4.
# This file can be run as a standalone script.
from .ch03 import MultiHeadAttention
import torch
import torch.nn as nn
#####################################
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
queries = self.W_query(x)
values = self.W_value(x)
# We implicitly split the matrix by adding a `num_heads` dimension
# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
keys = keys.transpose(1, 2)
queries = queries.transpose(1, 2)
values = values.transpose(1, 2)
# Compute scaled dot-product attention (aka self-attention) with a causal mask
attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
# Original mask truncated to the number of tokens and converted to boolean
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
# Use the mask to fill attention scores
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
# Combine heads, where self.d_out = self.num_heads * self.head_dim
context_vec = context_vec.reshape(b, num_tokens, self.d_out)
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
#####################################
# Chapter 4
#####################################
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
@@ -123,21 +59,21 @@ class TransformerBlock(nn.Module):
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
self.drop_resid = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
# Shortcut connection for attention block
shortcut = x
x = self.norm1(x)
x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
x = self.drop_shortcut(x)
x = self.drop_resid(x)
x = x + shortcut # Add the original input back
# Shortcut connection for feed-forward block
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = self.drop_resid(x)
x = x + shortcut # Add the original input back
return x

233
pkg/llms_from_scratch/ch05.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from .ch04 import generate_text_simple
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import torch
def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
# For-loop is the same as before: Get logits, and only focus on last time step
for _ in range(max_new_tokens):
idx_cond = idx[:, -context_size:]
with torch.no_grad():
logits = model(idx_cond)
logits = logits[:, -1, :]
# New: Filter logits with top_k sampling
if top_k is not None:
# Keep only top_k values
top_logits, _ = torch.topk(logits, top_k)
min_val = top_logits[:, -1]
logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
# New: Apply temperature scaling
if temperature > 0.0:
logits = logits / temperature
# Apply softmax to get probabilities
probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
# Sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
# Otherwise same as before: get idx of the vocab entry with the highest logits value
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
break
# Same as before: append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
return idx
def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
eval_freq, eval_iter, start_context, tokenizer):
# Initialize lists to track losses and tokens seen
train_losses, val_losses, track_tokens_seen = [], [], []
tokens_seen, global_step = 0, -1
# Main training loop
for epoch in range(num_epochs):
model.train() # Set model to training mode
for input_batch, target_batch in train_loader:
optimizer.zero_grad() # Reset loss gradients from previous batch iteration
loss = calc_loss_batch(input_batch, target_batch, model, device)
loss.backward() # Calculate loss gradients
optimizer.step() # Update model weights using loss gradients
tokens_seen += input_batch.numel()
global_step += 1
# Optional evaluation step
if global_step % eval_freq == 0:
train_loss, val_loss = evaluate_model(
model, train_loader, val_loader, device, eval_iter)
train_losses.append(train_loss)
val_losses.append(val_loss)
track_tokens_seen.append(tokens_seen)
print(f"Ep {epoch+1} (Step {global_step:06d}): "
f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
# Print a sample text after each epoch
generate_and_print_sample(
model, tokenizer, device, start_context
)
return train_losses, val_losses, track_tokens_seen
def evaluate_model(model, train_loader, val_loader, device, eval_iter):
model.eval()
with torch.no_grad():
train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
model.train()
return train_loss, val_loss
def generate_and_print_sample(model, tokenizer, device, start_context):
model.eval()
context_size = model.pos_emb.weight.shape[0]
encoded = text_to_token_ids(start_context, tokenizer).to(device)
with torch.no_grad():
token_ids = generate_text_simple(
model=model, idx=encoded,
max_new_tokens=50, context_size=context_size
)
decoded_text = token_ids_to_text(token_ids, tokenizer)
print(decoded_text.replace("\n", " ")) # Compact print format
model.train()
def assign(left, right):
if left.shape != right.shape:
raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
return torch.nn.Parameter(torch.tensor(right))
def load_weights_into_gpt(gpt, params):
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
for b in range(len(params["blocks"])):
q_w, k_w, v_w = np.split(
(params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.weight = assign(
gpt.trf_blocks[b].att.W_query.weight, q_w.T)
gpt.trf_blocks[b].att.W_key.weight = assign(
gpt.trf_blocks[b].att.W_key.weight, k_w.T)
gpt.trf_blocks[b].att.W_value.weight = assign(
gpt.trf_blocks[b].att.W_value.weight, v_w.T)
q_b, k_b, v_b = np.split(
(params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
gpt.trf_blocks[b].att.W_query.bias = assign(
gpt.trf_blocks[b].att.W_query.bias, q_b)
gpt.trf_blocks[b].att.W_key.bias = assign(
gpt.trf_blocks[b].att.W_key.bias, k_b)
gpt.trf_blocks[b].att.W_value.bias = assign(
gpt.trf_blocks[b].att.W_value.bias, v_b)
gpt.trf_blocks[b].att.out_proj.weight = assign(
gpt.trf_blocks[b].att.out_proj.weight,
params["blocks"][b]["attn"]["c_proj"]["w"].T)
gpt.trf_blocks[b].att.out_proj.bias = assign(
gpt.trf_blocks[b].att.out_proj.bias,
params["blocks"][b]["attn"]["c_proj"]["b"])
gpt.trf_blocks[b].ff.layers[0].weight = assign(
gpt.trf_blocks[b].ff.layers[0].weight,
params["blocks"][b]["mlp"]["c_fc"]["w"].T)
gpt.trf_blocks[b].ff.layers[0].bias = assign(
gpt.trf_blocks[b].ff.layers[0].bias,
params["blocks"][b]["mlp"]["c_fc"]["b"])
gpt.trf_blocks[b].ff.layers[2].weight = assign(
gpt.trf_blocks[b].ff.layers[2].weight,
params["blocks"][b]["mlp"]["c_proj"]["w"].T)
gpt.trf_blocks[b].ff.layers[2].bias = assign(
gpt.trf_blocks[b].ff.layers[2].bias,
params["blocks"][b]["mlp"]["c_proj"]["b"])
gpt.trf_blocks[b].norm1.scale = assign(
gpt.trf_blocks[b].norm1.scale,
params["blocks"][b]["ln_1"]["g"])
gpt.trf_blocks[b].norm1.shift = assign(
gpt.trf_blocks[b].norm1.shift,
params["blocks"][b]["ln_1"]["b"])
gpt.trf_blocks[b].norm2.scale = assign(
gpt.trf_blocks[b].norm2.scale,
params["blocks"][b]["ln_2"]["g"])
gpt.trf_blocks[b].norm2.shift = assign(
gpt.trf_blocks[b].norm2.shift,
params["blocks"][b]["ln_2"]["b"])
gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
def text_to_token_ids(text, tokenizer):
encoded = tokenizer.encode(text, allowed_special={"<|endoftext|>"})
encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
return encoded_tensor
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
def calc_loss_batch(input_batch, target_batch, model, device):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)
loss = torch.nn.functional.cross_entropy(logits.flatten(0, 1), target_batch.flatten())
return loss
def calc_loss_loader(data_loader, model, device, num_batches=None):
total_loss = 0.
if len(data_loader) == 0:
return float("nan")
elif num_batches is None:
num_batches = len(data_loader)
else:
# Reduce the number of batches to match the total number of batches in the data loader
# if num_batches exceeds the number of batches in the data loader
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
loss = calc_loss_batch(input_batch, target_batch, model, device)
total_loss += loss.item()
else:
break
return total_loss / num_batches
def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
fig, ax1 = plt.subplots(figsize=(5, 3))
# Plot training and validation loss against epochs
ax1.plot(epochs_seen, train_losses, label="Training loss")
ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
ax1.set_xlabel("Epochs")
ax1.set_ylabel("Loss")
ax1.legend(loc="upper right")
ax1.xaxis.set_major_locator(MaxNLocator(integer=True)) # only show integer labels on x-axis
# Create a second x-axis for tokens seen
ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
ax2.plot(tokens_seen, train_losses, alpha=0) # Invisible plot for aligning ticks
ax2.set_xlabel("Tokens seen")
fig.tight_layout() # Adjust layout to make room
plt.savefig("loss-plot.pdf")
plt.show()

254
pkg/llms_from_scratch/ch06.py Normal file
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@@ -0,0 +1,254 @@
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import urllib.request
import zipfile
import os
from pathlib import Path
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
import torch
import pandas as pd
def download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path):
if data_file_path.exists():
print(f"{data_file_path} already exists. Skipping download and extraction.")
return
# Downloading the file
with urllib.request.urlopen(url) as response:
with open(zip_path, "wb") as out_file:
out_file.write(response.read())
# Unzipping the file
with zipfile.ZipFile(zip_path, "r") as zip_ref:
zip_ref.extractall(extracted_path)
# Add .tsv file extension
original_file_path = Path(extracted_path) / "SMSSpamCollection"
os.rename(original_file_path, data_file_path)
print(f"File downloaded and saved as {data_file_path}")
def create_balanced_dataset(df):
# Count the instances of "spam"
num_spam = df[df["Label"] == "spam"].shape[0]
# Randomly sample "ham" instances to match the number of "spam" instances
ham_subset = df[df["Label"] == "ham"].sample(num_spam, random_state=123)
# Combine ham "subset" with "spam"
balanced_df = pd.concat([ham_subset, df[df["Label"] == "spam"]])
return balanced_df
def random_split(df, train_frac, validation_frac):
# Shuffle the entire DataFrame
df = df.sample(frac=1, random_state=123).reset_index(drop=True)
# Calculate split indices
train_end = int(len(df) * train_frac)
validation_end = train_end + int(len(df) * validation_frac)
# Split the DataFrame
train_df = df[:train_end]
validation_df = df[train_end:validation_end]
test_df = df[validation_end:]
return train_df, validation_df, test_df
class SpamDataset(Dataset):
def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
self.data = pd.read_csv(csv_file)
# Pre-tokenize texts
self.encoded_texts = [
tokenizer.encode(text) for text in self.data["Text"]
]
if max_length is None:
self.max_length = self._longest_encoded_length()
else:
self.max_length = max_length
# Truncate sequences if they are longer than max_length
self.encoded_texts = [
encoded_text[:self.max_length]
for encoded_text in self.encoded_texts
]
# Pad sequences to the longest sequence
self.encoded_texts = [
encoded_text + [pad_token_id] * (self.max_length - len(encoded_text))
for encoded_text in self.encoded_texts
]
def __getitem__(self, index):
encoded = self.encoded_texts[index]
label = self.data.iloc[index]["Label"]
return (
torch.tensor(encoded, dtype=torch.long),
torch.tensor(label, dtype=torch.long)
)
def __len__(self):
return len(self.data)
def _longest_encoded_length(self):
max_length = 0
for encoded_text in self.encoded_texts:
encoded_length = len(encoded_text)
if encoded_length > max_length:
max_length = encoded_length
return max_length
# Note: A more pythonic version to implement this method
# is the following, which is also used in the next chapter:
# return max(len(encoded_text) for encoded_text in self.encoded_texts)
def calc_accuracy_loader(data_loader, model, device, num_batches=None):
model.eval()
correct_predictions, num_examples = 0, 0
if num_batches is None:
num_batches = len(data_loader)
else:
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
with torch.no_grad():
logits = model(input_batch)[:, -1, :] # Logits of last output token
predicted_labels = torch.argmax(logits, dim=-1)
num_examples += predicted_labels.shape[0]
correct_predictions += (predicted_labels == target_batch).sum().item()
else:
break
return correct_predictions / num_examples
def calc_loss_batch(input_batch, target_batch, model, device):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, -1, :] # Logits of last output token
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss
def calc_loss_loader(data_loader, model, device, num_batches=None):
total_loss = 0.
if len(data_loader) == 0:
return float("nan")
elif num_batches is None:
num_batches = len(data_loader)
else:
# Reduce the number of batches to match the total number of batches in the data loader
# if num_batches exceeds the number of batches in the data loader
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
loss = calc_loss_batch(input_batch, target_batch, model, device)
total_loss += loss.item()
else:
break
return total_loss / num_batches
def evaluate_model(model, train_loader, val_loader, device, eval_iter):
model.eval()
with torch.no_grad():
train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
model.train()
return train_loss, val_loss
def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
eval_freq, eval_iter):
# Initialize lists to track losses and examples seen
train_losses, val_losses, train_accs, val_accs = [], [], [], []
examples_seen, global_step = 0, -1
# Main training loop
for epoch in range(num_epochs):
model.train() # Set model to training mode
for input_batch, target_batch in train_loader:
optimizer.zero_grad() # Reset loss gradients from previous batch iteration
loss = calc_loss_batch(input_batch, target_batch, model, device)
loss.backward() # Calculate loss gradients
optimizer.step() # Update model weights using loss gradients
examples_seen += input_batch.shape[0] # New: track examples instead of tokens
global_step += 1
# Optional evaluation step
if global_step % eval_freq == 0:
train_loss, val_loss = evaluate_model(
model, train_loader, val_loader, device, eval_iter)
train_losses.append(train_loss)
val_losses.append(val_loss)
print(f"Ep {epoch+1} (Step {global_step:06d}): "
f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
# Calculate accuracy after each epoch
train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
train_accs.append(train_accuracy)
val_accs.append(val_accuracy)
return train_losses, val_losses, train_accs, val_accs, examples_seen
def plot_values(epochs_seen, examples_seen, train_values, val_values, label="loss"):
fig, ax1 = plt.subplots(figsize=(5, 3))
# Plot training and validation loss against epochs
ax1.plot(epochs_seen, train_values, label=f"Training {label}")
ax1.plot(epochs_seen, val_values, linestyle="-.", label=f"Validation {label}")
ax1.set_xlabel("Epochs")
ax1.set_ylabel(label.capitalize())
ax1.legend()
# Create a second x-axis for examples seen
ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
ax2.plot(examples_seen, train_values, alpha=0) # Invisible plot for aligning ticks
ax2.set_xlabel("Examples seen")
fig.tight_layout() # Adjust layout to make room
plt.savefig(f"{label}-plot.pdf")
plt.show()
def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):
model.eval()
# Prepare inputs to the model
input_ids = tokenizer.encode(text)
supported_context_length = model.pos_emb.weight.shape[0]
# Note: In the book, this was originally written as pos_emb.weight.shape[1] by mistake
# It didn't break the code but would have caused unnecessary truncation (to 768 instead of 1024)
# Truncate sequences if they too long
input_ids = input_ids[:min(max_length, supported_context_length)]
# Pad sequences to the longest sequence
input_ids += [pad_token_id] * (max_length - len(input_ids))
input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension
# Model inference
with torch.no_grad():
logits = model(input_tensor)[:, -1, :] # Logits of the last output token
predicted_label = torch.argmax(logits, dim=-1).item()
# Return the classified result
return "spam" if predicted_label == 1 else "not spam"

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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import json
import os
import psutil
import urllib
import torch
import tqdm
from torch.utils.data import Dataset
def download_and_load_file(file_path, url):
if not os.path.exists(file_path):
with urllib.request.urlopen(url) as response:
text_data = response.read().decode("utf-8")
with open(file_path, "w", encoding="utf-8") as file:
file.write(text_data)
# The book originally contained this unnecessary "else" clause:
# else:
# with open(file_path, "r", encoding="utf-8") as file:
# text_data = file.read()
with open(file_path, "r", encoding="utf-8") as file:
data = json.load(file)
return data
def format_input(entry):
instruction_text = (
f"Below is an instruction that describes a task. "
f"Write a response that appropriately completes the request."
f"\n\n### Instruction:\n{entry['instruction']}"
)
input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
return instruction_text + input_text
class InstructionDataset(Dataset):
def __init__(self, data, tokenizer):
self.data = data
# Pre-tokenize texts
self.encoded_texts = []
for entry in data:
instruction_plus_input = format_input(entry)
response_text = f"\n\n### Response:\n{entry['output']}"
full_text = instruction_plus_input + response_text
self.encoded_texts.append(
tokenizer.encode(full_text)
)
def __getitem__(self, index):
return self.encoded_texts[index]
def __len__(self):
return len(self.data)
def custom_collate_draft_1(
batch,
pad_token_id=50256,
device="cpu"
):
# Find the longest sequence in the batch
# and increase the max length by +1, which will add one extra
# padding token below
batch_max_length = max(len(item)+1 for item in batch)
# Pad and prepare inputs
inputs_lst = []
for item in batch:
new_item = item.copy()
# Add an <|endoftext|> token
new_item += [pad_token_id]
# Pad sequences to batch_max_length
padded = (
new_item + [pad_token_id] *
(batch_max_length - len(new_item))
)
# Via padded[:-1], we remove the extra padded token
# that has been added via the +1 setting in batch_max_length
# (the extra padding token will be relevant in later codes)
inputs = torch.tensor(padded[:-1])
inputs_lst.append(inputs)
# Convert list of inputs to tensor and transfer to target device
inputs_tensor = torch.stack(inputs_lst).to(device)
return inputs_tensor
def custom_collate_draft_2(
batch,
pad_token_id=50256,
device="cpu"
):
# Find the longest sequence in the batch
batch_max_length = max(len(item)+1 for item in batch)
# Pad and prepare inputs
inputs_lst, targets_lst = [], []
for item in batch:
new_item = item.copy()
# Add an <|endoftext|> token
new_item += [pad_token_id]
# Pad sequences to max_length
padded = (
new_item + [pad_token_id] *
(batch_max_length - len(new_item))
)
inputs = torch.tensor(padded[:-1]) # Truncate the last token for inputs
targets = torch.tensor(padded[1:]) # Shift +1 to the right for targets
inputs_lst.append(inputs)
targets_lst.append(targets)
# Convert list of inputs to tensor and transfer to target device
inputs_tensor = torch.stack(inputs_lst).to(device)
targets_tensor = torch.stack(targets_lst).to(device)
return inputs_tensor, targets_tensor
def custom_collate_fn(
batch,
pad_token_id=50256,
ignore_index=-100,
allowed_max_length=None,
device="cpu"
):
# Find the longest sequence in the batch
batch_max_length = max(len(item)+1 for item in batch)
# Pad and prepare inputs and targets
inputs_lst, targets_lst = [], []
for item in batch:
new_item = item.copy()
# Add an <|endoftext|> token
new_item += [pad_token_id]
# Pad sequences to max_length
padded = (
new_item + [pad_token_id] *
(batch_max_length - len(new_item))
)
inputs = torch.tensor(padded[:-1]) # Truncate the last token for inputs
targets = torch.tensor(padded[1:]) # Shift +1 to the right for targets
# New: Replace all but the first padding tokens in targets by ignore_index
mask = targets == pad_token_id
indices = torch.nonzero(mask).squeeze()
if indices.numel() > 1:
targets[indices[1:]] = ignore_index
# New: Optionally truncate to maximum sequence length
if allowed_max_length is not None:
inputs = inputs[:allowed_max_length]
targets = targets[:allowed_max_length]
inputs_lst.append(inputs)
targets_lst.append(targets)
# Convert list of inputs and targets to tensors and transfer to target device
inputs_tensor = torch.stack(inputs_lst).to(device)
targets_tensor = torch.stack(targets_lst).to(device)
return inputs_tensor, targets_tensor
def check_if_running(process_name):
running = False
for proc in psutil.process_iter(["name"]):
if process_name in proc.info["name"]:
running = True
break
return running
def query_model(
prompt,
model="llama3",
url="http://localhost:11434/api/chat"
):
# Create the data payload as a dictionary
data = {
"model": model,
"messages": [
{"role": "user", "content": prompt}
],
"options": { # Settings below are required for deterministic responses
"seed": 123,
"temperature": 0,
"num_ctx": 2048
}
}
# Convert the dictionary to a JSON formatted string and encode it to bytes
payload = json.dumps(data).encode("utf-8")
# Create a request object, setting the method to POST and adding necessary headers
request = urllib.request.Request(
url,
data=payload,
method="POST"
)
request.add_header("Content-Type", "application/json")
# Send the request and capture the response
response_data = ""
with urllib.request.urlopen(request) as response:
# Read and decode the response
while True:
line = response.readline().decode("utf-8")
if not line:
break
response_json = json.loads(line)
response_data += response_json["message"]["content"]
return response_data
def generate_model_scores(json_data, json_key, model="llama3"):
scores = []
for entry in tqdm(json_data, desc="Scoring entries"):
prompt = (
f"Given the input `{format_input(entry)}` "
f"and correct output `{entry['output']}`, "
f"score the model response `{entry[json_key]}`"
f" on a scale from 0 to 100, where 100 is the best score. "
f"Respond with the integer number only."
)
score = query_model(prompt, model)
try:
scores.append(int(score))
except ValueError:
print(f"Could not convert score: {score}")
continue
return scores

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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.appendix_a import NeuralNetwork, ToyDataset
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
def test_dataset():
X_train = torch.tensor([
[-1.2, 3.1],
[-0.9, 2.9],
[-0.5, 2.6],
[2.3, -1.1],
[2.7, -1.5]
])
y_train = torch.tensor([0, 0, 0, 1, 1])
train_ds = ToyDataset(X_train, y_train)
len(train_ds) == 5
torch.manual_seed(123)
train_loader = DataLoader(
dataset=train_ds,
batch_size=2,
shuffle=True,
num_workers=0
)
torch.manual_seed(123)
model = NeuralNetwork(num_inputs=2, num_outputs=2)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
num_epochs = 3
for epoch in range(num_epochs):
model.train()
for batch_idx, (features, labels) in enumerate(train_loader):
logits = model(features)
loss = F.cross_entropy(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f"Epoch: {epoch+1:03d}/{num_epochs:03d}"
f" | Batch {batch_idx:03d}/{len(train_loader):03d}"
f" | Train/Val Loss: {loss:.2f}")
model.eval()
with torch.no_grad():
outputs = model(X_train)
expected = torch.tensor([
[2.8569, -4.1618],
[2.5382, -3.7548],
[2.0944, -3.1820],
[-1.4814, 1.4816],
[-1.7176, 1.7342]
])
torch.equal(outputs, expected)

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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch02 import create_dataloader_v1
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.appendix_d import train_model
import os
import urllib
import tiktoken
import torch
from torch.utils.data import Subset, DataLoader
def test_train(tmp_path):
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 256, # Shortened context length (orig: 1024)
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-key-value bias
}
OTHER_SETTINGS = {
"learning_rate": 5e-4,
"num_epochs": 2,
"batch_size": 1,
"weight_decay": 0.1
}
torch.manual_seed(123)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
##############################
# Download data if necessary
##############################
file_path = tmp_path / "the-verdict.txt"
url = "https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/ch02/01_main-chapter-code/the-verdict.txt"
if not os.path.exists(file_path):
with urllib.request.urlopen(url) as response:
text_data = response.read().decode("utf-8")
with open(file_path, "w", encoding="utf-8") as file:
file.write(text_data)
else:
with open(file_path, "r", encoding="utf-8") as file:
text_data = file.read()
##############################
# Initialize model
##############################
model = GPTModel(GPT_CONFIG_124M)
model.to(device) # no assignment model = model.to(device) necessary for nn.Module classes
##############################
# Set up dataloaders
##############################
# Train/validation ratio
train_ratio = 0.90
split_idx = int(train_ratio * len(text_data))
train_loader = create_dataloader_v1(
text_data[:split_idx],
batch_size=OTHER_SETTINGS["batch_size"],
max_length=GPT_CONFIG_124M["context_length"],
stride=GPT_CONFIG_124M["context_length"],
drop_last=True,
shuffle=True,
num_workers=0
)
val_loader = create_dataloader_v1(
text_data[split_idx:],
batch_size=OTHER_SETTINGS["batch_size"],
max_length=GPT_CONFIG_124M["context_length"],
stride=GPT_CONFIG_124M["context_length"],
drop_last=False,
shuffle=False,
num_workers=0
)
##############################
# Train model
##############################
tokenizer = tiktoken.get_encoding("gpt2")
train_subset = Subset(train_loader.dataset, range(1))
one_batch_train_loader = DataLoader(train_subset, batch_size=1)
val_subset = Subset(val_loader.dataset, range(1))
one_batch_val_loader = DataLoader(val_subset, batch_size=1)
peak_lr = 0.001 # this was originally set to 5e-4 in the book by mistake
optimizer = torch.optim.AdamW(model.parameters(), lr=peak_lr, weight_decay=0.1) # the book accidentally omitted the lr assignment
tokenizer = tiktoken.get_encoding("gpt2")
n_epochs = 6
warmup_steps = 1
train_losses, val_losses, tokens_seen, lrs = train_model(
model, one_batch_train_loader, one_batch_val_loader, optimizer, device, n_epochs=n_epochs,
eval_freq=5, eval_iter=1, start_context="Every effort moves you",
tokenizer=tokenizer, warmup_steps=warmup_steps,
initial_lr=1e-5, min_lr=1e-5
)
assert round(train_losses[0], 1) == 10.9
assert round(val_losses[0], 1) == 11.0
assert train_losses[-1] < train_losses[0]

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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch06 import (
download_and_unzip_spam_data, create_balanced_dataset,
random_split, SpamDataset, train_classifier_simple
)
from llms_from_scratch.appendix_e import replace_linear_with_lora
from pathlib import Path
import urllib
import pandas as pd
import tiktoken
import torch
from torch.utils.data import DataLoader, Subset
def test_train_classifier_lora(tmp_path):
########################################
# Download and prepare dataset
########################################
url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip"
zip_path = tmp_path / "sms_spam_collection.zip"
extracted_path = tmp_path / "sms_spam_collection"
data_file_path = Path(extracted_path) / "SMSSpamCollection.tsv"
try:
download_and_unzip_spam_data(
url, zip_path, extracted_path, data_file_path
)
except (urllib.error.HTTPError, urllib.error.URLError, TimeoutError) as e:
print(f"Primary URL failed: {e}. Trying backup URL...")
backup_url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/sms%2Bspam%2Bcollection.zip"
download_and_unzip_spam_data(
backup_url, zip_path, extracted_path, data_file_path
)
df = pd.read_csv(data_file_path, sep="\t", header=None, names=["Label", "Text"])
balanced_df = create_balanced_dataset(df)
balanced_df["Label"] = balanced_df["Label"].map({"ham": 0, "spam": 1})
train_df, validation_df, test_df = random_split(balanced_df, 0.7, 0.1)
train_df.to_csv(tmp_path / "train.csv", index=None)
validation_df.to_csv(tmp_path / "validation.csv", index=None)
test_df.to_csv(tmp_path / "test.csv", index=None)
########################################
# Create data loaders
########################################
tokenizer = tiktoken.get_encoding("gpt2")
train_dataset = SpamDataset(
csv_file=tmp_path / "train.csv",
max_length=None,
tokenizer=tokenizer
)
val_dataset = SpamDataset(
csv_file=tmp_path / "validation.csv",
max_length=train_dataset.max_length,
tokenizer=tokenizer
)
num_workers = 0
batch_size = 8
torch.manual_seed(123)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True,
)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=False,
)
########################################
# Load pretrained model
########################################
# Small GPT model for testing purposes
BASE_CONFIG = {
"vocab_size": 50257,
"context_length": 120,
"drop_rate": 0.0,
"qkv_bias": False,
"emb_dim": 12,
"n_layers": 1,
"n_heads": 2
}
model = GPTModel(BASE_CONFIG)
model.eval()
device = "cpu"
########################################
# Modify and pretrained model
########################################
for param in model.parameters():
param.requires_grad = False
torch.manual_seed(123)
num_classes = 2
model.out_head = torch.nn.Linear(in_features=BASE_CONFIG["emb_dim"], out_features=num_classes)
replace_linear_with_lora(model, rank=16, alpha=16)
model.to(device)
for param in model.trf_blocks[-1].parameters():
param.requires_grad = True
for param in model.final_norm.parameters():
param.requires_grad = True
########################################
# Finetune modified model
########################################
torch.manual_seed(123)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)
train_subset = Subset(train_loader.dataset, range(5))
batch_train_loader = DataLoader(train_subset, batch_size=5)
val_subset = Subset(val_loader.dataset, range(5))
batch_val_loader = DataLoader(val_subset, batch_size=5)
num_epochs = 6
train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
model, batch_train_loader, batch_val_loader, optimizer, device,
num_epochs=num_epochs, eval_freq=1, eval_iter=1,
)
assert round(train_losses[0], 1) == 0.8
assert round(val_losses[0], 1) == 0.8
assert train_losses[-1] < train_losses[0]

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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch02 import create_dataloader_v1
import os
import urllib.request
import pytest
import torch
@pytest.mark.parametrize("file_name", ["the-verdict.txt"])
def test_dataloader(tmp_path, file_name):
if not os.path.exists("the-verdict.txt"):
url = ("https://raw.githubusercontent.com/rasbt/"
"LLMs-from-scratch/main/ch02/01_main-chapter-code/"
"the-verdict.txt")
file_path = "the-verdict.txt"
urllib.request.urlretrieve(url, file_path)
with open("the-verdict.txt", "r", encoding="utf-8") as f:
raw_text = f.read()
vocab_size = 50257
output_dim = 256
context_length = 1024
token_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)
pos_embedding_layer = torch.nn.Embedding(context_length, output_dim)
batch_size = 8
max_length = 4
dataloader = create_dataloader_v1(
raw_text,
batch_size=batch_size,
max_length=max_length,
stride=max_length
)
for batch in dataloader:
x, y = batch
token_embeddings = token_embedding_layer(x)
pos_embeddings = pos_embedding_layer(torch.arange(max_length))
input_embeddings = token_embeddings + pos_embeddings
break
input_embeddings.shape == torch.Size([8, 4, 256])

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pkg/llms_from_scratch/tests/test_ch03.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch03 import MultiHeadAttention
import torch
def test_mha():
context_length = 100
d_in = 256
d_out = 16
mha = MultiHeadAttention(d_in, d_out, context_length, 0.0, num_heads=2)
batch = torch.rand(8, 6, d_in)
context_vecs = mha(batch)
context_vecs.shape == torch.Size([8, 6, d_out])

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pkg/llms_from_scratch/tests/test_ch04.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch04 import generate_text_simple
import torch
import tiktoken
def test_GPTModel():
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-Key-Value bias
}
torch.manual_seed(123)
model = GPTModel(GPT_CONFIG_124M)
model.eval() # disable dropout
start_context = "Hello, I am"
tokenizer = tiktoken.get_encoding("gpt2")
encoded = tokenizer.encode(start_context)
encoded_tensor = torch.tensor(encoded).unsqueeze(0)
print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
print("\nInput text:", start_context)
print("Encoded input text:", encoded)
print("encoded_tensor.shape:", encoded_tensor.shape)
out = generate_text_simple(
model=model,
idx=encoded_tensor,
max_new_tokens=10,
context_size=GPT_CONFIG_124M["context_length"]
)
expect = torch.tensor([
[15496, 11, 314, 716, 27018, 24086, 47843, 30961, 42348, 7267,
49706, 43231, 47062, 34657]
])
torch.equal(expect, out)

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pkg/llms_from_scratch/tests/test_ch05.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch02 import create_dataloader_v1
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch05 import train_model_simple
import os
import urllib
import pytest
import tiktoken
import torch
from torch.utils.data import Subset, DataLoader
@pytest.mark.parametrize("file_name", ["the-verdict.txt"])
def test_train_simple(tmp_path, file_name):
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 256, # Shortened context length (orig: 1024)
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-key-value bias
}
OTHER_SETTINGS = {
"learning_rate": 5e-4,
"num_epochs": 2,
"batch_size": 1,
"weight_decay": 0.1
}
torch.manual_seed(123)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
##############################
# Download data if necessary
##############################
file_path = tmp_path / "the-verdict.txt"
url = "https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/ch02/01_main-chapter-code/the-verdict.txt"
if not os.path.exists(file_path):
with urllib.request.urlopen(url) as response:
text_data = response.read().decode('utf-8')
with open(file_path, "w", encoding="utf-8") as file:
file.write(text_data)
else:
with open(file_path, "r", encoding="utf-8") as file:
text_data = file.read()
##############################
# Initialize model
##############################
model = GPTModel(GPT_CONFIG_124M)
model.to(device) # no assignment model = model.to(device) necessary for nn.Module classes
optimizer = torch.optim.AdamW(
model.parameters(), lr=OTHER_SETTINGS["learning_rate"], weight_decay=OTHER_SETTINGS["weight_decay"]
)
##############################
# Set up dataloaders
##############################
# Train/validation ratio
train_ratio = 0.90
split_idx = int(train_ratio * len(text_data))
train_loader = create_dataloader_v1(
text_data[:split_idx],
batch_size=OTHER_SETTINGS["batch_size"],
max_length=GPT_CONFIG_124M["context_length"],
stride=GPT_CONFIG_124M["context_length"],
drop_last=True,
shuffle=True,
num_workers=0
)
val_loader = create_dataloader_v1(
text_data[split_idx:],
batch_size=OTHER_SETTINGS["batch_size"],
max_length=GPT_CONFIG_124M["context_length"],
stride=GPT_CONFIG_124M["context_length"],
drop_last=False,
shuffle=False,
num_workers=0
)
##############################
# Train model
##############################
tokenizer = tiktoken.get_encoding("gpt2")
train_subset = Subset(train_loader.dataset, range(1))
one_batch_train_loader = DataLoader(train_subset, batch_size=1)
val_subset = Subset(val_loader.dataset, range(1))
one_batch_val_loader = DataLoader(val_subset, batch_size=1)
train_losses, val_losses, tokens_seen = train_model_simple(
model, one_batch_train_loader, one_batch_val_loader, optimizer, device,
num_epochs=OTHER_SETTINGS["num_epochs"], eval_freq=1, eval_iter=1,
start_context="Every effort moves you", tokenizer=tokenizer
)
assert round(train_losses[0], 1) == 7.6
assert round(val_losses[0], 1) == 10.1
assert train_losses[-1] < train_losses[0]

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pkg/llms_from_scratch/tests/test_ch06.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch06 import (
download_and_unzip_spam_data, create_balanced_dataset,
random_split, SpamDataset, train_classifier_simple
)
from pathlib import Path
import urllib
import pandas as pd
import tiktoken
import torch
from torch.utils.data import DataLoader, Subset
def test_train_classifier(tmp_path):
########################################
# Download and prepare dataset
########################################
url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip"
zip_path = tmp_path / "sms_spam_collection.zip"
extracted_path = tmp_path / "sms_spam_collection"
data_file_path = Path(extracted_path) / "SMSSpamCollection.tsv"
try:
download_and_unzip_spam_data(
url, zip_path, extracted_path, data_file_path
)
except (urllib.error.HTTPError, urllib.error.URLError, TimeoutError) as e:
print(f"Primary URL failed: {e}. Trying backup URL...")
backup_url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/sms%2Bspam%2Bcollection.zip"
download_and_unzip_spam_data(
backup_url, zip_path, extracted_path, data_file_path
)
df = pd.read_csv(data_file_path, sep="\t", header=None, names=["Label", "Text"])
balanced_df = create_balanced_dataset(df)
balanced_df["Label"] = balanced_df["Label"].map({"ham": 0, "spam": 1})
train_df, validation_df, test_df = random_split(balanced_df, 0.7, 0.1)
train_df.to_csv(tmp_path / "train.csv", index=None)
validation_df.to_csv(tmp_path / "validation.csv", index=None)
test_df.to_csv(tmp_path / "test.csv", index=None)
########################################
# Create data loaders
########################################
tokenizer = tiktoken.get_encoding("gpt2")
train_dataset = SpamDataset(
csv_file=tmp_path / "train.csv",
max_length=None,
tokenizer=tokenizer
)
val_dataset = SpamDataset(
csv_file=tmp_path / "validation.csv",
max_length=train_dataset.max_length,
tokenizer=tokenizer
)
num_workers = 0
batch_size = 8
torch.manual_seed(123)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True,
)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=False,
)
########################################
# Load pretrained model
########################################
# Small GPT model for testing purposes
BASE_CONFIG = {
"vocab_size": 50257,
"context_length": 120,
"drop_rate": 0.0,
"qkv_bias": False,
"emb_dim": 12,
"n_layers": 1,
"n_heads": 2
}
model = GPTModel(BASE_CONFIG)
model.eval()
device = "cpu"
########################################
# Modify and pretrained model
########################################
for param in model.parameters():
param.requires_grad = False
torch.manual_seed(123)
num_classes = 2
model.out_head = torch.nn.Linear(in_features=BASE_CONFIG["emb_dim"], out_features=num_classes)
model.to(device)
for param in model.trf_blocks[-1].parameters():
param.requires_grad = True
for param in model.final_norm.parameters():
param.requires_grad = True
########################################
# Finetune modified model
########################################
torch.manual_seed(123)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.0)
train_subset = Subset(train_loader.dataset, range(5))
batch_train_loader = DataLoader(train_subset, batch_size=5)
val_subset = Subset(val_loader.dataset, range(5))
batch_val_loader = DataLoader(val_subset, batch_size=5)
num_epochs = 5
train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
model, batch_train_loader, batch_val_loader, optimizer, device,
num_epochs=num_epochs, eval_freq=1, eval_iter=1,
)
assert round(train_losses[0], 1) == 0.8
assert round(val_losses[0], 1) == 0.8
assert train_losses[-1] < train_losses[0]

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pkg/llms_from_scratch/tests/test_ch07.py Normal file
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
from llms_from_scratch.ch04 import GPTModel
from llms_from_scratch.ch05 import train_model_simple
from llms_from_scratch.ch07 import (
download_and_load_file, InstructionDataset, format_input, custom_collate_fn
)
from functools import partial
import torch
from torch.utils.data import DataLoader
import tiktoken
def test_instruction_finetune(tmp_path):
#######################################
# Download and prepare dataset
#######################################
file_path = tmp_path / "instruction-data.json"
url = "https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/ch07/01_main-chapter-code/instruction-data.json"
data = download_and_load_file(file_path, url)
train_portion = int(len(data) * 0.85) # 85% for training
test_portion = int(len(data) * 0.1) # 10% for testing
train_data = data[:train_portion]
test_data = data[train_portion:train_portion + test_portion]
val_data = data[train_portion + test_portion:]
# Use very small subset for testing purposes
train_data = train_data[:15]
val_data = val_data[:15]
test_data = test_data[:15]
tokenizer = tiktoken.get_encoding("gpt2")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
customized_collate_fn = partial(custom_collate_fn, device=device, allowed_max_length=100)
num_workers = 0
batch_size = 8
torch.manual_seed(123)
train_dataset = InstructionDataset(train_data, tokenizer)
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
collate_fn=customized_collate_fn,
shuffle=True,
drop_last=True,
num_workers=num_workers
)
val_dataset = InstructionDataset(val_data, tokenizer)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
collate_fn=customized_collate_fn,
shuffle=False,
drop_last=False,
num_workers=num_workers
)
#######################################
# Load pretrained model
#######################################
# Small GPT model for testing purposes
BASE_CONFIG = {
"vocab_size": 50257,
"context_length": 120,
"drop_rate": 0.0,
"qkv_bias": False,
"emb_dim": 12,
"n_layers": 1,
"n_heads": 2
}
model = GPTModel(BASE_CONFIG)
model.eval()
device = "cpu"
CHOOSE_MODEL = "Small test model"
print("Loaded model:", CHOOSE_MODEL)
print(50*"-")
#######################################
# Finetuning the model
#######################################
num_epochs = 10
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)
torch.manual_seed(123)
train_losses, val_losses, tokens_seen = train_model_simple(
model, train_loader, val_loader, optimizer, device,
num_epochs=num_epochs, eval_freq=5, eval_iter=5,
start_context=format_input(val_data[0]), tokenizer=tokenizer
)
assert round(train_losses[0], 1) == 10.9
assert round(val_losses[0], 1) == 10.9
assert train_losses[-1] < train_losses[0]

21
pyproject.toml
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@@ -1,6 +1,10 @@
[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "llms-from-scratch"
version = "0.1.0"
version = "1.0.0"
description = "Implement a ChatGPT-like LLM in PyTorch from scratch, step by step"
readme = "README.md"
requires-python = ">=3.10"
@@ -14,17 +18,17 @@ dependencies = [
"numpy>=1.26,<2.1",
"pandas>=2.2.1",
"pip>=25.0.1",
"pytest>=8.3.5",
]
[tool.setuptools.packages]
find = {}
[tool.uv.sources]
llms-from-scratch = { workspace = true }
[dependency-groups]
dev = [
"build>=1.2.2.post1",
"llms-from-scratch",
"twine>=6.1.0",
]
[tool.ruff]
@@ -37,3 +41,12 @@ ignore = [
"C406", "E226", "E402", "E702", "E703",
"E722", "E731", "E741"
]
# `llms_from_scratch` PyPI package
[tool.setuptools]
package-dir = {"" = "pkg"}
[tool.setuptools.packages.find]
where = ["pkg"]