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Update pep8 (#78)

Browse Source 
* simplify requirements file

* style

* apply linter
This commit is contained in:
Sebastian Raschka 2024-03-18 08:16:17 -05:00 committed by GitHub
parent e316cafd9f
commit 9d6da22ebb
13 changed files with 82 additions and 120 deletions
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5
appendix-A/02_installing-python-libraries/python_environment_check.py
View File

@ -25,9 +25,10 @@ def get_packages(pkgs):
except AttributeError:
try:
versions.append(imported.version_info)
except:
except AttributeError:
try:
import importlib, importlib_metadata
import importlib
import importlib_metadata
imported = importlib.import_module(p)
version = importlib_metadata.version(p)
versions.append(version)

16
appendix-A/03_main-chapter-code/DDP-script.py
View File

@ -91,11 +91,11 @@ def prepare_dataset():
train_loader = DataLoader(
dataset=train_ds,
batch_size=2,
shuffle=False, # NEW: False because of DistributedSampler below
shuffle=False, # NEW: False because of DistributedSampler below
pin_memory=True,
drop_last=True,
# NEW: chunk batches across GPUs without overlapping samples:
sampler=DistributedSampler(train_ds) # NEW
sampler=DistributedSampler(train_ds) # NEW
)
test_loader = DataLoader(
dataset=test_ds,
@ -108,14 +108,14 @@ def prepare_dataset():
# NEW: wrapper
def main(rank, world_size, num_epochs):
ddp_setup(rank, world_size) # NEW: initialize process groups
ddp_setup(rank, world_size) # NEW: initialize process groups
train_loader, test_loader = prepare_dataset()
model = NeuralNetwork(num_inputs=2, num_outputs=2)
model.to(rank)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
model = DDP(model, device_ids=[rank]) # NEW: wrap model with DDP
model = DDP(model, device_ids=[rank]) # NEW: wrap model with DDP
# the core model is now accessible as model.module
for epoch in range(num_epochs):
@ -123,15 +123,15 @@ def main(rank, world_size, num_epochs):
model.train()
for features, labels in train_loader:
features, labels = features.to(rank), labels.to(rank) # New: use rank
features, labels = features.to(rank), labels.to(rank) # New: use rank
logits = model(features)
loss = F.cross_entropy(logits, labels) # Loss function
loss = F.cross_entropy(logits, labels) # Loss function
optimizer.zero_grad()
loss.backward()
optimizer.step()
### LOGGING
# LOGGING
print(f"[GPU{rank}] Epoch: {epoch+1:03d}/{num_epochs:03d}"
f" | Batchsize {labels.shape[0]:03d}"
f" | Train/Val Loss: {loss:.2f}")
@ -142,7 +142,7 @@ def main(rank, world_size, num_epochs):
test_acc = compute_accuracy(model, test_loader, device=rank)
print(f"[GPU{rank}] Test accuracy", test_acc)
destroy_process_group() # NEW: cleanly exit distributed mode
destroy_process_group() # NEW: cleanly exit distributed mode
def compute_accuracy(model, dataloader, device):

14
appendix-D/01_main-chapter-code/previous_chapters.py
View File

@ -36,7 +36,7 @@ class GPTDatasetV1(Dataset):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
@ -80,7 +80,7 @@ class MultiHeadAttention(nn.Module):
# 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)
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)
@ -102,7 +102,7 @@ class MultiHeadAttention(nn.Module):
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
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)
@ -135,7 +135,7 @@ class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
@ -161,7 +161,7 @@ class TransformerBlock(nn.Module):
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
block_size=cfg["ctx_len"],
num_heads=cfg["n_heads"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
@ -227,7 +227,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
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)
@ -315,4 +315,4 @@ def text_to_token_ids(text, tokenizer):
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())
return tokenizer.decode(flat.tolist())

74
ch02/02_bonus_bytepair-encoder/bpe_openai_gpt2.py
View File

@ -1,39 +1,3 @@
"""
Byte pair encoding utilities
Code from https://github.com/openai/gpt-2/blob/master/src/encoder.py
And modified code (download_vocab) from
https://github.com/openai/gpt-2/blob/master/download_model.py
Modified MIT License
Software Copyright (c) 2019 OpenAI
We don’t claim ownership of the content you create with GPT-2, so it is yours to do with as you please.
We only ask that you use GPT-2 responsibly and clearly indicate your content was created using GPT-2.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.
The above copyright notice and this permission notice need not be included
with content created by the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
OR OTHER DEALINGS IN THE SOFTWARE.
"""
import os
import json
import regex as re
@ -41,6 +5,7 @@ import requests
from tqdm import tqdm
from functools import lru_cache
@lru_cache()
def bytes_to_unicode():
"""
@ -52,20 +17,21 @@ def bytes_to_unicode():
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
@ -75,17 +41,18 @@ def get_pairs(word):
prev_char = char
return pairs
class Encoder:
def __init__(self, encoder, bpe_merges, errors='replace'):
self.encoder = encoder
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
def bpe(self, token):
@ -98,7 +65,7 @@ class Encoder:
return token
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
@ -109,12 +76,12 @@ class Encoder:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
except ValueError:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
@ -141,16 +108,14 @@ class Encoder:
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def get_encoder(model_name, models_dir):
with open(os.path.join(models_dir, model_name, 'encoder.json'), 'r') as f:
encoder = json.load(f)
with open(os.path.join(models_dir, model_name, 'vocab.bpe'), 'r', encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
return Encoder(encoder=encoder, bpe_merges=bpe_merges)
def download_vocab():
@ -158,11 +123,10 @@ def download_vocab():
subdir = 'gpt2_model'
if not os.path.exists(subdir):
os.makedirs(subdir)
subdir = subdir.replace('\\','/') # needed for Windows
subdir = subdir.replace('\\', '/') # needed for Windows
for filename in ['encoder.json', 'vocab.bpe']:
r = requests.get("https://openaipublic.blob.core.windows.net/gpt-2/models/117M" + "/" + filename, stream=True)
r = requests.get("https://openaipublic.blob.core.windows.net/gpt-2/models/117M/" + filename, stream=True)
with open(os.path.join(subdir, filename), 'wb') as f:
file_size = int(r.headers["content-length"])

31
ch03/02_bonus_efficient-multihead-attention/ch03.py
View File

@ -8,33 +8,33 @@ class CausalAttention(nn.Module):
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_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(block_size, block_size), diagonal=1)) # New
self.dropout = nn.Dropout(dropout) # New
self.register_buffer('mask', torch.triu(torch.ones(block_size, block_size), diagonal=1)) # New
def forward(self, x):
b, num_tokens, d_in = x.shape # New batch dimension b
b, num_tokens, d_in = x.shape # New batch dimension b
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 = 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)
self.mask.bool()[:num_tokens, :num_tokens], -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights) # New
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, block_size, dropout, num_heads, qkv_bias=False):
super().__init__()
self.heads = nn.ModuleList(
[CausalAttention(d_in, d_out, block_size, dropout, qkv_bias)
[CausalAttention(d_in, d_out, block_size, dropout, qkv_bias)
for _ in range(num_heads)]
)
self.out_proj = nn.Linear(d_out*num_heads, d_out*num_heads)
@ -44,7 +44,6 @@ class MultiHeadAttentionWrapper(nn.Module):
return self.out_proj(context_vec)
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
super().__init__()
@ -52,7 +51,7 @@ class MultiHeadAttention(nn.Module):
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.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)
@ -70,7 +69,7 @@ class MultiHeadAttention(nn.Module):
# 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)
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)
@ -92,10 +91,10 @@ class MultiHeadAttention(nn.Module):
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
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
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
return context_vec

12
ch04/01_main-chapter-code/gpt.py
View File

@ -35,7 +35,7 @@ class GPTDatasetV1(Dataset):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
@ -78,7 +78,7 @@ class MultiHeadAttention(nn.Module):
# 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)
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)
@ -100,7 +100,7 @@ class MultiHeadAttention(nn.Module):
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
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)
@ -132,7 +132,7 @@ class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
@ -158,7 +158,7 @@ class TransformerBlock(nn.Module):
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
block_size=cfg["ctx_len"],
num_heads=cfg["n_heads"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
@ -224,7 +224,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
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)

18
ch04/01_main-chapter-code/previous_chapters.py
View File

@ -27,7 +27,7 @@ class GPTDatasetV1(Dataset):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
@ -49,7 +49,7 @@ class MultiHeadAttention(nn.Module):
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.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)
@ -61,13 +61,13 @@ class MultiHeadAttention(nn.Module):
def forward(self, x):
b, num_tokens, d_in = x.shape
keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
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)
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)
@ -84,15 +84,15 @@ class MultiHeadAttention(nn.Module):
# 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)
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
context_vec = self.out_proj(context_vec) # optional projection
return context_vec
return context_vec

8
ch05/01_main-chapter-code/previous_chapters.py
View File

@ -100,7 +100,7 @@ class MultiHeadAttention(nn.Module):
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
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)
@ -132,7 +132,7 @@ class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
@ -158,7 +158,7 @@ class TransformerBlock(nn.Module):
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
block_size=cfg["ctx_len"],
num_heads=cfg["n_heads"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
@ -224,7 +224,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
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)

4
ch05/02_bonus_hparam_tuning/hparam_search.py
View File

@ -159,7 +159,7 @@ if __name__ == "__main__":
stride=GPT_CONFIG_124M["ctx_len"],
drop_last=False,
shuffle=False
)
)
model = GPTModel(GPT_CONFIG_124M)
model.to(device)
@ -199,4 +199,4 @@ if __name__ == "__main__":
if not interrupted:
print("Hyperparameter search completed.")
print(f"Best hyperparameters: {best_hparams}")
print(f"Best Val loss: {best_val_loss} | Training loss {train_loss}")
print(f"Best Val loss: {best_val_loss} | Training loss {train_loss}")

12
ch05/02_bonus_hparam_tuning/previous_chapters.py
View File

@ -35,7 +35,7 @@ class GPTDatasetV1(Dataset):
return self.input_ids[idx], self.target_ids[idx]
def create_dataloader_v1(txt, batch_size=4, max_length=256,
def create_dataloader_v1(txt, batch_size=4, max_length=256,
stride=128, shuffle=True, drop_last=True):
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("gpt2")
@ -78,7 +78,7 @@ class MultiHeadAttention(nn.Module):
# 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)
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)
@ -100,7 +100,7 @@ class MultiHeadAttention(nn.Module):
attn_weights = self.dropout(attn_weights)
# Shape: (b, num_tokens, num_heads, head_dim)
context_vec = (attn_weights @ values).transpose(1, 2)
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)
@ -132,7 +132,7 @@ class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
torch.sqrt(torch.tensor(2.0 / torch.pi)) *
(x + 0.044715 * torch.pow(x, 3))
))
@ -158,7 +158,7 @@ class TransformerBlock(nn.Module):
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
block_size=cfg["ctx_len"],
num_heads=cfg["n_heads"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
self.ff = FeedForward(cfg)
@ -224,7 +224,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
# Focus only on the last time step
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
logits = logits[:, -1, :]
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)

2
ch05/03_bonus_pretraining_on_gutenberg/prepare_dataset.py
View File

@ -63,4 +63,4 @@ if __name__ == "__main__":
target_dir = "path_to_your_large_files"
print(f"{len(all_files)} files to process.")
combine_files(all_files, args.output_dir)
combine_files(all_files, args.output_dir)

2
ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py
View File

@ -99,7 +99,7 @@ def train_model_simple(model, optimizer, device, n_epochs,
max_length=GPT_CONFIG_124M["ctx_len"],
stride=GPT_CONFIG_124M["ctx_len"]
)
print(f"Training ...")
print("Training ...")
model.train()
for input_batch, target_batch in train_loader:
optimizer.zero_grad()

4
ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
View File

@ -9,11 +9,11 @@ from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
#####################################
# Chapter 2
#####################################
class GPTDatasetV1(Dataset):
def __init__(self, txt, tokenizer, max_length, stride):
self.tokenizer = tokenizer
@ -310,5 +310,3 @@ def text_to_token_ids(text, tokenizer):
def token_ids_to_text(token_ids, tokenizer):
flat = token_ids.squeeze(0) # remove batch dimension
return tokenizer.decode(flat.tolist())