You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
367 lines
14 KiB
Python
367 lines
14 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from torch.nn import functional as F
|
|
from einops import rearrange
|
|
import numpy as np
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
from datetime import datetime
|
|
import sys
|
|
from pathlib import Path
|
|
import shutil
|
|
seed = 1337
|
|
torch.manual_seed(seed)
|
|
|
|
batch_size = 50
|
|
gradient_accumulation_steps = 1 # check this impl for correctness https://unsloth.ai/blog/gradient
|
|
max_iters = int(4e4) #40000
|
|
eval_interval = 300
|
|
learning_rate = 1e-3
|
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
eval_iters = 200
|
|
layers_num = 2
|
|
h_dim = 64
|
|
max_seq_len = 128
|
|
num_heads = 1
|
|
dropout_rate = 0.1
|
|
pixel_size = 3.6e-6
|
|
assert batch_size % gradient_accumulation_steps == 0
|
|
|
|
############################### MODEL #############################################################
|
|
|
|
# RoFormer: Enhanced Transformer with Rotary Position Embedding https://arxiv.org/abs/2104.09864
|
|
class RoPE(nn.Module):
|
|
def __init__(self, dim, max_seq_len=512):
|
|
super().__init__()
|
|
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
|
t = torch.arange(max_seq_len).type_as(inv_freq)
|
|
freqs = torch.einsum('i,j->ij', t, inv_freq)
|
|
self.register_buffer('emb', torch.cat([freqs, freqs], dim=-1))
|
|
|
|
def rotate_half(self, x):
|
|
x1, x2 = x.chunk(2, dim=-1)
|
|
return torch.cat((-x2, x1), dim=-1)
|
|
|
|
def forward(self, x, offset=0):
|
|
seq_len = x.size(1)
|
|
emb = self.emb[offset:offset+seq_len].view(1, seq_len, -1)
|
|
cos = emb.cos()
|
|
sin = emb.sin()
|
|
return (x * cos) + (self.rotate_half(x) * sin)
|
|
|
|
# Transformers without Normalization https://jiachenzhu.github.io/DyT/
|
|
class DyT(nn.Module):
|
|
def __init__(self, num_features, alpha_init_value=0.5):
|
|
super().__init__()
|
|
self.alpha = nn.Parameter(torch.ones(1) * alpha_init_value)
|
|
self.weight = nn.Parameter(torch.ones(num_features))
|
|
self.bias = nn.Parameter(torch.zeros(num_features))
|
|
|
|
def forward(self, x):
|
|
x = torch.tanh(self.alpha * x)
|
|
return x * self.weight + self.bias
|
|
|
|
# Attention Is All You Need https://arxiv.org/pdf/1706.03762v7
|
|
# NeoBERT: A Next-Generation BERT https://arxiv.org/html/2502.19587v1
|
|
class TransformerLayer(nn.Module):
|
|
def __init__(self, h_dim, num_heads=4, dropout_rate = 0.1, max_seq_len = 128):
|
|
super().__init__()
|
|
self.__dict__.update({k:v for k,v in locals().items() if k != 'self'})
|
|
self.q_proj = nn.Linear(h_dim, h_dim)
|
|
self.k_proj = nn.Linear(h_dim, h_dim)
|
|
self.v_proj = nn.Linear(h_dim, h_dim)
|
|
self.o_proj = nn.Linear(h_dim, h_dim)
|
|
self.ff1 = nn.Linear(h_dim, 4*h_dim)
|
|
self.ff2 = nn.Linear(4*h_dim, h_dim)
|
|
self.ln1 = DyT(h_dim)
|
|
self.ln2 = DyT(h_dim)
|
|
self.rope = RoPE(dim=h_dim//self.num_heads, max_seq_len=max_seq_len)
|
|
self.k1 = nn.Parameter(torch.randn(1))
|
|
self.k2 = nn.Parameter(torch.randn(1))
|
|
|
|
def split_to_heads(self, x, B, T, H):
|
|
if self.num_heads <= 1: return x
|
|
return rearrange(x, 'b t (n h) -> (b n) t h', b=B, t=T, n=self.num_heads)
|
|
|
|
def gather_heads(self, x, B, T, H):
|
|
if self.num_heads <= 1: return x
|
|
return rearrange(x, '(b n) t h -> b t (n h)', b=B, t=T, n=self.num_heads)
|
|
|
|
def attention(self, x):
|
|
q = self.rope(self.split_to_heads(self.q_proj(x), *x.shape))
|
|
k = self.rope(self.split_to_heads(self.k_proj(x), *x.shape))
|
|
v = self.split_to_heads(self.v_proj(x), *x.shape)
|
|
scores = (self.k1 * (q @ k.transpose(1, 2))) * (self.h_dim ** -0.5)
|
|
tril = torch.tril(torch.ones(x.shape[1],x.shape[1])).to(self.q_proj.bias.device)
|
|
scores = scores.masked_fill(tril == 0, float('-inf')) # encoder does not need this line
|
|
attention = nn.functional.softmax(scores, dim=2)
|
|
return self.o_proj(self.gather_heads(self.k2 * (attention @ v), *x.shape))
|
|
|
|
def forward(self, x):
|
|
x = x + F.dropout1d(self.attention(self.ln1(x)), p=self.dropout_rate)
|
|
x = x + F.dropout1d(self.ff2(F.gelu(self.ff1(self.ln2(x)))), p=self.dropout_rate)
|
|
return x
|
|
|
|
class GPT2(nn.Module):
|
|
def __init__(self, vocab_size, layers_num=1, h_dim=64, max_seq_len=64, num_heads=1, dropout_rate = 0.1, pixel_size=None):
|
|
super().__init__()
|
|
self.__dict__.update({k:v for k,v in locals().items() if k != 'self'})
|
|
self.layers = nn.ModuleList([
|
|
TransformerLayer(h_dim=self.h_dim, num_heads=self.num_heads, dropout_rate=self.dropout_rate, max_seq_len=self.max_seq_len)
|
|
for _ in range(layers_num)])
|
|
self.tok_embeds = nn.Embedding(vocab_size, h_dim)
|
|
self.pos_embeds = nn.Parameter(torch.randn(1, self.max_seq_len, h_dim))
|
|
self.lm_head = nn.Linear(h_dim, vocab_size)
|
|
|
|
def forward(self, x, targets=None):
|
|
x = self.tok_embeds(x) + self.pos_embeds[:, :x.shape[1], :]
|
|
for l in self.layers:
|
|
x = l(x)
|
|
logits = self.lm_head(x) # B,T,C
|
|
loss = F.cross_entropy(rearrange(logits, "b t c -> b c t"), targets) if not targets is None else None
|
|
return logits, loss
|
|
|
|
# what is the purpose? autoregressive inference!
|
|
def generate(self, start_idx, max_new_tokens):
|
|
idx = start_idx
|
|
for i in range(max_new_tokens):
|
|
idx_cond = idx[:,-self.max_seq_len:]
|
|
logits, loss = self(idx_cond)
|
|
logits = logits[:,-1,:] # B, C
|
|
probs = F.softmax(logits, dim=-1)
|
|
idx_next = torch.multinomial(probs, num_samples=1).to(self.lm_head.bias.device)
|
|
idx = torch.cat([idx, idx_next], dim=1)
|
|
return idx
|
|
|
|
###################################################################################################
|
|
|
|
MODEL_CLASS = GPT2
|
|
train_data_path = Path("./data/wiki.train.tokens")
|
|
val_data_path = Path("./data/wiki.valid.tokens")
|
|
test_data_path = Path("./data/wiki.test.tokens")
|
|
comment = f"{Path(__file__).stem}_{train_data_path.name}_seq_{max_seq_len}{'_' + sys.argv[1] if len(sys.argv) > 1 else ''}"
|
|
|
|
logs_dir = f'logs/{datetime.now().date()}_{datetime.now().hour:02d}_{datetime.now().minute:02d}_{datetime.now().second:02d}_{comment}/'
|
|
writer = SummaryWriter(logs_dir)
|
|
script_snapshot_path = Path(logs_dir + Path(sys.argv[0]).parent.name)
|
|
print("Logs dir:", logs_dir)
|
|
# script_snapshot_path.write_bytes(Path(sys.argv[0]).read_bytes()) # copy this version of script
|
|
shutil.copytree(Path(sys.argv[0]).parent, script_snapshot_path) # snapshot this version of repository
|
|
script_snapshot_path.chmod(0o500) # with read-only permission
|
|
|
|
# Create standalone checkpoints directory with your specified format
|
|
checkpoints_dir = f'./checkpoints/{datetime.now().date()}_{datetime.now().hour:02d}_{datetime.now().minute:02d}_{datetime.now().second:02d}_{comment}/'
|
|
Path(checkpoints_dir).mkdir(parents=True, exist_ok=True)
|
|
print("Checkpoints dir:", checkpoints_dir)
|
|
|
|
#################################### Dataset #########################################
|
|
|
|
# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
|
|
with open(train_data_path, encoding='utf-8') as f:
|
|
train_text = f.read()
|
|
|
|
with open(val_data_path, encoding='utf-8') as f:
|
|
val_text = f.read()
|
|
|
|
with open(test_data_path, encoding='utf-8') as f:
|
|
test_text = f.read()
|
|
|
|
text = train_text + val_text + test_text
|
|
|
|
chars = sorted(set(text))
|
|
vocab_size = len(chars)
|
|
|
|
wtoi = {w:i for i,w in enumerate(chars)}
|
|
itow = {i:w for i,w in enumerate(chars)}
|
|
|
|
encode = lambda s: [wtoi[w] for w in s]
|
|
decode = lambda idx: ''.join([itow[i] for i in idx])
|
|
|
|
def get_batch(data, seq_len, batch_size):
|
|
ix = torch.randint(len(data)-seq_len, (batch_size, ))
|
|
x = torch.stack([data[i:i+seq_len] for i in ix]).to(device)
|
|
y = torch.stack([data[i+1:i+1+seq_len] for i in ix]).to(device)
|
|
return x, y
|
|
|
|
train_data = torch.tensor(encode(train_text), dtype=torch.long)
|
|
val_data = torch.tensor(encode(val_text), dtype=torch.long)
|
|
test_data = torch.tensor(encode(test_text), dtype=torch.long)
|
|
|
|
@torch.no_grad()
|
|
def perplexity(model, data, batch_size=32):
|
|
model.eval()
|
|
stride = max(1, len(data) // 10000)
|
|
total_loss_sum = 0.0
|
|
total_tokens_count = 0
|
|
|
|
# Precompute all valid start positions
|
|
start_positions = list(range(0, len(data) - max_seq_len - 1, stride))
|
|
total_sequences = len(start_positions)
|
|
|
|
# Process sequences in batches
|
|
for i in range(0, total_sequences, batch_size):
|
|
batch_starts = start_positions[i:min(i + batch_size, total_sequences)]
|
|
|
|
# Efficiently stack sequences into batch tensors
|
|
x_batch = torch.stack([
|
|
data[start:start + max_seq_len]
|
|
for start in batch_starts
|
|
]).to(device)
|
|
|
|
y_batch = torch.stack([
|
|
data[start + 1:start + max_seq_len + 1]
|
|
for start in batch_starts
|
|
]).to(device)
|
|
|
|
# Forward pass (model should return mean loss averaged over all tokens in batch)
|
|
_, mean_loss = model(x_batch, y_batch)
|
|
|
|
# Accumulate weighted loss (mean_loss is already averaged over tokens)
|
|
num_tokens = y_batch.numel()
|
|
total_loss_sum += mean_loss.item() * num_tokens
|
|
total_tokens_count += num_tokens
|
|
|
|
# Progress update
|
|
processed = min(i + batch_size, total_sequences)
|
|
print(f"\rppl {processed}/{total_sequences} ({processed/total_sequences*100:.1f}%)", end="", flush=True)
|
|
return np.exp(total_loss_sum / total_tokens_count)
|
|
|
|
#################################### Model #########################################mo
|
|
def complete(m, start_idxs=[0], max_new_tokens=100):
|
|
start_idx = torch.tensor([start_idxs]).to(device)
|
|
generated_tokens = m.generate(start_idx=start_idx, max_new_tokens=max_new_tokens)
|
|
return decode(generated_tokens[0].tolist())
|
|
|
|
m = MODEL_CLASS(
|
|
vocab_size=vocab_size,
|
|
h_dim=h_dim,
|
|
max_seq_len=max_seq_len,
|
|
num_heads=num_heads,
|
|
pixel_size=pixel_size,
|
|
layers_num=layers_num
|
|
)
|
|
m = m.to(device)
|
|
model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
|
|
writer.add_text('model', model_description, 0)
|
|
|
|
print(f"{sum(p.numel() for p in m.parameters()) * 8} minimum number of tokens to train model.")
|
|
print(f"{(sum(p.numel() for p in m.parameters()) * 8)//(batch_size)} minimum number of iterations to train this model.")
|
|
|
|
optimizer = torch.optim.AdamW(m.parameters(), lr=learning_rate, betas=(0.90, 0.95), weight_decay=0.01)
|
|
|
|
#################################### Checkpoint Function #########################################
|
|
|
|
def save_checkpoint(model, optimizer, step, loss, config, wtoi, itow, checkpoint_dir):
|
|
"""Save model checkpoint with complete training state"""
|
|
checkpoint_path = Path(checkpoint_dir) / f'checkpoint_{step:07d}.pt'
|
|
torch.save({
|
|
'step': step,
|
|
'model_state_dict': model.state_dict(),
|
|
'optimizer_state_dict': optimizer.state_dict(),
|
|
'loss': loss,
|
|
'config': config,
|
|
'wtoi': wtoi,
|
|
'itow': itow,
|
|
}, checkpoint_path)
|
|
|
|
# Training config for checkpointing
|
|
training_config = {
|
|
'vocab_size': vocab_size,
|
|
'layers_num': layers_num,
|
|
'h_dim': h_dim,
|
|
'max_seq_len': max_seq_len,
|
|
'num_heads': num_heads,
|
|
'dropout_rate': dropout_rate,
|
|
'batch_size': batch_size,
|
|
'learning_rate': learning_rate,
|
|
'gradient_accumulation_steps': gradient_accumulation_steps,
|
|
'pixel_size': pixel_size,
|
|
'max_iters': max_iters,
|
|
}
|
|
#################################### Train #########################################
|
|
|
|
start_time = datetime.now()
|
|
print("Started at:", start_time)
|
|
|
|
m.eval()
|
|
task_prompts = [
|
|
"1 2 3 4 5",
|
|
"The capital of France is",
|
|
"The chemical symbol of gold is",
|
|
"If yesterday was Friday, then tomorrow will be",
|
|
"The opposite of hot is",
|
|
"The planets of the solar system are:",
|
|
"My favorite color is",
|
|
"If 5*x + 3 = 13, then x is",
|
|
]
|
|
completion = complete(m, encode("\n\n"), max_seq_len)
|
|
print(completion)
|
|
writer.add_text('completions', completion, 0)
|
|
|
|
task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt in task_prompts])
|
|
print(task_results)
|
|
writer.add_text('completions/task', task_results, 0)
|
|
|
|
for i in range(max_iters):
|
|
m.train()
|
|
optimizer.zero_grad(set_to_none=True)
|
|
accumulated_loss = 0.0
|
|
for j in range(gradient_accumulation_steps):
|
|
xb, yb = get_batch(train_data, seq_len=max_seq_len, batch_size=batch_size//gradient_accumulation_steps)
|
|
logits, loss = m(xb, yb)
|
|
loss = loss / gradient_accumulation_steps
|
|
loss.backward()
|
|
accumulated_loss += loss.item()
|
|
if i % 100 == 0:
|
|
writer.add_scalar('Gradient_Norm/Total', torch.nn.utils.clip_grad_norm_(m.parameters(), max_norm=1e6).item(), i)
|
|
optimizer.step()
|
|
writer.add_scalar('loss', accumulated_loss, i)
|
|
print(f"\r{i}/{max_iters} {accumulated_loss}", end="")
|
|
if i % 5000 == 0:
|
|
m.eval()
|
|
ppl = perplexity(model=m, data=val_data, batch_size=batch_size)
|
|
writer.add_scalar('val_perplexity', ppl.item(), i)
|
|
print(f"\r{datetime.now() - start_time} Perplexity at {i}: {ppl}")
|
|
writer.add_text('completions', complete(m, encode("\n\n"), max_seq_len), i)
|
|
task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt in task_prompts])
|
|
writer.add_text('completions/task', task_results, i)
|
|
save_checkpoint(
|
|
model=m,
|
|
optimizer=optimizer,
|
|
step=i,
|
|
loss=accumulated_loss,
|
|
config=training_config,
|
|
wtoi=wtoi,
|
|
itow=itow,
|
|
checkpoint_dir=checkpoints_dir
|
|
)
|
|
|
|
m.eval()
|
|
ppl = perplexity(model=m, data=val_data, batch_size=batch_size)
|
|
print(f"\r{i+1}/{max_iters} {accumulated_loss}")
|
|
print(f"\r{datetime.now()} Perplexity at {i}: {ppl}")
|
|
writer.add_scalar('val_perplexity', ppl.item(), i+1)
|
|
writer.add_scalar('loss', accumulated_loss, i)
|
|
|
|
ppl = perplexity(model=m, data=test_data, batch_size=batch_size)
|
|
writer.add_scalar('test_perplexity', ppl.item(), i+1)
|
|
print(f"\rTest Perplexity at {i}: {ppl}")
|
|
|
|
completion = complete(m, encode("\n\n"), max_seq_len)
|
|
print(completion)
|
|
writer.add_text('completions', completion, i+1)
|
|
task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt in task_prompts])
|
|
print(task_results)
|
|
writer.add_text('completions/task', task_results, i+1)
|
|
|
|
# Save final checkpoint
|
|
save_checkpoint(
|
|
model=m,
|
|
optimizer=optimizer,
|
|
step=max_iters,
|
|
loss=accumulated_loss,
|
|
config=training_config,
|
|
wtoi=wtoi,
|
|
itow=itow,
|
|
checkpoint_dir=checkpoints_dir
|
|
)
|
|
print(f"\n✓ Training complete. Final checkpoint saved to {checkpoints_dir}") |