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"""
Nano-GPT: GPT-2 style decoder-only transformer
From scratch implementation
"""
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from config import config
class CausalSelfAttention(nn.Module):
"""Multi-head causal self-attention"""
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_heads == 0
# Key, Query, Value for all heads
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
# Output projection
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# Regularization
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_heads = config.n_heads
self.n_embd = config.n_embd
self.dropout = config.dropout
# Causal mask
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
def forward(self, x):
B, T, C = x.size() # batch, sequence, embedding
# Calculate Q, K, V
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
q = q.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
v = v.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
# Attention
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
# Reassemble heads
y = y.transpose(1, 2).contiguous().view(B, T, C)
# Output projection
y = self.resid_dropout(self.c_proj(y))
return y
class MLP(nn.Module):
"""Feed-forward network"""
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.gelu = nn.GELU()
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
"""Transformer block"""
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class NanoGPT(nn.Module):
"""Nano-GPT Model"""
def __init__(self, config):
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.n_embd),
wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config) for _ in range(config.n_layers)]),
ln_f = nn.LayerNorm(config.n_embd),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Weight tying
self.transformer.wte.weight = self.lm_head.weight
# Initialize weights
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layers))
print(f"Number of parameters: {self.get_num_params()/1e6:.2f}M")
def get_num_params(self):
return sum(p.numel() for p in self.parameters())
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
device = idx.device
b, t = idx.size()
assert t <= self.config.block_size
# Embeddings
pos = torch.arange(0, t, dtype=torch.long, device=device)
tok_emb = self.transformer.wte(idx)
pos_emb = self.transformer.wpe(pos)
x = self.transformer.drop(tok_emb + pos_emb)
# Transformer blocks
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
# Language model head
if targets is not None:
logits = self.lm_head(x)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
logits = self.lm_head(x[:, [-1], :])
loss = None
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""Generate text"""
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx |