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import torch
import torch.nn as nn
import torch.nn.functional as F
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 FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
nn.GELU(),
nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
)
def forward(self, x):
return self.layers(x)
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
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)
self.dropout = nn.Dropout(dropout)
self.register_buffer(
"mask",
torch.triu(torch.ones(context_length, context_length), diagonal=1).bool()
)
def forward(self, x):
b, num_tokens, _ = x.shape
keys = self.W_key(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2)
queries = self.W_query(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2)
values = self.W_value(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2)
attn_scores = queries @ keys.transpose(2, 3)
mask = self.mask[:num_tokens, :num_tokens]
attn_scores.masked_fill_(mask, float("-inf"))
attn_weights = torch.softmax(attn_scores / (keys.shape[-1] ** 0.5), dim=-1)
attn_weights = self.dropout(attn_weights)
context = (attn_weights @ values).transpose(1, 2).contiguous().view(b, num_tokens, self.d_out)
return self.out_proj(context)
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"],
)
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg["emb_dim"])
self.norm2 = LayerNorm(cfg["emb_dim"])
self.drop = nn.Dropout(cfg["drop_rate"])
def forward(self, x):
x = x + self.drop(self.att(self.norm1(x)))
x = x + self.drop(self.ff(self.norm2(x)))
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 = torch.arange(seq_len, device=in_idx.device)
pos_embeds = self.pos_emb(pos)
x = self.drop_emb(tok_embeds + pos_embeds)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x)
return logits
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