64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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class MultiHeadSelfAttention(nn.Module):
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def __init__(self, embed_dim, heads):
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super().__init__()
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assert embed_dim % heads == 0
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self.heads = heads
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self.head_dim = embed_dim // heads
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self.scale = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))
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self.to_qkv = nn.Linear(embed_dim, embed_dim * 3)
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self.out = nn.Linear(embed_dim, embed_dim)
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def forward(self, x):
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B, T, C = x.shape
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qkv = self.to_qkv(x).view(B, T, self.heads, 3 * self.head_dim)
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q, k, v = qkv.chunk(3, dim=-1)
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attn_scores = (q @ k.transpose(-2, -1)) / self.scale
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attn_weights = torch.softmax(attn_scores, dim=-1)
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out = attn_weights @ v
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out = out.transpose(1, 2).contiguous().view(B, T, C)
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return self.out(out)
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class TransformerBlock(nn.Module):
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def __init__(self, embed_dim, heads):
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super().__init__()
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self.attn = MultiHeadSelfAttention(embed_dim, heads)
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self.norm1 = nn.LayerNorm(embed_dim)
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self.ff = nn.Sequential(
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nn.Linear(embed_dim, embed_dim * 4),
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nn.ReLU(),
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nn.Linear(embed_dim * 4, embed_dim)
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)
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self.norm2 = nn.LayerNorm(embed_dim)
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def forward(self, x):
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x = x + self.attn(self.norm1(x))
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x = x + self.ff(self.norm2(x))
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return x
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class TinyTransformer(nn.Module):
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def __init__(self, vocab_size, embed_dim=256, depth=4, heads=8):
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super().__init__()
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self.token_embed = nn.Embedding(vocab_size, embed_dim)
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self.pos_embed = nn.Parameter(torch.randn(1, 128, embed_dim))
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self.blocks = nn.Sequential(*[TransformerBlock(embed_dim, heads) for _ in range(depth)])
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self.norm = nn.LayerNorm(embed_dim)
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self.head = nn.Linear(embed_dim, vocab_size)
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def forward(self, x):
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B, T = x.shape
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tok = self.token_embed(x)
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pos = self.pos_embed[:, :T, :]
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x = tok + pos
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x = self.blocks(x)
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x = self.norm(x)
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return self.head(x)
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