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transformer_blocks.py
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transformer_blocks.py
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import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
class SinusoidalEmbedding(nn.Module):
def __init__(self, emb_min_freq=1.0, emb_max_freq=1000.0, embedding_dims=32):
super(SinusoidalEmbedding, self).__init__()
frequencies = torch.exp(
torch.linspace(np.log(emb_min_freq), np.log(emb_max_freq), embedding_dims // 2)
)
self.register_buffer("angular_speeds", 2.0 * torch.pi * frequencies)
def forward(self, x):
embeddings = torch.cat(
[torch.sin(self.angular_speeds * x), torch.cos(self.angular_speeds * x)], dim=-1
)
return embeddings
class MHAttention(nn.Module):
def __init__(self, is_causal=False, dropout_level=0.0, n_heads=4):
super().__init__()
self.is_causal = is_causal
self.dropout_level = dropout_level
self.n_heads = n_heads
def forward(self, q, k, v, attn_mask=None):
assert q.size(-1) == k.size(-1)
assert k.size(-2) == v.size(-2)
q, k, v = [rearrange(x, "bs n (h d) -> bs h n d", h=self.n_heads) for x in [q, k, v]]
out = nn.functional.scaled_dot_product_attention(
q,
k,
v,
attn_mask=attn_mask,
is_causal=self.is_causal,
dropout_p=self.dropout_level if self.training else 0,
)
out = rearrange(out, "bs h n d -> bs n (h d)", h=self.n_heads)
return out
class SelfAttention(nn.Module):
def __init__(self, embed_dim, is_causal=False, dropout_level=0.0, n_heads=4):
super().__init__()
self.qkv_linear = nn.Linear(embed_dim, 3 * embed_dim, bias=False)
self.mha = MHAttention(is_causal, dropout_level, n_heads)
def forward(self, x):
q, k, v = self.qkv_linear(x).chunk(3, dim=2)
return self.mha(q, k, v)
class CrossAttention(nn.Module):
def __init__(self, embed_dim, is_causal=False, dropout_level=0, n_heads=4):
super().__init__()
self.kv_linear = nn.Linear(embed_dim, 2 * embed_dim, bias=False)
self.q_linear = nn.Linear(embed_dim, embed_dim, bias=False)
self.mha = MHAttention(is_causal, dropout_level, n_heads)
def forward(self, x, y):
q = self.q_linear(x)
k, v = self.kv_linear(y).chunk(2, dim=2)
return self.mha(q, k, v)
class MLP(nn.Module):
def __init__(self, embed_dim, mlp_multiplier, dropout_level):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(embed_dim, mlp_multiplier * embed_dim),
nn.GELU(),
nn.Linear(mlp_multiplier * embed_dim, embed_dim),
nn.Dropout(dropout_level),
)
def forward(self, x):
return self.mlp(x)
class MLPSepConv(nn.Module):
def __init__(self, embed_dim, mlp_multiplier, dropout_level):
"""see: https://github.com/ofsoundof/LocalViT"""
super().__init__()
self.mlp = nn.Sequential(
# this Conv with kernel size 1 is equivalent to the Linear layer in a "regular" transformer MLP
nn.Conv2d(embed_dim, mlp_multiplier * embed_dim, kernel_size=1, padding="same"),
nn.Conv2d(
mlp_multiplier * embed_dim,
mlp_multiplier * embed_dim,
kernel_size=3,
padding="same",
groups=mlp_multiplier * embed_dim,
), # <- depthwise conv
nn.GELU(),
nn.Conv2d(mlp_multiplier * embed_dim, embed_dim, kernel_size=1, padding="same"),
nn.Dropout(dropout_level),
)
def forward(self, x):
w = h = int(np.sqrt(x.size(1))) # only square images for now
x = rearrange(x, "bs (h w) d -> bs d h w", h=h, w=w)
x = self.mlp(x)
x = rearrange(x, "bs d h w -> bs (h w) d")
return x
class DecoderBlock(nn.Module):
def __init__(
self,
embed_dim: int,
is_causal: bool,
mlp_multiplier: int,
dropout_level: float,
mlp_class: type[MLP] | type[MLPSepConv],
):
super().__init__()
self.self_attention = SelfAttention(embed_dim, is_causal, dropout_level, n_heads=embed_dim // 64)
self.cross_attention = CrossAttention(
embed_dim, is_causal=False, dropout_level=0, n_heads=embed_dim // 64
)
self.mlp = mlp_class(embed_dim, mlp_multiplier, dropout_level)
self.norm1 = nn.LayerNorm(embed_dim)
self.norm2 = nn.LayerNorm(embed_dim)
self.norm3 = nn.LayerNorm(embed_dim)
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
x = self.self_attention(self.norm1(x)) + x
x = self.cross_attention(self.norm2(x), y) + x
x = self.mlp(self.norm3(x)) + x
return x