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import torch.nn as nn
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.jit import Final
from timm.layers import (
Mlp,
DropPath,
use_fused_attn,
)
class Attention(nn.Module):
"""
Multi-head self-attention layer with optional fused attention (scaled dot-product).
Args:
dim (int): Input embedding dimension.
num_heads (int, optional): Number of attention heads. Defaults to 8.
qkv_bias (bool, optional): Whether to add bias in QKV projections. Defaults to False.
qk_norm (bool, optional): Whether to apply LayerNorm to Q and K. Defaults to False.
attn_drop (float, optional): Dropout probability for attention weights. Defaults to 0.0.
proj_drop (float, optional): Dropout probability after output projection. Defaults to 0.0.
norm_layer (nn.Module, optional): Normalization layer. Defaults to nn.LayerNorm.
"""
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = False,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
norm_layer: nn.Module = nn.LayerNorm,
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of multi-head attention.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, seq_len, dim).
"""
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, self.head_dim)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q,
k,
v,
dropout_p=self.attn_drop.p if self.training else 0.0,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
"""
Applies a learnable scaling parameter (gamma) to the input.
Args:
dim (int): Input embedding dimension.
init_values (float, optional): Initial value of gamma. Defaults to 1e-5.
inplace (bool, optional): Whether to modify the tensor in-place. Defaults to False.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of LayerScale.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).
Returns:
torch.Tensor: Scaled tensor of shape (batch_size, seq_len, dim).
"""
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class TransformerBlock(nn.Module):
"""
Single transformer block consisting of multi-head attention and MLP.
Includes optional LayerScale, residual connections, and stochastic depth.
Args:
dim (int): Input embedding dimension.
num_heads (int): Number of attention heads.
mlp_ratio (float, optional): Expansion ratio for MLP hidden dimension. Defaults to 4.0.
qkv_bias (bool, optional): Whether to add bias in QKV projections. Defaults to False.
qk_norm (bool, optional): Whether to apply LayerNorm to Q and K. Defaults to False.
proj_drop (float, optional): Dropout probability after projections. Defaults to 0.0.
attn_drop (float, optional): Dropout probability in attention. Defaults to 0.0.
init_values (float, optional): Initial value for LayerScale gamma. Defaults to None.
drop_path (float, optional): Drop path (stochastic depth) probability. Defaults to 0.0.
act_layer (nn.Module, optional): Activation layer. Defaults to nn.GELU.
norm_layer (nn.Module, optional): Normalization layer. Defaults to nn.LayerNorm.
mlp_layer (nn.Module, optional): MLP implementation. Defaults to timm Mlp.
"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.0,
attn_drop: float = 0.0,
init_values: Optional[float] = None,
drop_path: float = 0.0,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: nn.Module = Mlp,
) -> None:
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.ls1 = (
LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = mlp_layer(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = (
LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of transformer block.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, seq_len, dim).
"""
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class Transformer(nn.Module):
"""
Transformer encoder consisting of stacked transformer blocks.
Adapted from the timm library implementation.
Args:
embed_dim (int): Input embedding dimension.
num_heads (int): Number of attention heads.
num_layers (int): Number of stacked transformer blocks.
mlp_ratio (float, optional): Expansion ratio for MLP hidden dimension. Defaults to 4.0.
qkv_bias (bool, optional): Whether to add bias in QKV projections. Defaults to False.
qk_norm (bool, optional): Whether to apply LayerNorm to Q and K. Defaults to False.
proj_drop (float, optional): Dropout probability after projections. Defaults to 0.0.
attn_drop (float, optional): Dropout probability in attention. Defaults to 0.0.
drop_path (float, optional): Drop path (stochastic depth) probability. Defaults to 0.0.
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
num_layers: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.0,
attn_drop: float = 0.0,
drop_path: float = 0.0,
):
super(Transformer, self).__init__()
self.blocks = nn.ModuleList(
[
TransformerBlock(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path,
)
for _ in range(num_layers)
]
)
def forward(self, x):
"""
Forward pass of transformer encoder.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, embed_dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, seq_len, embed_dim).
"""
for block in self.blocks:
x = block(x)
return x |