diffusionsfm/model/memory_efficient_attention.py

import ipdb
import torch.nn as nn
from xformers.ops import memory_efficient_attention


class MEAttention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_norm=False,
        attn_drop=0.0,
        proj_drop=0.0,
        norm_layer=nn.LayerNorm,
    ):
        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.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):
        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)

        # MEA expects [B, N, H, D], whereas timm uses [B, H, N, D]
        x = memory_efficient_attention(
            q.transpose(1, 2),
            k.transpose(1, 2),
            v.transpose(1, 2),
            scale=self.scale,
        )
        x = x.reshape(B, N, C)

        x = self.proj(x)
        x = self.proj_drop(x)
        return x