transformer.py

import torch
import numpy as np
import pandas as pd
import scanpy as sc
import torch.nn.functional as F
from easydl import *
from anndata import AnnData
from torch import nn, einsum
from scipy.stats import pearsonr
from torch.autograd import Function
from torch.autograd.variable import *
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class SelectItem(nn.Module):
    def __init__(self, item_index):
        super(SelectItem, self).__init__()
        self.item_index = item_index

    def forward(self, inputs):
        return inputs[self.item_index]
class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim = -1)
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()
        
    # @get_local('attn')
    def forward(self, x):
        b, n, _, h = *x.shape, self.heads
        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
        attn = self.attend(dots)
        out = einsum('b h i j, b h j d -> b h i d', attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class attn_block(nn.Module):
    def __init__(self, dim, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.attn=PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))
        self.ff=PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
    def forward(self, x):
        x = self.attn(x) + x
        x = self.ff(x) + x
        return x