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import torch
from torch import nn
from torch.nn import functional as F
import math
class SelfAttention(nn.Module):
def __init__(self, n_heads, d_embed, in_proj_bias=True, out_proj_bias=True):
super().__init__()
self.in_proj = nn.Linear(d_embed, 3 * d_embed, bias=in_proj_bias)
self.out_proj = nn.Linear(d_embed, d_embed, bias=out_proj_bias)
self.n_heads = n_heads
self.d_head = d_embed // n_heads
def forward(self, x, causal_mask=False):
input_shape = x.shape
batch_size, sequence_length, d_embed = input_shape
interim_shape = (batch_size, sequence_length, self.n_heads, self.d_head)
q, k, v = self.in_proj(x).chunk(3, dim=-1)
q = q.view(interim_shape).transpose(1, 2)
k = k.view(interim_shape).transpose(1, 2)
v = v.view(interim_shape).transpose(1, 2)
weight = q @ k.transpose(-1, -2)
if causal_mask:
mask = torch.ones_like(weight, dtype=torch.bool).triu(1)
weight.masked_fill_(mask, -torch.inf)
weight /= math.sqrt(self.d_head)
weight = F.softmax(weight, dim=-1)
output = weight @ v
output = output.transpose(1, 2)
output = output.reshape(input_shape)
output = self.out_proj(output)
return output
class CrossAttention(nn.Module):
def __init__(self, n_heads, d_embed, d_cross, in_proj_bias=True, out_proj_bias=True):
super().__init__()
self.q_proj = nn.Linear(d_embed, d_embed, bias=in_proj_bias)
self.k_proj = nn.Linear(d_cross, d_embed, bias=in_proj_bias)
self.v_proj = nn.Linear(d_cross, d_embed, bias=in_proj_bias)
self.out_proj = nn.Linear(d_embed, d_embed, bias=out_proj_bias)
self.n_heads = n_heads
self.d_head = d_embed // n_heads
def forward(self, x, y):
input_shape = x.shape
batch_size, sequence_length, d_embed = input_shape
interim_shape = (batch_size, -1, self.n_heads, self.d_head)
q = self.q_proj(x)
k = self.k_proj(y)
v = self.v_proj(y)
q = q.view(interim_shape).transpose(1, 2)
k = k.view(interim_shape).transpose(1, 2)
v = v.view(interim_shape).transpose(1, 2)
weight = q @ k.transpose(-1, -2)
weight /= math.sqrt(self.d_head)
weight = F.softmax(weight, dim=-1)
output = weight @ v
output = output.transpose(1, 2).contiguous()
output = output.view(input_shape)
output = self.out_proj(output)
return output |