DL-Art-School/codes/models/lucidrains/dalle/attention.py

384 lines
13 KiB
Python

from inspect import isfunction
from math import ceil
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from rotary_embedding_torch import apply_rotary_emb
# helpers
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def stable_softmax(t, dim = -1, alpha = 32 ** 2):
t = t / alpha
t = t - torch.amax(t, dim = dim, keepdim = True).detach()
return (t * alpha).softmax(dim = dim)
def apply_pos_emb(pos_emb, qkv):
n = qkv[0].shape[-2]
pos_emb = pos_emb[..., :n, :]
return tuple(map(lambda t: apply_rotary_emb(pos_emb, t), qkv))
# classes
class Attention(nn.Module):
def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = 64, dropout = 0., stable = False):
super().__init__()
inner_dim = dim_head * heads
self.heads = heads
self.seq_len = seq_len
self.scale = dim_head ** -0.5
self.stable = stable
self.causal = causal
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x, mask = None, rotary_pos_emb = None):
b, n, _, h, device = *x.shape, self.heads, x.device
softmax = torch.softmax if not self.stable else stable_softmax
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)
if exists(rotary_pos_emb):
q, k, v = apply_pos_emb(rotary_pos_emb, (q, k, v))
q = q * self.scale
dots = torch.einsum('b h i d, b h j d -> b h i j', q, k)
mask_value = max_neg_value(dots)
if exists(mask):
mask = rearrange(mask, 'b j -> b () () j')
dots.masked_fill_(~mask, mask_value)
del mask
if self.causal:
i, j = dots.shape[-2:]
mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
dots.masked_fill_(mask, mask_value)
attn = softmax(dots, dim=-1)
out = torch.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)')
out = self.to_out(out)
return out
# sparse attention with convolutional pattern, as mentioned in the blog post. customizable kernel size and dilation
class SparseConvCausalAttention(nn.Module):
def __init__(self, dim, seq_len, image_size = 32, kernel_size = 5, dilation = 1, heads = 8, dim_head = 64, dropout = 0., stable = False, **kwargs):
super().__init__()
assert kernel_size % 2 == 1, 'kernel size must be odd'
inner_dim = dim_head * heads
self.seq_len = seq_len
self.heads = heads
self.scale = dim_head ** -0.5
self.image_size = image_size
self.kernel_size = kernel_size
self.dilation = dilation
self.stable = stable
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x, mask = None, rotary_pos_emb = None):
b, n, _, h, img_size, kernel_size, dilation, seq_len, device = *x.shape, self.heads, self.image_size, self.kernel_size, self.dilation, self.seq_len, x.device
softmax = torch.softmax if not self.stable else stable_softmax
img_seq_len = img_size ** 2
text_len = seq_len + 1 - img_seq_len
# padding
padding = seq_len - n + 1
mask = default(mask, lambda: torch.ones(b, text_len, device = device).bool())
x = F.pad(x, (0, 0, 0, padding), value = 0)
mask = mask[:, :text_len]
# derive query / keys / values
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)
if exists(rotary_pos_emb):
q, k, v = apply_pos_emb(rotary_pos_emb, (q, k, v))
q *= self.scale
((q_text, q_img), (k_text, k_img), (v_text, v_img)) = map(lambda t: (t[:, :-img_seq_len], t[:, -img_seq_len:]), (q, k, v))
# text attention
dots_text = einsum('b i d, b j d -> b i j', q_text, k_text)
mask_value = max_neg_value(dots_text)
i, j = dots_text.shape[-2:]
text_causal_mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
dots_text.masked_fill_(text_causal_mask, mask_value)
attn_text = softmax(dots_text, dim = -1)
out_text = einsum('b i j, b j d -> b i d', attn_text, v_text)
# image attention
effective_kernel_size = (kernel_size - 1) * dilation + 1
padding = effective_kernel_size // 2
k_img, v_img = map(lambda t: rearrange(t, 'b (h w) c -> b c h w', h = img_size), (k_img, v_img))
k_img, v_img = map(lambda t: F.unfold(t, kernel_size, padding = padding, dilation = dilation), (k_img, v_img))
k_img, v_img = map(lambda t: rearrange(t, 'b (d j) i -> b i j d', j = kernel_size ** 2), (k_img, v_img))
# let image attend to all of text
dots_image = einsum('b i d, b i j d -> b i j', q_img, k_img)
dots_image_to_text = einsum('b i d, b j d -> b i j', q_img, k_text)
# calculate causal attention for local convolution
i, j = dots_image.shape[-2:]
img_seq = torch.arange(img_seq_len, device = device)
k_img_indices = rearrange(img_seq.float(), '(h w) -> () () h w', h = img_size)
k_img_indices = F.pad(k_img_indices, (padding,) * 4, value = img_seq_len) # padding set to be max, so it is never attended to
k_img_indices = F.unfold(k_img_indices, kernel_size, dilation = dilation)
k_img_indices = rearrange(k_img_indices, 'b j i -> b i j')
# mask image attention
q_img_indices = rearrange(img_seq, 'i -> () i ()')
causal_mask = q_img_indices < k_img_indices
# concat text mask with image causal mask
causal_mask = repeat(causal_mask, '() i j -> b i j', b = b * h)
mask = repeat(mask, 'b j -> (b h) i j', i = i, h = h)
mask = torch.cat((~mask, causal_mask), dim = -1)
# image can attend to all of text
dots = torch.cat((dots_image_to_text, dots_image), dim = -1)
dots.masked_fill_(mask, mask_value)
attn = softmax(dots, dim = -1)
# aggregate
attn_image_to_text, attn_image = attn[..., :text_len], attn[..., text_len:]
out_image_to_image = einsum('b i j, b i j d -> b i d', attn_image, v_img)
out_image_to_text = einsum('b i j, b j d -> b i d', attn_image_to_text, v_text)
out_image = out_image_to_image + out_image_to_text
# combine attended values for both text and image
out = torch.cat((out_text, out_image), dim = 1)
out = rearrange(out, '(b h) n d -> b n (h d)', h = h)
out = self.to_out(out)
return out[:, :n]
# sparse axial causal attention
class SparseAxialCausalAttention(nn.Module):
def __init__(self, dim, seq_len, image_size = 32, axis = 0, heads = 8, dim_head = 64, dropout = 0., stable = False, **kwargs):
super().__init__()
assert axis in {0, 1}, 'axis must be either 0 (along height) or 1 (along width)'
self.axis = axis
inner_dim = dim_head * heads
self.seq_len = seq_len
self.heads = heads
self.scale = dim_head ** -0.5
self.image_size = image_size
self.stable = stable
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x, mask = None, rotary_pos_emb = None):
b, n, _, h, img_size, axis, seq_len, device = *x.shape, self.heads, self.image_size, self.axis, self.seq_len, x.device
softmax = torch.softmax if not self.stable else stable_softmax
img_seq_len = img_size ** 2
text_len = seq_len + 1 - img_seq_len
# padding
padding = seq_len - n + 1
mask = default(mask, lambda: torch.ones(b, text_len, device = device).bool())
x = F.pad(x, (0, 0, 0, padding), value = 0)
mask = mask[:, :text_len]
# derive queries / keys / values
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)
if exists(rotary_pos_emb):
q, k, v = apply_pos_emb(rotary_pos_emb, (q, k, v))
q *= self.scale
((q_text, q_img), (k_text, k_img), (v_text, v_img)) = map(lambda t: (t[:, :-img_seq_len], t[:, -img_seq_len:]), (q, k, v))
# text attention
dots_text = einsum('b i d, b j d -> b i j', q_text, k_text)
mask_value = max_neg_value(dots_text)
i, j = dots_text.shape[-2:]
text_causal_mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
dots_text.masked_fill_(text_causal_mask, mask_value)
attn_text = softmax(dots_text, dim = -1)
out_text = einsum('b i j, b j d -> b i d', attn_text, v_text)
# image attention
split_axis_einops = 'b (h w) c -> b h w c' if axis == 0 else 'b (h w) c -> b w h c'
merge_axis_einops = 'b x n d -> b (x n) d' if axis == 0 else 'b x n d -> b (n x) d'
# split out axis
q_img, k_img, v_img = map(lambda t: rearrange(t, split_axis_einops, h = img_size), (q_img, k_img, v_img))
# similarity
dots_image_to_image = einsum('b x i d, b x j d -> b x i j', q_img, k_img)
dots_image_to_text = einsum('b x i d, b j d -> b x i j', q_img, k_text)
dots = torch.cat((dots_image_to_text, dots_image_to_image), dim = -1)
# mask so image has full attention to text, but causal along axis
bh, x, i, j = dots.shape
causal_mask = torch.ones(i, img_size, device = device).triu_(img_size - i + 1).bool()
causal_mask = repeat(causal_mask, 'i j -> b x i j', b = bh, x = x)
mask = repeat(mask, 'b j -> (b h) x i j', h = h, x = x, i = i)
mask = torch.cat((~mask, causal_mask), dim = -1)
dots.masked_fill_(mask, mask_value)
# attention.
attn = softmax(dots, dim = -1)
# aggregate
attn_image_to_text, attn_image_to_image = attn[..., :text_len], attn[..., text_len:]
out_image_to_image = einsum('b x i j, b x j d -> b x i d', attn_image_to_image, v_img)
out_image_to_text = einsum('b x i j, b j d -> b x i d', attn_image_to_text, v_text)
out_image = out_image_to_image + out_image_to_text
# merge back axis
out_image = rearrange(out_image, merge_axis_einops, x = img_size)
# combine attended values for both text and image
out = torch.cat((out_text, out_image), dim = 1)
out = rearrange(out, '(b h) n d -> b n (h d)', h = h)
out = self.to_out(out)
return out[:, :n]
# microsoft sparse attention CUDA kernel
class SparseAttention(Attention):
def __init__(
self,
*args,
block_size = 16,
text_seq_len = 256,
num_random_blocks = None,
**kwargs
):
super().__init__(*args, **kwargs)
from deepspeed.ops.sparse_attention import SparseSelfAttention, VariableSparsityConfig
self.block_size = block_size
num_random_blocks = default(num_random_blocks, self.seq_len // block_size // 4)
global_block_indices = list(range(ceil(text_seq_len / block_size)))
self.attn_fn = SparseSelfAttention(
sparsity_config = VariableSparsityConfig(
num_heads = self.heads,
block = self.block_size,
num_random_blocks = num_random_blocks,
global_block_indices = global_block_indices,
attention = 'unidirectional' if self.causal else 'bidirectional'
),
max_seq_length = self.seq_len,
attn_mask_mode = 'add'
)
def forward(self, x, mask = None, rotary_pos_emb = None):
b, n, _, h, device = *x.shape, self.heads, x.device
remainder = n % self.block_size
mask = default(mask, lambda: torch.ones(b, n, device = device).bool())
if remainder > 0:
padding = self.block_size - remainder
x = F.pad(x, (0, 0, 0, padding), value = 0)
mask = F.pad(mask, (0, padding), value = False)
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)
if exists(rotary_pos_emb):
q, k, v = apply_pos_emb(rotary_pos_emb, (q, k, v))
key_pad_mask = None
if exists(mask):
key_pad_mask = ~mask
attn_mask = None
if self.causal:
i, j = q.shape[-2], k.shape[-2]
mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
attn_mask = torch.zeros(i, j, device = device).to(q)
mask_value = max_neg_value(q) / 2
attn_mask.masked_fill_(mask, mask_value)
out = self.attn_fn(q, k, v, attn_mask = attn_mask, key_padding_mask = key_pad_mask)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return out[:, :n]