233 lines
8.3 KiB
Python
233 lines
8.3 KiB
Python
from functools import partial
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from itertools import islice, cycle
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange
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from models.lucidrains.dalle.reversible import ReversibleSequence, SequentialSequence
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from models.lucidrains.dalle.attention import Attention, SparseAttention, SparseConvCausalAttention, SparseAxialCausalAttention
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from rotary_embedding_torch import RotaryEmbedding, broadcat
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from g_mlp_pytorch import gMLPBlock
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import torch_intermediary as ml
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# helpers
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def exists(val):
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return val is not None
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def default(val, d):
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return val if exists(val) else d
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def cast_tuple(val, depth = 1):
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if isinstance(val, list):
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val = tuple(val)
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return val if isinstance(val, tuple) else (val,) * depth
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# classes
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class DivideMax(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, x):
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maxes = x.amax(dim = self.dim, keepdim = True).detach()
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return x / maxes
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# https://arxiv.org/abs/2103.17239
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class LayerScale(nn.Module):
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def __init__(self, dim, depth, fn):
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super().__init__()
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if depth <= 18:
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init_eps = 0.1
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elif depth > 18 and depth <= 24:
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init_eps = 1e-5
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else:
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init_eps = 1e-6
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scale = torch.zeros(1, 1, dim).fill_(init_eps)
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self.scale = nn.Parameter(scale)
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(x, **kwargs) * self.scale
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# layer norm
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class PreNorm(nn.Module):
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def __init__(self, dim, fn, sandwich = False):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.norm_out = nn.LayerNorm(dim) if sandwich else nn.Identity()
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self.fn = fn
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def forward(self, x, **kwargs):
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x = self.norm(x)
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x = self.fn(x, **kwargs)
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return self.norm_out(x)
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# feed forward
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class GEGLU(nn.Module):
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def forward(self, x):
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x, gates = x.chunk(2, dim = -1)
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return x * F.gelu(gates)
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class FeedForward(nn.Module):
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def __init__(self, dim, dropout = 0., mult = 4.):
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super().__init__()
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self.net = nn.Sequential(
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ml.Linear(dim, dim * mult * 2),
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GEGLU(),
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nn.Dropout(dropout),
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ml.Linear(dim * mult, dim)
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)
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def forward(self, x):
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return self.net(x)
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# token shift classes
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class PreShiftToken(nn.Module):
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def __init__(self, fn, image_size, seq_len):
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super().__init__()
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self.fn = fn
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self.image_size = image_size
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self.seq_len = seq_len
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def forward(self, x, **kwargs):
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n = x.shape[1]
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seq_len, image_size = self.seq_len, self.image_size
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img_seq_len = image_size ** 2
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text_len = seq_len - img_seq_len + 1
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padding = seq_len - n + 1
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# get text and image tokens
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x_text, x_img = x[:, :text_len], x[:, text_len:]
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x_img = F.pad(x_img, (0, 0, 0, padding))
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x_img = rearrange(x_img, 'b (h w) d -> b h w d', h = image_size)
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# shift 1 from the left for text tokens
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x_text_shift, x_text_pass = x_text.chunk(2, dim = -1)
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x_text_shift = F.pad(x_text_shift, (0, 0, 1, -1))
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x_text = torch.cat((x_text_shift, x_text_pass), dim = -1)
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# shift from top, left for image tokens
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x_img_shift_top, x_img_shift_left, *x_img_pass = x_img.chunk(4, dim = -1)
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x_img_shift_left = F.pad(x_img_shift_left, (0, 0, 1, -1))
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x_img_shift_top = F.pad(x_img_shift_top, (0, 0, 0, 0, 1, -1))
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x_img = torch.cat((x_img_shift_top, x_img_shift_left, *x_img_pass), dim = -1)
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# merge text and image sequence back together
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x_img = rearrange(x_img, 'b h w d -> b (h w) d')
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x = torch.cat((x_text, x_img[:, :-padding]), dim = 1)
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return self.fn(x, **kwargs)
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# main transformer class
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class Transformer(nn.Module):
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def __init__(
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self,
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*,
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dim,
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depth,
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seq_len,
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reversible = False,
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causal = True,
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heads = 8,
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dim_head = 64,
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ff_mult = 4,
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attn_dropout = 0.,
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ff_dropout = 0.,
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attn_types = None,
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image_fmap_size = None,
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oned_fmap_size = None,
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sparse_attn = False,
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stable = False,
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sandwich_norm = False,
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shift_tokens = False,
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rotary_emb = True
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):
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super().__init__()
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layers = nn.ModuleList([])
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sparse_layer = cast_tuple(sparse_attn, depth)
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attn_types = default(attn_types, ('full',))
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attn_types = cast_tuple(attn_types)
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attn_type_layer = islice(cycle(attn_types), depth)
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for ind, sparse_attn, attn_type in zip(range(depth), sparse_layer, attn_type_layer):
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if attn_type == 'full':
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attn_class = partial(Attention, stable = stable)
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elif attn_type == 'sparse':
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attn_class = SparseAttention
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elif attn_type == 'axial_row':
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attn_class = partial(SparseAxialCausalAttention, seq_len = seq_len, axis = 0, image_size = image_fmap_size, stable = stable)
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elif attn_type == 'axial_col':
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attn_class = partial(SparseAxialCausalAttention, seq_len = seq_len, axis = 1, image_size = image_fmap_size, stable = stable)
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elif attn_type == 'conv_like':
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attn_class = partial(SparseConvCausalAttention, seq_len = seq_len, image_size = image_fmap_size, stable = stable)
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elif attn_type == 'mlp':
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attn_class = partial(gMLPBlock, seq_len = seq_len)
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else:
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raise ValueError(f'attention type "{attn_type}" is not valid')
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if attn_type != 'mlp':
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attn = attn_class(dim, causal = causal, seq_len = seq_len, heads = heads, dim_head = dim_head, dropout = attn_dropout)
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else:
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attn = attn_class(dim = dim, causal = causal, dim_ff = dim * 4)
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ff = FeedForward(dim, mult = ff_mult, dropout = ff_dropout)
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if shift_tokens:
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attn, ff = map(lambda t: PreShiftToken(t, image_size = image_fmap_size, seq_len = seq_len), (attn, ff))
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layers.append(nn.ModuleList([
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LayerScale(dim, ind + 1, PreNorm(dim, attn, sandwich = sandwich_norm)),
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LayerScale(dim, ind + 1, PreNorm(dim, ff, sandwich = sandwich_norm))
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]))
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execute_type = ReversibleSequence if reversible else SequentialSequence
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route_attn = ((True, False),) * depth
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attn_route_map = {'mask': route_attn, 'rotary_pos_emb': route_attn}
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self.layers = execute_type(layers, args_route = attn_route_map)
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# generate positional embeddings for rotary
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pos_emb = None
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if rotary_emb:
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assert 'mlp' not in attn_types, 'you cannot use gMLPs if rotary embedding is turned on'
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rot_dim = dim_head // 3
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img_seq_len = (image_fmap_size ** 2) if image_fmap_size is not None else oned_fmap_size
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text_len = seq_len - img_seq_len + 1
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text_pos_emb = RotaryEmbedding(dim = rot_dim)
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img_axial_pos_emb = RotaryEmbedding(dim = rot_dim, freqs_for = 'pixel')
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text_freqs = text_pos_emb(torch.arange(text_len))
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img_to_text_freqs = text_pos_emb(torch.full((img_seq_len,), 8192)) # image is given a position far away from text
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text_freqs = torch.cat((text_freqs, img_to_text_freqs), dim = 0)
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img_freqs_axial = img_axial_pos_emb(torch.linspace(-1, 1, steps = image_fmap_size if image_fmap_size is not None else oned_fmap_size))
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img_freqs = broadcat((rearrange(img_freqs_axial, 'i d -> i () d'), rearrange(img_freqs_axial, 'j d -> () j d')), dim = -1)
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img_freqs = rearrange(img_freqs, 'h w d -> (h w) d')
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text_axial_freqs = img_axial_pos_emb(torch.full((text_len,), -10.)) # text is given a position of -10 apart from the image axial positions, which is from range [-1, 1]
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text_axial_freqs = torch.cat((text_axial_freqs, text_axial_freqs), dim = -1)
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img_freqs = torch.cat((text_axial_freqs, img_freqs), dim = 0)
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pos_emb = torch.cat((text_freqs, img_freqs), dim = -1)
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pos_emb = rearrange(pos_emb, 'n d -> () n d')
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self.register_buffer('pos_emb', pos_emb)
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def forward(self, x, **kwargs):
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return self.layers(x, rotary_pos_emb = self.pos_emb, **kwargs) |