import math
import random
from functools import partial
import torch
import torch.nn as nn
from x_transformers.x_transformers import (DEFAULT_DIM_HEAD,
AlibiPositionalBias, Attention,
AttentionLayers, FeedForward,
FixedPositionalEmbedding, GRUGating,
LayerIntermediates,
LearnedAlibiPositionalBias,
RelativePositionBias, Residual,
RMSNorm, RotaryEmbedding, Scale,
ScaleNorm, ShiftTokens, cast_tuple,
default, equals, exists,
groupby_prefix_and_trim, not_equals)
import dlas.torch_intermediary as ml
class TimeIntegrationBlock(nn.Module):
def __init__(self, time_emb_dim, dim, normalizer):
super().__init__()
self.emb_layers = nn.Sequential(
nn.SiLU(),
ml.Linear(
time_emb_dim,
2 * dim
),
)
self.normalizer = normalizer
def forward(self, x, time_emb):
emb_out = self.emb_layers(time_emb).type(x.dtype)
scale, shift = torch.chunk(emb_out, 2, dim=1)
x = self.normalizer(x)
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class TimestepEmbeddingAttentionLayers(AttentionLayers):
"""
Modification of x-transformers.AttentionLayers that performs timestep embeddings and layerdrop.
"""
def __init__(
self,
dim,
timestep_dim,
depth,
heads=8,
causal=False,
cross_attend=False,
only_cross=False,
use_scalenorm=False,
use_rmsnorm=False,
use_rezero=False,
alibi_pos_bias=False,
alibi_num_heads=None,
alibi_learned=False,
rel_pos_bias=False,
rel_pos_num_buckets=32,
rel_pos_max_distance=128,
position_infused_attn=False,
rotary_pos_emb=False,
rotary_emb_dim=None,
custom_layers=None,
sandwich_coef=None,
par_ratio=None,
residual_attn=False,
cross_residual_attn=False,
macaron=False,
gate_residual=False,
scale_residual=False,
shift_tokens=0,
use_qk_norm_attn=False,
qk_norm_attn_seq_len=None,
zero_init_branch_output=False,
layerdrop_percent=.1,
**kwargs
):
super().__init__(dim, depth)
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
self.dim = dim
self.depth = depth
self.layers = nn.ModuleList([])
self.layerdrop_percent = layerdrop_percent
self.has_pos_emb = position_infused_attn or rel_pos_bias or rotary_pos_emb
self.pia_pos_emb = FixedPositionalEmbedding(
dim) if position_infused_attn else None
rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32)
self.rotary_pos_emb = RotaryEmbedding(
rotary_emb_dim) if rotary_pos_emb else None
assert not (
alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both'
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
if rel_pos_bias:
self.rel_pos = RelativePositionBias(
scale=dim_head ** 0.5, causal=causal, heads=heads, num_buckets=rel_pos_num_buckets, max_distance=rel_pos_max_distance)
elif alibi_pos_bias:
alibi_num_heads = default(alibi_num_heads, heads)
assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads'
alibi_pos_klass = LearnedAlibiPositionalBias if alibi_learned or not causal else AlibiPositionalBias
self.rel_pos = alibi_pos_klass(
heads=alibi_num_heads, bidirectional=not causal)
else:
self.rel_pos = None
self.residual_attn = residual_attn
self.cross_residual_attn = cross_residual_attn
self.cross_attend = cross_attend
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
norm_class = RMSNorm if use_rmsnorm else norm_class
norm_fn = partial(norm_class, dim)
norm_fn = nn.Identity if use_rezero else norm_fn
branch_fn = Rezero if use_rezero else None
if cross_attend and not only_cross:
default_block = ('a', 'c', 'f')
elif cross_attend and only_cross:
default_block = ('c', 'f')
else:
default_block = ('a', 'f')
if macaron:
default_block = ('f',) + default_block
# qk normalization
if use_qk_norm_attn:
attn_scale_init_value = -math.log(math.log2(qk_norm_attn_seq_len **
2 - qk_norm_attn_seq_len)) if exists(qk_norm_attn_seq_len) else None
attn_kwargs = {**attn_kwargs, 'qk_norm': True,
'scale_init_value': attn_scale_init_value}
# zero init
if zero_init_branch_output:
attn_kwargs = {**attn_kwargs, 'zero_init_output': True}
ff_kwargs = {**ff_kwargs, 'zero_init_output': True}
# calculate layer block order
if exists(custom_layers):
layer_types = custom_layers
elif exists(par_ratio):
par_depth = depth * len(default_block)
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
default_block = tuple(filter(not_equals('f'), default_block))
par_attn = par_depth // par_ratio
# 2 / 3 attention layer cutoff suggested by PAR paper
depth_cut = par_depth * 2 // 3
par_width = (depth_cut + depth_cut // par_attn) // par_attn
assert len(
default_block) <= par_width, 'default block is too large for par_ratio'
par_block = default_block + \
('f',) * (par_width - len(default_block))
par_head = par_block * par_attn
layer_types = par_head + ('f',) * (par_depth - len(par_head))
elif exists(sandwich_coef):
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
layer_types = ('a',) * sandwich_coef + default_block * \
(depth - sandwich_coef) + ('f',) * sandwich_coef
else:
layer_types = default_block * depth
self.layer_types = layer_types
self.num_layer_types = len(set(self.layer_types))
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
# calculate token shifting
shift_tokens = cast_tuple(shift_tokens, len(layer_types))
# iterate and construct layers
for ind, (layer_type, layer_shift_tokens) in enumerate(zip(self.layer_types, shift_tokens)):
if layer_type == 'a':
layer = Attention(dim, heads=heads,
causal=causal, **attn_kwargs)
elif layer_type == 'c':
layer = Attention(dim, heads=heads, **attn_kwargs)
elif layer_type == 'f':
layer = FeedForward(dim, **ff_kwargs)
layer = layer if not macaron else Scale(0.5, layer)
else:
raise Exception(f'invalid layer type {layer_type}')
if layer_shift_tokens > 0:
shift_range_upper = layer_shift_tokens + 1
shift_range_lower = -layer_shift_tokens if not causal else 0
layer = ShiftTokens(
range(shift_range_lower, shift_range_upper), layer)
if exists(branch_fn):
layer = branch_fn(layer)
residual_fn = GRUGating if gate_residual else Residual
residual = residual_fn(dim, scale_residual=scale_residual)
layer_uses_qk_norm = use_qk_norm_attn and layer_type in ('a', 'c')
pre_branch_norm = TimeIntegrationBlock(
timestep_dim, dim, norm_fn())
post_branch_norm = norm_fn() if layer_uses_qk_norm else None
# Always do prenorm for timestep integration.
post_main_norm = None
norms = nn.ModuleList([
pre_branch_norm,
post_branch_norm,
post_main_norm
])
self.layers.append(nn.ModuleList([
norms,
layer,
residual
]))
def forward(
self,
x,
time_emb=None,
context=None,
mask=None,
context_mask=None,
attn_mask=None,
mems=None,
return_hiddens=False
):
assert not (self.cross_attend ^ exists(
context)), 'context must be passed in if cross_attend is set to True'
assert time_emb is not None, 'must specify a timestep embedding.'
hiddens = []
intermediates = []
prev_attn = None
prev_cross_attn = None
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
rotary_pos_emb = None
if exists(self.rotary_pos_emb):
max_rotary_emb_length = max(
list(map(lambda m: (m.shape[1] if exists(m) else 0) + x.shape[1], mems)))
rotary_pos_emb = self.rotary_pos_emb(
max_rotary_emb_length, x.device)
unused_params = []
to_drop = 0
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
if layer_type == 'a':
# Do layer drop where applicable. Do not drop first layer. When doing layer-drop, drop all of the joined layers (e.g. attention + context + feedforward)
if self.training and self.layerdrop_percent > 0 and ind != 0 and random.random() < self.layerdrop_percent:
to_drop = self.num_layer_types
hiddens.append(x)
layer_mem = mems.pop(0) if mems else None
if to_drop > 0:
to_drop -= 1
# Record the unused parameters so they can be used in null-operations later to not trigger DDP.
unused_params.extend(list(block.parameters()))
unused_params.extend(list(residual_fn.parameters()))
unused_params.extend(list(norm.parameters()))
continue
residual = x
pre_branch_norm, post_branch_norm, post_main_norm = norm
x = pre_branch_norm(x, time_emb)
if layer_type == 'a':
out, inter = block(x, mask=mask, attn_mask=attn_mask, sinusoidal_emb=self.pia_pos_emb,
rel_pos=self.rel_pos, rotary_pos_emb=rotary_pos_emb, prev_attn=prev_attn, mem=layer_mem)
elif layer_type == 'c':
out, inter = block(
x, context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
elif layer_type == 'f':
out = block(x)
if exists(post_branch_norm):
out = post_branch_norm(out)
x = residual_fn(out, residual)
if layer_type in ('a', 'c'):
intermediates.append(inter)
if layer_type == 'a' and self.residual_attn:
prev_attn = inter.pre_softmax_attn
elif layer_type == 'c' and self.cross_residual_attn:
prev_cross_attn = inter.pre_softmax_attn
if exists(post_main_norm):
x = post_main_norm(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
x = x + extraneous_addition * 0
if return_hiddens:
intermediates = LayerIntermediates(
hiddens=hiddens,
attn_intermediates=intermediates
)
return x, intermediates
return x