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