forked from mrq/tortoise-tts
1268 lines
41 KiB
Python
1268 lines
41 KiB
Python
import math
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from collections import namedtuple
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from functools import partial
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from inspect import isfunction
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import torch
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from torch import nn, einsum
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import tortoise.utils.torch_intermediary as ml
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DEFAULT_DIM_HEAD = 64
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Intermediates = namedtuple('Intermediates', [
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'pre_softmax_attn',
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'post_softmax_attn'
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])
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LayerIntermediates = namedtuple('Intermediates', [
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'hiddens',
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'attn_intermediates',
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'past_key_values',
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])
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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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if exists(val):
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return val
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return d() if isfunction(d) else d
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def cast_tuple(val, depth):
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return val if isinstance(val, tuple) else (val,) * depth
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class always():
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def __init__(self, val):
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self.val = val
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def __call__(self, *args, **kwargs):
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return self.val
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class not_equals():
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def __init__(self, val):
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self.val = val
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def __call__(self, x, *args, **kwargs):
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return x != self.val
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class equals():
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def __init__(self, val):
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self.val = val
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def __call__(self, x, *args, **kwargs):
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return x == self.val
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def max_neg_value(tensor):
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return -torch.finfo(tensor.dtype).max
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def l2norm(t):
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return F.normalize(t, p=2, dim=-1)
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# init helpers
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def init_zero_(layer):
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nn.init.constant_(layer.weight, 0.)
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if exists(layer.bias):
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nn.init.constant_(layer.bias, 0.)
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# keyword argument helpers
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def pick_and_pop(keys, d):
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values = list(map(lambda key: d.pop(key), keys))
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return dict(zip(keys, values))
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def group_dict_by_key(cond, d):
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return_val = [dict(), dict()]
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for key in d.keys():
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match = bool(cond(key))
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ind = int(not match)
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return_val[ind][key] = d[key]
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return (*return_val,)
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def string_begins_with(prefix, str):
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return str.startswith(prefix)
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def group_by_key_prefix(prefix, d):
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return group_dict_by_key(partial(string_begins_with, prefix), d)
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def groupby_prefix_and_trim(prefix, d):
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kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
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kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
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return kwargs_without_prefix, kwargs
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# activations
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class ReluSquared(nn.Module):
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def forward(self, x):
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return F.relu(x) ** 2
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# positional embeddings
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class AbsolutePositionalEmbedding(nn.Module):
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def __init__(self, dim, max_seq_len):
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super().__init__()
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self.scale = dim ** -0.5
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# nn.Embedding
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self.emb = ml.Embedding(max_seq_len, dim)
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def forward(self, x):
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n = torch.arange(x.shape[1], device=x.device)
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pos_emb = self.emb(n)
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pos_emb = rearrange(pos_emb, 'n d -> () n d')
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return pos_emb * self.scale
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class FixedPositionalEmbedding(nn.Module):
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def __init__(self, dim):
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super().__init__()
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inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer('inv_freq', inv_freq)
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def forward(self, x, seq_dim=1, offset=0):
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t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
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sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
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emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
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return rearrange(emb, 'n d -> () n d')
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class RelativePositionBias(nn.Module):
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def __init__(self, scale, causal=False, num_buckets=32, max_distance=128, heads=8):
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super().__init__()
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self.scale = scale
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self.causal = causal
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self.num_buckets = num_buckets
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self.max_distance = max_distance
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# nn.Embedding
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self.relative_attention_bias = ml.Embedding(num_buckets, heads)
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@staticmethod
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def _relative_position_bucket(relative_position, causal=True, num_buckets=32, max_distance=128):
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ret = 0
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n = -relative_position
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if not causal:
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num_buckets //= 2
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ret += (n < 0).long() * num_buckets
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n = torch.abs(n)
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else:
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n = torch.max(n, torch.zeros_like(n))
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max_exact = num_buckets // 2
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is_small = n < max_exact
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val_if_large = max_exact + (
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torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
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).long()
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val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
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ret += torch.where(is_small, n, val_if_large)
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return ret
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def forward(self, qk_dots):
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i, j, device = *qk_dots.shape[-2:], qk_dots.device
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q_pos = torch.arange(i, dtype=torch.long, device=device)
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k_pos = torch.arange(j, dtype=torch.long, device=device)
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rel_pos = k_pos[None, :] - q_pos[:, None]
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rp_bucket = self._relative_position_bucket(rel_pos, causal=self.causal, num_buckets=self.num_buckets,
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max_distance=self.max_distance)
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values = self.relative_attention_bias(rp_bucket)
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bias = rearrange(values, 'i j h -> () h i j')
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return qk_dots + (bias * self.scale)
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class AlibiPositionalBias(nn.Module):
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def __init__(self, heads, **kwargs):
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super().__init__()
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self.heads = heads
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slopes = torch.Tensor(self._get_slopes(heads))
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slopes = rearrange(slopes, 'h -> () h () ()')
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self.register_buffer('slopes', slopes, persistent=False)
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self.register_buffer('bias', None, persistent=False)
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@staticmethod
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def _get_slopes(heads):
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def get_slopes_power_of_2(n):
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start = (2 ** (-2 ** -(math.log2(n) - 3)))
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ratio = start
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return [start * ratio ** i for i in range(n)]
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if math.log2(heads).is_integer():
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return get_slopes_power_of_2(heads)
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closest_power_of_2 = 2 ** math.floor(math.log2(heads))
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return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][
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:heads - closest_power_of_2]
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def forward(self, qk_dots):
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h, i, j, device = *qk_dots.shape[-3:], qk_dots.device
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if exists(self.bias) and self.bias.shape[-1] >= j:
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return qk_dots + self.bias[..., :j]
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bias = torch.arange(j, device=device)
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bias = rearrange(bias, 'j -> () () () j')
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bias = bias * self.slopes
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num_heads_unalibied = h - bias.shape[1]
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bias = F.pad(bias, (0, 0, 0, 0, 0, num_heads_unalibied))
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self.register_buffer('bias', bias, persistent=False)
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return qk_dots + self.bias
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class LearnedAlibiPositionalBias(AlibiPositionalBias):
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def __init__(self, heads, bidirectional=False):
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super().__init__(heads)
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los_slopes = torch.log(self.slopes)
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self.learned_logslopes = nn.Parameter(los_slopes)
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self.bidirectional = bidirectional
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if self.bidirectional:
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self.learned_logslopes_future = nn.Parameter(los_slopes)
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def forward(self, qk_dots):
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h, i, j, device = *qk_dots.shape[-3:], qk_dots.device
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def get_slopes(param):
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return F.pad(param.exp(), (0, 0, 0, 0, 0, h - param.shape[1]))
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if exists(self.bias) and self.bias.shape[-1] >= j:
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bias = self.bias[..., :i, :j]
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else:
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i_arange = torch.arange(i, device=device)
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j_arange = torch.arange(j, device=device)
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bias = rearrange(j_arange, 'j -> 1 1 1 j') - rearrange(i_arange, 'i -> 1 1 i 1')
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self.register_buffer('bias', bias, persistent=False)
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if self.bidirectional:
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past_slopes = get_slopes(self.learned_logslopes)
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future_slopes = get_slopes(self.learned_logslopes_future)
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bias = torch.tril(bias * past_slopes) + torch.triu(bias * future_slopes)
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else:
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slopes = get_slopes(self.learned_logslopes)
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bias = bias * slopes
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return qk_dots + bias
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class RotaryEmbedding(nn.Module):
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def __init__(self, dim):
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super().__init__()
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inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer('inv_freq', inv_freq)
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def forward(self, max_seq_len, device):
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t = torch.arange(max_seq_len, device=device).type_as(self.inv_freq)
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freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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return rearrange(emb, 'n d -> () () n d')
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def rotate_half(x):
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x = rearrange(x, '... (j d) -> ... j d', j=2)
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x1, x2 = x.unbind(dim=-2)
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(t, freqs):
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seq_len = t.shape[-2]
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freqs = freqs[:, :, -seq_len:]
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return (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
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# norms
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class Scale(nn.Module):
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def __init__(self, value, fn):
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super().__init__()
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self.value = value
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self.fn = fn
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def forward(self, x, **kwargs):
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out = self.fn(x, **kwargs)
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scale_fn = lambda t: t * self.value
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if not isinstance(out, tuple):
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return scale_fn(out)
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return (scale_fn(out[0]), *out[1:])
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class Rezero(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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self.g = nn.Parameter(torch.zeros(1))
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def forward(self, x, **kwargs):
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out = self.fn(x, **kwargs)
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rezero_fn = lambda t: t * self.g
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if not isinstance(out, tuple):
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return rezero_fn(out)
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return (rezero_fn(out[0]), *out[1:])
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class ScaleNorm(nn.Module):
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def __init__(self, dim, eps=1e-5):
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super().__init__()
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self.scale = dim ** -0.5
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self.eps = eps
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self.g = nn.Parameter(torch.ones(1))
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def forward(self, x):
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norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
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return x / norm.clamp(min=self.eps) * self.g
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class RMSNorm(nn.Module):
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def __init__(self, dim, eps=1e-8):
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super().__init__()
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self.scale = dim ** -0.5
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self.eps = eps
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self.g = nn.Parameter(torch.ones(dim))
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def forward(self, x):
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norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
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return x / norm.clamp(min=self.eps) * self.g
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class RMSScaleShiftNorm(nn.Module):
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def __init__(self, dim, eps=1e-8):
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super().__init__()
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self.scale = dim ** -0.5
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self.eps = eps
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self.g = nn.Parameter(torch.ones(dim))
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# nn.Linear
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self.scale_shift_process = ml.Linear(dim * 2, dim * 2)
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def forward(self, x, norm_scale_shift_inp):
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norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
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norm = x / norm.clamp(min=self.eps) * self.g
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ss_emb = self.scale_shift_process(norm_scale_shift_inp)
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scale, shift = torch.chunk(ss_emb, 2, dim=1)
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h = norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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return h
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# residual and residual gates
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class Residual(nn.Module):
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def __init__(self, dim, scale_residual=False):
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super().__init__()
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self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
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def forward(self, x, residual):
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if exists(self.residual_scale):
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residual = residual * self.residual_scale
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return x + residual
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class GRUGating(nn.Module):
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def __init__(self, dim, scale_residual=False):
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super().__init__()
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self.gru = nn.GRUCell(dim, dim)
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self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
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def forward(self, x, residual):
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if exists(self.residual_scale):
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residual = residual * self.residual_scale
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gated_output = self.gru(
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rearrange(x, 'b n d -> (b n) d'),
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rearrange(residual, 'b n d -> (b n) d')
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)
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return gated_output.reshape_as(x)
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# token shifting
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def shift(t, amount, mask=None):
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if amount == 0:
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return t
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if exists(mask):
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t = t.masked_fill(~mask[..., None], 0.)
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return F.pad(t, (0, 0, amount, -amount), value=0.)
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class ShiftTokens(nn.Module):
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def __init__(self, shifts, fn):
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super().__init__()
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self.fn = fn
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self.shifts = tuple(shifts)
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def forward(self, x, **kwargs):
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mask = kwargs.get('mask', None)
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shifts = self.shifts
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segments = len(shifts)
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feats_per_shift = x.shape[-1] // segments
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splitted = x.split(feats_per_shift, dim=-1)
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segments_to_shift, rest = splitted[:segments], splitted[segments:]
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segments_to_shift = list(map(lambda args: shift(*args, mask=mask), zip(segments_to_shift, shifts)))
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x = torch.cat((*segments_to_shift, *rest), dim=-1)
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return self.fn(x, **kwargs)
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# feedforward
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class GLU(nn.Module):
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def __init__(self, dim_in, dim_out, activation):
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super().__init__()
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self.act = activation
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# nn.Linear
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self.proj = ml.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * self.act(gate)
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class FeedForward(nn.Module):
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def __init__(
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self,
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dim,
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dim_out=None,
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mult=4,
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glu=False,
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relu_squared=False,
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post_act_ln=False,
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dropout=0.,
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zero_init_output=False
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):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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activation = ReluSquared() if relu_squared else nn.GELU()
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project_in = nn.Sequential(
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# nn.Linear
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ml.Linear(dim, inner_dim),
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activation
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) if not glu else GLU(dim, inner_dim, activation)
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self.net = nn.Sequential(
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project_in,
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nn.LayerNorm(inner_dim) if post_act_ln else nn.Identity(),
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nn.Dropout(dropout),
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# nn.Linear
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ml.Linear(inner_dim, dim_out)
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)
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# init last linear layer to 0
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if zero_init_output:
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init_zero_(self.net[-1])
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def forward(self, x):
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return self.net(x)
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# attention.
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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dim_head=DEFAULT_DIM_HEAD,
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heads=8,
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causal=False,
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talking_heads=False,
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head_scale=False,
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collab_heads=False,
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collab_compression=.3,
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sparse_topk=None,
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use_entmax15=False,
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num_mem_kv=0,
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dropout=0.,
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on_attn=False,
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gate_values=False,
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zero_init_output=False,
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max_attend_past=None,
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qk_norm=False,
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scale_init_value=None,
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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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):
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super().__init__()
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.causal = causal
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|
self.max_attend_past = max_attend_past
|
|
|
|
qk_dim = v_dim = dim_head * heads
|
|
|
|
# collaborative heads
|
|
self.collab_heads = collab_heads
|
|
if self.collab_heads:
|
|
qk_dim = int(collab_compression * qk_dim)
|
|
self.collab_mixing = nn.Parameter(torch.randn(heads, qk_dim))
|
|
|
|
# nn.Linear
|
|
self.to_q = ml.Linear(dim, qk_dim, bias=False)
|
|
# nn.Linear
|
|
self.to_k = ml.Linear(dim, qk_dim, bias=False)
|
|
# nn.Linear
|
|
self.to_v = ml.Linear(dim, v_dim, bias=False)
|
|
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
# add GLU gating for aggregated values, from alphafold2
|
|
self.to_v_gate = None
|
|
if gate_values:
|
|
# nn.Linear
|
|
self.to_v_gate = ml.Linear(dim, v_dim)
|
|
nn.init.constant_(self.to_v_gate.weight, 0)
|
|
nn.init.constant_(self.to_v_gate.bias, 1)
|
|
|
|
# cosine sim attention
|
|
self.qk_norm = qk_norm
|
|
if qk_norm:
|
|
scale_init_value = default(scale_init_value,
|
|
-3) # if not provided, initialize as though it were sequence length of 1024
|
|
self.scale = nn.Parameter(torch.ones(1, heads, 1, 1) * scale_init_value)
|
|
|
|
# talking heads
|
|
self.talking_heads = talking_heads
|
|
if talking_heads:
|
|
self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
|
self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
|
|
|
# head scaling
|
|
self.head_scale = head_scale
|
|
if head_scale:
|
|
self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1))
|
|
|
|
# explicit topk sparse attention
|
|
self.sparse_topk = sparse_topk
|
|
|
|
# entmax
|
|
self.attn_fn = F.softmax
|
|
|
|
# add memory key / values
|
|
self.num_mem_kv = num_mem_kv
|
|
if num_mem_kv > 0:
|
|
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
|
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
|
|
|
# attention on attention
|
|
self.attn_on_attn = on_attn
|
|
# nn.Linear
|
|
self.to_out = nn.Sequential(ml.Linear(v_dim, dim * 2), nn.GLU()) if on_attn else ml.Linear(v_dim, dim)
|
|
|
|
self.rel_pos_bias = rel_pos_bias
|
|
if rel_pos_bias:
|
|
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
|
|
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)
|
|
|
|
# init output projection 0
|
|
if zero_init_output:
|
|
init_zero_(self.to_out)
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
context=None,
|
|
mask=None,
|
|
context_mask=None,
|
|
attn_mask=None,
|
|
sinusoidal_emb=None,
|
|
rotary_pos_emb=None,
|
|
prev_attn=None,
|
|
mem=None,
|
|
layer_past=None,
|
|
):
|
|
b, n, _, h, talking_heads, collab_heads, head_scale, scale, device, has_context = *x.shape, self.heads, self.talking_heads, self.collab_heads, self.head_scale, self.scale, x.device, exists(
|
|
context)
|
|
kv_input = default(context, x)
|
|
|
|
q_input = x
|
|
k_input = kv_input
|
|
v_input = kv_input
|
|
|
|
if exists(mem):
|
|
k_input = torch.cat((mem, k_input), dim=-2)
|
|
v_input = torch.cat((mem, v_input), dim=-2)
|
|
|
|
if exists(sinusoidal_emb):
|
|
# in shortformer, the query would start at a position offset depending on the past cached memory
|
|
offset = k_input.shape[-2] - q_input.shape[-2]
|
|
q_input = q_input + sinusoidal_emb(q_input, offset=offset)
|
|
k_input = k_input + sinusoidal_emb(k_input)
|
|
|
|
q = self.to_q(q_input)
|
|
k = self.to_k(k_input)
|
|
v = self.to_v(v_input)
|
|
|
|
if not collab_heads:
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
|
|
else:
|
|
q = einsum('b i d, h d -> b h i d', q, self.collab_mixing)
|
|
k = rearrange(k, 'b n d -> b () n d')
|
|
v = rearrange(v, 'b n (h d) -> b h n d', h=h)
|
|
|
|
if layer_past is not None:
|
|
past_key, past_value = layer_past
|
|
k = torch.cat([past_key, k], dim=-2)
|
|
v = torch.cat([past_value, v], dim=-2)
|
|
k_cache = k
|
|
v_cache = v
|
|
|
|
if exists(rotary_pos_emb) and not has_context:
|
|
l = rotary_pos_emb.shape[-1]
|
|
(ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v))
|
|
ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl))
|
|
q, k, v = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)))
|
|
|
|
input_mask = None
|
|
if any(map(exists, (mask, context_mask))):
|
|
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
|
|
k_mask = q_mask if not exists(context) else context_mask
|
|
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
|
|
q_mask = rearrange(q_mask, 'b i -> b () i ()')
|
|
k_mask = rearrange(k_mask, 'b j -> b () () j')
|
|
input_mask = q_mask * k_mask
|
|
|
|
if self.num_mem_kv > 0:
|
|
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
|
|
k = torch.cat((mem_k, k), dim=-2)
|
|
v = torch.cat((mem_v, v), dim=-2)
|
|
if exists(input_mask):
|
|
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
|
|
|
|
if collab_heads:
|
|
k = k.expand(-1, h, -1, -1)
|
|
|
|
if self.qk_norm:
|
|
q, k = map(l2norm, (q, k))
|
|
scale = 1 / (self.scale.exp().clamp(min=1e-2))
|
|
|
|
dots = einsum('b h i d, b h j d -> b h i j', q, k) * scale
|
|
mask_value = max_neg_value(dots)
|
|
|
|
if exists(prev_attn):
|
|
dots = dots + prev_attn
|
|
|
|
pre_softmax_attn = dots.clone()
|
|
|
|
if talking_heads:
|
|
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
|
|
|
|
if self.rel_pos_bias:
|
|
dots = self.rel_pos(dots)
|
|
|
|
if exists(input_mask):
|
|
dots.masked_fill_(~input_mask, mask_value)
|
|
del input_mask
|
|
|
|
if exists(attn_mask):
|
|
assert 2 <= attn_mask.ndim <= 4, 'attention mask must have greater than 2 dimensions but less than or equal to 4'
|
|
if attn_mask.ndim == 2:
|
|
attn_mask = rearrange(attn_mask, 'i j -> () () i j')
|
|
elif attn_mask.ndim == 3:
|
|
attn_mask = rearrange(attn_mask, 'h i j -> () h i j')
|
|
dots.masked_fill_(~attn_mask, mask_value)
|
|
|
|
if exists(self.max_attend_past):
|
|
i, j = dots.shape[-2:]
|
|
range_q = torch.arange(j - i, j, device=device)
|
|
range_k = torch.arange(j, device=device)
|
|
dist = rearrange(range_q, 'i -> () () i ()') - rearrange(range_k, 'j -> () () () j')
|
|
mask = dist > self.max_attend_past
|
|
dots.masked_fill_(mask, mask_value)
|
|
del mask
|
|
|
|
if self.causal:
|
|
i, j = dots.shape[-2:]
|
|
r = torch.arange(i, device=device)
|
|
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
|
|
mask = F.pad(mask, (j - i, 0), value=False)
|
|
dots.masked_fill_(mask, mask_value)
|
|
del mask
|
|
|
|
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
|
|
top, _ = dots.topk(self.sparse_topk, dim=-1)
|
|
vk = top[..., -1].unsqueeze(-1).expand_as(dots)
|
|
mask = dots < vk
|
|
dots.masked_fill_(mask, mask_value)
|
|
del mask
|
|
|
|
attn = self.attn_fn(dots, dim=-1)
|
|
post_softmax_attn = attn.clone()
|
|
|
|
attn = self.dropout(attn)
|
|
|
|
if talking_heads:
|
|
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
|
|
|
|
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
|
|
|
if head_scale:
|
|
out = out * self.head_scale_params
|
|
|
|
out = rearrange(out, 'b h n d -> b n (h d)')
|
|
|
|
if exists(self.to_v_gate):
|
|
gates = self.to_v_gate(x)
|
|
out = out * gates.sigmoid()
|
|
|
|
intermediates = Intermediates(
|
|
pre_softmax_attn=pre_softmax_attn,
|
|
post_softmax_attn=post_softmax_attn
|
|
)
|
|
|
|
return self.to_out(out), intermediates, k_cache, v_cache
|
|
|
|
|
|
class AttentionLayers(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
depth,
|
|
heads=8,
|
|
causal=False,
|
|
cross_attend=False,
|
|
only_cross=False,
|
|
use_scalenorm=False,
|
|
use_rms_scaleshift_norm=False,
|
|
use_rmsnorm=False,
|
|
use_rezero=False,
|
|
alibi_pos_bias=False,
|
|
alibi_num_heads=None,
|
|
alibi_learned=False,
|
|
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,
|
|
pre_norm=True,
|
|
gate_residual=False,
|
|
scale_residual=False,
|
|
shift_tokens=0,
|
|
sandwich_norm=False,
|
|
use_qk_norm_attn=False,
|
|
qk_norm_attn_seq_len=None,
|
|
zero_init_branch_output=False,
|
|
**kwargs
|
|
):
|
|
super().__init__()
|
|
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.causal = causal
|
|
|
|
rel_pos_bias = 'rel_pos_bias' in attn_kwargs
|
|
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'
|
|
|
|
if 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
|
|
|
|
assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm'
|
|
self.pre_norm = pre_norm
|
|
self.sandwich_norm = sandwich_norm
|
|
|
|
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_class = RMSScaleShiftNorm if use_rms_scaleshift_norm 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
|
|
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
|
|
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_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)):
|
|
is_last_layer = ind == (len(self.layer_types) - 1)
|
|
|
|
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 = norm_fn() if pre_norm and not layer_uses_qk_norm else None
|
|
post_branch_norm = norm_fn() if sandwich_norm or layer_uses_qk_norm else None
|
|
post_main_norm = norm_fn() if not pre_norm and not is_last_layer else 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,
|
|
context=None,
|
|
full_context=None, # for passing a list of hidden states from an encoder
|
|
mask=None,
|
|
context_mask=None,
|
|
attn_mask=None,
|
|
mems=None,
|
|
return_hiddens=False,
|
|
norm_scale_shift_inp=None,
|
|
past_key_values=None,
|
|
expected_seq_len=None,
|
|
):
|
|
|
|
assert not (self.cross_attend ^ (exists(context) or exists(
|
|
full_context))), 'context must be passed in if cross_attend is set to True'
|
|
assert context is None or full_context is None, 'only one of full_context or context can be provided'
|
|
|
|
hiddens = []
|
|
intermediates = []
|
|
prev_attn = None
|
|
prev_cross_attn = None
|
|
|
|
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
|
|
norm_args = {}
|
|
if exists(norm_scale_shift_inp):
|
|
norm_args['norm_scale_shift_inp'] = norm_scale_shift_inp
|
|
|
|
rotary_pos_emb = None
|
|
if exists(self.rotary_pos_emb):
|
|
if not self.training and self.causal:
|
|
assert expected_seq_len is not None, "To decode a transformer with rotary embeddings, you must specify an `expected_seq_len`"
|
|
elif expected_seq_len is None:
|
|
expected_seq_len = 0
|
|
seq_len = x.shape[1]
|
|
if past_key_values is not None:
|
|
seq_len += past_key_values[0][0].shape[-2]
|
|
max_rotary_emb_length = max(list(map(lambda m: (m.shape[1] if exists(m) else 0) + seq_len, mems)) + [expected_seq_len])
|
|
rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
|
|
|
|
present_key_values = []
|
|
cross_attn_count = 0
|
|
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
|
|
if layer_type == 'a':
|
|
layer_mem = mems.pop(0) if mems else None
|
|
|
|
residual = x
|
|
|
|
pre_branch_norm, post_branch_norm, post_main_norm = norm
|
|
|
|
if exists(pre_branch_norm):
|
|
x = pre_branch_norm(x, **norm_args)
|
|
|
|
if layer_type == 'a' or layer_type == 'c':
|
|
if past_key_values is not None:
|
|
layer_kv = past_key_values.pop(0)
|
|
layer_past = tuple(s.to(x.device) for s in layer_kv)
|
|
else:
|
|
layer_past = None
|
|
|
|
if layer_type == 'a':
|
|
out, inter, k, v = block(x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb,
|
|
prev_attn, layer_mem, layer_past)
|
|
elif layer_type == 'c':
|
|
if exists(full_context):
|
|
out, inter, k, v = block(x, full_context[cross_attn_count], mask, context_mask, None, None,
|
|
None, prev_attn, None, layer_past)
|
|
else:
|
|
out, inter, k, v = block(x, context, mask, context_mask, None, None, None, prev_attn, None, layer_past)
|
|
elif layer_type == 'f':
|
|
out = block(x)
|
|
|
|
if layer_type == 'a' or layer_type == 'c' and present_key_values is not None:
|
|
present_key_values.append((k.detach(), v.detach()))
|
|
|
|
if exists(post_branch_norm):
|
|
out = post_branch_norm(out, **norm_args)
|
|
|
|
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, **norm_args)
|
|
|
|
if layer_type == 'c':
|
|
cross_attn_count += 1
|
|
|
|
if layer_type == 'f':
|
|
hiddens.append(x)
|
|
|
|
if return_hiddens:
|
|
intermediates = LayerIntermediates(
|
|
hiddens=hiddens,
|
|
attn_intermediates=intermediates,
|
|
past_key_values=present_key_values
|
|
)
|
|
|
|
return x, intermediates
|
|
|
|
return x
|
|
|
|
|
|
class Encoder(AttentionLayers):
|
|
def __init__(self, **kwargs):
|
|
assert 'causal' not in kwargs, 'cannot set causality on encoder'
|
|
super().__init__(causal=False, **kwargs)
|
|
|
|
|
|
class Decoder(AttentionLayers):
|
|
def __init__(self, **kwargs):
|
|
assert 'causal' not in kwargs, 'cannot set causality on decoder'
|
|
super().__init__(causal=True, **kwargs)
|
|
|
|
|
|
class CrossAttender(AttentionLayers):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(cross_attend=True, only_cross=True, **kwargs)
|
|
|
|
|
|
class ViTransformerWrapper(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
image_size,
|
|
patch_size,
|
|
attn_layers,
|
|
num_classes=None,
|
|
dropout=0.,
|
|
emb_dropout=0.
|
|
):
|
|
super().__init__()
|
|
assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder'
|
|
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
|
|
dim = attn_layers.dim
|
|
num_patches = (image_size // patch_size) ** 2
|
|
patch_dim = 3 * patch_size ** 2
|
|
|
|
self.patch_size = patch_size
|
|
|
|
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
|
# nn.Linear
|
|
self.patch_to_embedding = ml.Linear(patch_dim, dim)
|
|
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
|
self.dropout = nn.Dropout(emb_dropout)
|
|
|
|
self.attn_layers = attn_layers
|
|
self.norm = nn.LayerNorm(dim)
|
|
self.mlp_head = FeedForward(dim, dim_out=num_classes, dropout=dropout) if exists(num_classes) else None
|
|
|
|
def forward(
|
|
self,
|
|
img,
|
|
return_embeddings=False
|
|
):
|
|
p = self.patch_size
|
|
|
|
x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
|
|
x = self.patch_to_embedding(x)
|
|
b, n, _ = x.shape
|
|
|
|
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
|
|
x = torch.cat((cls_tokens, x), dim=1)
|
|
x = x + self.pos_embedding[:, :(n + 1)]
|
|
x = self.dropout(x)
|
|
|
|
x = self.attn_layers(x)
|
|
x = self.norm(x)
|
|
|
|
if not exists(self.mlp_head) or return_embeddings:
|
|
return x
|
|
|
|
return self.mlp_head(x[:, 0])
|
|
|
|
|
|
class TransformerWrapper(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
num_tokens,
|
|
max_seq_len,
|
|
attn_layers,
|
|
emb_dim=None,
|
|
max_mem_len=0.,
|
|
shift_mem_down=0,
|
|
emb_dropout=0.,
|
|
num_memory_tokens=None,
|
|
tie_embedding=False,
|
|
use_pos_emb=True
|
|
):
|
|
super().__init__()
|
|
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
|
|
|
dim = attn_layers.dim
|
|
emb_dim = default(emb_dim, dim)
|
|
|
|
self.max_seq_len = max_seq_len
|
|
self.max_mem_len = max_mem_len
|
|
self.shift_mem_down = shift_mem_down
|
|
|
|
# nn.Embedding
|
|
self.token_emb = ml.Embedding(num_tokens, emb_dim)
|
|
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
|
|
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
|
|
self.emb_dropout = nn.Dropout(emb_dropout)
|
|
|
|
# nn.Linear
|
|
self.project_emb = ml.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
|
|
self.attn_layers = attn_layers
|
|
self.norm = nn.LayerNorm(dim)
|
|
|
|
self.init_()
|
|
|
|
# nn.Linear
|
|
self.to_logits = ml.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
|
|
|
|
# memory tokens (like [cls]) from Memory Transformers paper
|
|
num_memory_tokens = default(num_memory_tokens, 0)
|
|
self.num_memory_tokens = num_memory_tokens
|
|
if num_memory_tokens > 0:
|
|
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
|
|
|
|
def init_(self):
|
|
nn.init.kaiming_normal_(self.token_emb.weight)
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
return_embeddings=False,
|
|
mask=None,
|
|
return_hiddens=False,
|
|
return_attn=False,
|
|
mems=None,
|
|
use_cache=False,
|
|
**kwargs
|
|
):
|
|
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
|
|
x = self.token_emb(x)
|
|
x = x + self.pos_emb(x)
|
|
x = self.emb_dropout(x)
|
|
|
|
x = self.project_emb(x)
|
|
|
|
if num_mem > 0:
|
|
mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
|
|
x = torch.cat((mem, x), dim=1)
|
|
|
|
# auto-handle masking after appending memory tokens
|
|
if exists(mask):
|
|
mask = F.pad(mask, (num_mem, 0), value=True)
|
|
|
|
if self.shift_mem_down and exists(mems):
|
|
mems_l, mems_r = mems[:self.shift_mem_down], mems[self.shift_mem_down:]
|
|
mems = [*mems_r, *mems_l]
|
|
|
|
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
|
|
x = self.norm(x)
|
|
|
|
mem, x = x[:, :num_mem], x[:, num_mem:]
|
|
|
|
out = self.to_logits(x) if not return_embeddings else x
|
|
|
|
if return_hiddens:
|
|
hiddens = intermediates.hiddens
|
|
return out, hiddens
|
|
|
|
res = [out]
|
|
if return_attn:
|
|
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
|
res.append(attn_maps)
|
|
if use_cache:
|
|
res.append(intermediates.past_key_values)
|
|
|
|
if len(res) > 1:
|
|
return tuple(res)
|
|
return res[0]
|
|
|
|
|
|
class ContinuousTransformerWrapper(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
max_seq_len,
|
|
attn_layers,
|
|
dim_in=None,
|
|
dim_out=None,
|
|
emb_dim=None,
|
|
emb_dropout=0.,
|
|
use_pos_emb=True
|
|
):
|
|
super().__init__()
|
|
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
|
|
|
dim = attn_layers.dim
|
|
|
|
self.max_seq_len = max_seq_len
|
|
|
|
self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len) if (
|
|
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
|
|
self.emb_dropout = nn.Dropout(emb_dropout)
|
|
|
|
# nn.Linear
|
|
self.project_in = ml.Linear(dim_in, dim) if exists(dim_in) else nn.Identity()
|
|
|
|
self.attn_layers = attn_layers
|
|
self.norm = nn.LayerNorm(dim)
|
|
|
|
# nn.Linear
|
|
self.project_out = ml.Linear(dim, dim_out) if exists(dim_out) else nn.Identity()
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
return_embeddings=False,
|
|
mask=None,
|
|
return_attn=False,
|
|
mems=None,
|
|
use_cache=False,
|
|
**kwargs
|
|
):
|
|
b, n, _, device = *x.shape, x.device
|
|
|
|
x = self.project_in(x)
|
|
x = x + self.pos_emb(x)
|
|
x = self.emb_dropout(x)
|
|
|
|
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
|
|
x = self.norm(x)
|
|
|
|
out = self.project_out(x) if not return_embeddings else x
|
|
|
|
res = [out]
|
|
if return_attn:
|
|
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
|
res.append(attn_maps)
|
|
if use_cache:
|
|
res.append(intermediates.past_key_values)
|
|
|
|
if len(res) > 1:
|
|
return tuple(res)
|
|
return res[0]
|
|
|