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#===================================================================================================================
#
# X Trasformer Module
#
# Partial x-transformers code With useful modifications
#
# Version 1.0
#
# Original source code courtesy of lucidrains
# https://github.com/lucidrains/x-transformers
#
# Original source code retrieved on 10/10/2023
#
# Project Los Angeles
# Tegridy Code 2023
#===================================================================================================================
# Critical dependencies
#
# !pip install torch
# !pip install einops
#===================================================================================================================
from functools import partial
from typing import Optional, Tuple
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
# from torch.nn.attention import SDPBackend, sdpa_kernel
from collections import namedtuple
from functools import wraps
from packaging import version
from dataclasses import dataclass
from einops import rearrange, repeat
# constants
EfficientAttentionConfig = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
@dataclass
class Intermediates:
qk_similarities: Optional[Tensor] = None
pre_softmax_attn: Optional[Tensor] = None
post_softmax_attn: Optional[Tensor] = None
cached_kv: Optional[Tuple[Tensor, Tensor]] = None
def to_tuple(self):
return (self.qk_similarities, self.pre_softmax_attn, self.post_softmax_attn)
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def compact(arr):
return [*filter(exists, arr)]
def once(fn):
called = False
@wraps(fn)
def inner(x):
nonlocal called
if called:
return
called = True
return fn(x)
return inner
print_once = once(print)
# functions for creating causal mask
# need a special one for onnx cpu (no support for .triu)
def create_causal_mask(i, j, device):
return torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
def onnx_create_causal_mask(i, j, device):
r = torch.arange(i, device = device)
causal_mask = rearrange(r, 'i -> i 1') < rearrange(r, 'j -> 1 j')
causal_mask = F.pad(causal_mask, (j - i, 0), value = False)
return causal_mask
# main class
class Attend(nn.Module):
def __init__(
self,
*,
dropout = 0.,
causal = False,
heads = None,
talking_heads = False,
sparse_topk = None,
scale = None,
qk_norm = False,
flash = False,
add_zero_kv = False,
onnxable = False
):
super().__init__()
self.scale = scale
self.qk_norm = qk_norm
self.causal = causal
self.create_causal_mask = onnx_create_causal_mask if onnxable else create_causal_mask
self.attn_fn = partial(F.softmax, dtype = torch.float32) if not qk_norm else F.softmax
self.dropout = dropout
self.attn_dropout = nn.Dropout(dropout)
# talking heads
assert not (flash and talking_heads), 'talking heads not compatible with flash attention'
self.talking_heads = talking_heads
if talking_heads:
self.pre_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
self.post_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
# sparse topk
assert not (flash and sparse_topk), 'sparse topk not compatible with flash attention'
self.sparse_topk = sparse_topk
# add a key / value token composed of zeros
# in case this helps controlling outliers, proposed by https://www.evanmiller.org/attention-is-off-by-one.html
self.add_zero_kv = add_zero_kv
# flash attention
self.flash = flash
assert not (flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
# determine efficient attention configs for cuda and cpu
self.cpu_config = EfficientAttentionConfig(True, True, True)
self.cuda_config = None
if not torch.cuda.is_available() or not flash:
return
device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
major, minor = device_properties.major, device_properties.minor
if (major, minor) == (8, 0):
print_once('A100 GPU detected, using flash attention if input tensor is on cuda')
self.cuda_config = EfficientAttentionConfig(True, False, False)
elif (major, minor) == (9, 0):
print_once('H100 GPU detected, using flash attention')
self.cuda_config = EfficientAttentionConfig(True, False, False)
else:
print_once('Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda')
self.cuda_config = EfficientAttentionConfig(False, True, True)
def flash_attn(
self,
q, k, v,
mask = None,
attn_bias = None
):
batch, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device
# Recommended for multi-query single-key-value attention by Tri Dao
# kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
if k.ndim == 3:
k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
if v.ndim == 3:
v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
# handle scale - by default they scale by dim_head ** -0.5, but need to take care if using cosine sim attention
if self.qk_norm:
default_scale = q.shape[-1] ** -0.5
q = q * (self.scale / default_scale)
# Check if mask exists and expand to compatible shape
# The mask is B L, so it would have to be expanded to B H N L
causal = self.causal
# in the case of kv caching with one token (q_len == 1), just turn off causal masking
# in speculative decoding, this may go up to 5-6, so right aligned causal mask will be needed there
if q_len == 1 and causal:
causal = False
# expand key padding mask
if exists(mask):
assert mask.ndim == 4
mask = mask.expand(batch, heads, q_len, k_len)
# handle kv cache - this should be bypassable in updated flash attention 2
if k_len > q_len and causal:
causal_mask = self.create_causal_mask(q_len, k_len, device = device)
if not exists(mask):
mask = ~causal_mask
else:
mask = mask & ~causal_mask
causal = False
# manually handle causal mask, if another mask was given
row_is_entirely_masked = None
if exists(mask) and causal:
causal_mask = self.create_causal_mask(q_len, k_len, device = device)
mask = mask & ~causal_mask
# protect against an entire row being masked out
row_is_entirely_masked = ~mask.any(dim = -1)
mask[..., 0] = mask[..., 0] | row_is_entirely_masked
causal = False
# handle alibi positional bias
# convert from bool to float
if exists(attn_bias):
attn_bias = rearrange(attn_bias, 'h i j -> 1 h i j').expand(batch, heads, -1, -1)
# if mask given, the mask would already contain the causal mask from above logic
# otherwise, if no mask given but still causal, mask out alibi positional bias to a large negative number
mask_value = -torch.finfo(q.dtype).max
if exists(mask):
attn_bias = attn_bias.masked_fill(~mask, mask_value // 2)
elif causal:
causal_mask = self.create_causal_mask(q_len, k_len, device = device)
attn_bias = attn_bias.masked_fill(causal_mask, mask_value // 2)
causal = False
# scaled_dot_product_attention handles attn_mask either as bool or additive bias
# make it an additive bias here
mask = attn_bias
# Check if there is a compatible device for flash attention
config = self.cuda_config if is_cuda else self.cpu_config
# pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
# Legacy code...
with torch.backends.cuda.sdp_kernel(enable_math=True, enable_mem_efficient=True):
# New SDP kernel code...
# with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
out = F.scaled_dot_product_attention(
q, k, v,
attn_mask = mask,
dropout_p = self.dropout if self.training else 0.,
is_causal = causal
)
# for a row that is entirely masked out, should zero out the output of that row token
if exists(row_is_entirely_masked):
out = out.masked_fill(row_is_entirely_masked[..., None], 0.)
return out, Intermediates()
def forward(
self,
q, k, v,
mask = None,
attn_bias = None,
prev_attn = None
):
"""
einstein notation
b - batch
h - heads
n, i, j - sequence length (base sequence length, source, target)
d - feature dimension
"""
n, heads, kv_heads, device = q.shape[-2], q.shape[1], k.shape[1], q.device
scale = default(self.scale, q.shape[-1] ** -0.5)
causal = self.causal
# handle kv cached decoding
if n == 1 and causal:
causal = False
# handle grouped multi-query attention
if kv_heads == 1:
k, v = map(lambda t: rearrange(t, 'b 1 n d -> b n d'), (k, v))
elif kv_heads < heads:
k, v = map(lambda t: repeat(t, 'b kvh n d -> b (r kvh) n d', r = heads // kv_heads), (k, v))
# handle zero kv, as means for allowing network to attend to nothing
if self.add_zero_kv:
k, v = map(lambda t: F.pad(t, (0, 0, 1, 0), value = 0.), (k, v))
if exists(mask):
mask = F.pad(mask, (1, 0), value = True)
if exists(attn_bias):
attn_bias = F.pad(attn_bias, (1, 0), value = 0.)
if self.flash:
assert not exists(prev_attn), 'residual attention not compatible with flash attention'
return self.flash_attn(q, k, v, mask = mask, attn_bias = attn_bias)
kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
if exists(prev_attn):
dots = dots + prev_attn
qk_similarities = dots.clone()
if self.talking_heads:
dots = self.pre_softmax_talking_heads(dots)
if exists(attn_bias):
dots = dots + attn_bias
i, j, dtype = *dots.shape[-2:], dots.dtype
mask_value = -torch.finfo(dots.dtype).max
if exists(self.sparse_topk) and self.sparse_topk < j:
top_values, _ = dots.topk(self.sparse_topk, dim = -1)
sparse_topk_mask = dots < top_values[..., -1:]
mask = (mask & sparse_topk_mask) if exists(mask) else sparse_topk_mask
if exists(mask):
dots = dots.masked_fill(~mask, mask_value)
if causal:
causal_mask = self.create_causal_mask(i, j, device = device)
dots = dots.masked_fill(causal_mask, mask_value)
pre_softmax_attn = dots.clone()
attn = self.attn_fn(dots, dim = -1)
attn = attn.type(dtype)
post_softmax_attn = attn.clone()
attn = self.attn_dropout(attn)
if self.talking_heads:
attn = self.post_softmax_talking_heads(attn)
out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
intermediates = Intermediates(
qk_similarities = qk_similarities,
pre_softmax_attn = pre_softmax_attn,
post_softmax_attn = post_softmax_attn
)
return out, intermediates
#===================================================================================================================
from math import ceil, log
from typing import Optional, Union, Tuple, Callable
import torch
from torch import nn, Tensor
from torch.nn import Module
import torch.nn.functional as F
from einops import rearrange, pack, unpack
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def identity(t, *args, **kwargs):
return t
def cast_tuple(t, length = 1):
return t if isinstance(t, tuple) else (t,) * length
def eval_decorator(fn):
def inner(self, *args, **kwargs):
was_training = self.training
self.eval()
out = fn(self, *args, **kwargs)
self.train(was_training)
return out
return inner
# for variable lengthed prefixes
def align_right(t, lens, pad_id = 0):
batch, seq_len, device, dtype = *t.shape, t.device, t.dtype
assert lens.ndim == 1 and lens.shape[0] == batch
assert lens.amax() <= seq_len
pad_lens = seq_len - lens
max_pad_len = pad_lens.amax()
batch_arange = torch.arange(batch, device = device, dtype = torch.long)[..., None]
prompt_len_arange = torch.arange(seq_len, device = device, dtype = torch.long)
t = F.pad(t, (max_pad_len, 0), value = 0)
offset = max_pad_len - pad_lens
aligned = t[batch_arange, prompt_len_arange + offset[..., None]]
return aligned
# nucleus
def top_p(logits, thres = 0.9):
sorted_logits, sorted_indices = torch.sort(logits, descending = True)
cum_probs = torch.cumsum(F.softmax(sorted_logits, dim = -1), dim = -1)
sorted_indices_to_remove = cum_probs > thres
sorted_indices_to_remove = F.pad(sorted_indices_to_remove, (1, -1), value = False)
sorted_logits[sorted_indices_to_remove] = float('-inf')
return sorted_logits.scatter(1, sorted_indices, sorted_logits)
# topk
def top_k(logits, frac_num_tokens = 0.1, k = None):
num_tokens = logits.shape[-1]
k = default(k, ceil(frac_num_tokens * num_tokens))
k = min(k, num_tokens)
val, ind = torch.topk(logits, k)
probs = torch.full_like(logits, float('-inf'))
probs.scatter_(1, ind, val)
return probs
# top_a
def top_a(logits, min_p_pow = 2.0, min_p_ratio = 0.02):
probs = F.softmax(logits, dim = -1)
max_probs = torch.amax(probs, dim = -1, keepdim = True)
limit = torch.pow(max_probs, min_p_pow) * min_p_ratio
return torch.where(probs < limit, float('-inf'), logits)
# contrastive decoding function
def contrastive_decode_fn(
expert_logits,
amateur_logits,
alpha = 0.1,
beta = 0.5
):
"""
Appendix A Algorithm 2
https://arxiv.org/abs/2309.09117
"""
cutoff = log(alpha) + expert_logits.amax(dim = -1, keepdim = True)
diffs = (1 + beta) * expert_logits - beta * amateur_logits
contrastive_decode_logits = diffs.masked_fill(expert_logits < cutoff, -torch.finfo(expert_logits.dtype).max)
return contrastive_decode_logits
# autoregressive wrapper class
class AutoregressiveWrapper(Module):
def __init__(
self,
net,
ignore_index = -100,
pad_value = 0,
mask_prob = 0.,
add_attn_z_loss = False
):
super().__init__()
self.pad_value = pad_value
self.ignore_index = ignore_index
self.net = net
self.max_seq_len = net.max_seq_len
# paper shows masking (MLM) in conjunction with autoregressive decoder-only training leads to big improvements https://arxiv.org/abs/2210.13432
assert mask_prob < 1.
self.mask_prob = mask_prob
# whether to add router z-loss
self.add_attn_z_loss = add_attn_z_loss
@torch.no_grad()
@eval_decorator
def generate(
self,
prompts,
seq_len,
eos_token = None,
temperature = 1.,
prompt_lens: Optional[Tensor] = None,
filter_logits_fn: Callable = top_k,
restrict_to_max_seq_len = True,
amateur_model: Optional[Union[Module, Tuple[Module]]] = None,
filter_kwargs: dict = dict(),
contrastive_decode_kwargs: Union[dict, Tuple[dict]] = dict(
beta = 0.5,
alpha = 0.1
),
cache_kv = True,
verbose=True,
return_prime=False,
**kwargs
):
max_seq_len, device = self.max_seq_len, prompts.device
prompts, ps = pack([prompts], '* n')
b, t = prompts.shape
# handle variable lengthed prompts (prefixes)
seq_start_pos = None
if exists(prompt_lens):
prompts = align_right(prompts, prompt_lens, pad_id = self.pad_value)
seq_start_pos = t - prompt_lens
# output from which sampled tokens appended to
out = prompts
if verbose:
print("Generating sequence of max length:", seq_len)
# kv caches
cache = None
# if doing contrastive decoding, turn off filter automatically
if exists(amateur_model):
amateur_model = cast_tuple(amateur_model)
contrastive_decode_kwargs = cast_tuple(contrastive_decode_kwargs)
assert len(amateur_model) == len(contrastive_decode_kwargs)
amateur_caches = [None] * len(amateur_model)
filter_logits_fn = identity
for i, module in enumerate(amateur_model):
if isinstance(module, AutoregressiveWrapper):
amateur_model[i] = module.net
module.eval()
# sampling up to seq_len
for sl in range(seq_len):
if restrict_to_max_seq_len:
x = out[:, -max_seq_len:]
if exists(cache):
for inter in cache.attn_intermediates:
inter.cached_kv = [t[..., -(max_seq_len - 1):, :] for t in inter.cached_kv]
logits, new_cache = self.net(
x,
return_intermediates = True,
cache = cache,
seq_start_pos = seq_start_pos,
**kwargs
)
if cache_kv and self.net.can_cache_kv:
cache = new_cache
logits = logits[:, -1]
# handle contrastive decoding, Li et al.
# https://arxiv.org/abs/2210.15097
if exists(amateur_model):
for i, (amateur, amateur_cache, amateur_contrastive_decode_kwargs) in enumerate(zip(amateur_model, amateur_caches, contrastive_decode_kwargs)):
amateur_logits, next_amateur_cache = amateur(
x,
return_intermediates = True,
cache = amateur_cache,
seq_start_pos = seq_start_pos,
**kwargs
)
amateur_logits = amateur_logits[:, -1]
assert amateur_logits.shape == logits.shape, 'logits dimension are not the same between amateur and expert model'
logits = contrastive_decode_fn(logits, amateur_logits, **amateur_contrastive_decode_kwargs)
if cache_kv and amateur.can_cache_kv:
amateur_caches[i] = next_amateur_cache
# filter by top_k, top_p (nucleus), top_a, or custom
filtered_logits = filter_logits_fn(logits, **filter_kwargs)
probs = F.softmax(filtered_logits / temperature, dim=-1)
sample = torch.multinomial(probs, 1)
out = torch.cat((out, sample), dim=-1)
if verbose:
if sl % 32 == 0:
print(sl, '/', seq_len)
if exists(eos_token):
is_eos_tokens = (out == eos_token)
if is_eos_tokens.any(dim = -1).all():
# mask out everything after the eos tokens
shifted_is_eos_tokens = F.pad(is_eos_tokens, (1, -1))
mask = shifted_is_eos_tokens.float().cumsum(dim = -1) >= 1
out = out.masked_fill(mask, self.pad_value)
if verbose:
print('Model called the end of sequence at:', sl, '/', seq_len)
break
if return_prime:
return out[:, :]
else:
return out[:, t:]
# out, = unpack(out, ps, '* n')
# return out
def compute_accuracy(self, logits, labels):
out = torch.argmax(logits, dim=-1)
out = out.flatten()
labels = labels.flatten()
mask = (labels != self.ignore_index) # can also be self.pad_value (your choice)
out = out[mask]
labels = labels[mask]
num_right = (out == labels)
num_right = torch.sum(num_right).type(torch.float32)
acc = num_right / len(labels)
return acc
def forward(self, x, **kwargs):
seq, ignore_index, add_attn_z_loss = x.shape[1], self.ignore_index, self.add_attn_z_loss
inp, target = x[:, :-1], x[:, 1:]
inp = torch.where(inp == ignore_index, self.pad_value, inp)
if self.mask_prob > 0.:
rand = torch.randn(inp.shape, device = x.device)
rand[:, 0] = -torch.finfo(rand.dtype).max # first token should not be masked out
num_mask = min(int(seq * self.mask_prob), seq - 1)
indices = rand.topk(num_mask, dim = -1).indices
mask = ~torch.zeros_like(inp).scatter(1, indices, 1.).bool()
kwargs.update(self_attn_kv_mask = mask)
logits, cache = self.net(
inp,
return_intermediates = True,
return_attn_z_loss = add_attn_z_loss,
**kwargs
)
acc = self.compute_accuracy(logits, target)
loss = F.cross_entropy(
rearrange(logits, 'b n c -> b c n'),
target,
ignore_index = ignore_index
)
if add_attn_z_loss:
loss = loss + cache.attn_z_loss
return loss, acc
#===============================================================================
import math
from random import random
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from functools import partial, wraps
from inspect import isfunction
from collections import namedtuple
from dataclasses import dataclass
from typing import List, Callable, Optional
from einops import rearrange, repeat, reduce, pack, unpack
from einops.layers.torch import Rearrange
# constants
DEFAULT_DIM_HEAD = 64
@dataclass
class LayerIntermediates:
hiddens: Optional[List[Tensor]] = None
attn_intermediates: Optional[List[Intermediates]] = None
layer_hiddens: Optional[List[Tensor]] = None
attn_z_loss: Optional[Tensor] = None
mems: Optional[Tensor] = None
# helpers
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def cast_tuple(val, depth):
return val if isinstance(val, tuple) else (val,) * depth
def divisible_by(num, den):
return (num % den) == 0
def maybe(fn):
@wraps(fn)
def inner(x, *args, **kwargs):
if not exists(x):
return x
return fn(x, *args, **kwargs)
return inner
class always():
def __init__(self, val):
self.val = val
def __call__(self, *args, **kwargs):
return self.val
class not_equals():
def __init__(self, val):
self.val = val
def __call__(self, x, *args, **kwargs):
return x != self.val
class equals():
def __init__(self, val):
self.val = val
def __call__(self, x, *args, **kwargs):
return x == self.val
def Sequential(*modules):
return nn.Sequential(*filter(exists, modules))
# tensor helpers
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def l2norm(t, groups = 1):
t = rearrange(t, '... (g d) -> ... g d', g = groups)
t = F.normalize(t, p = 2, dim = -1)
return rearrange(t, '... g d -> ... (g d)')
def pad_at_dim(t, pad, dim = -1, value = 0.):
dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1)
zeros = ((0, 0) * dims_from_right)
return F.pad(t, (*zeros, *pad), value = value)
def or_reduce(masks):
head, *body = masks
for rest in body:
head = head | rest
return head
# auxiliary loss helpers
def calc_z_loss(
pre_softmax_attns: List[Tensor],
mask = None,
weight = 1.
):
# the same loss applied to the mixture of experts router logits in https://arxiv.org/abs/2202.08906
# in the paper, in a tiny footnote, they mention using it on attention logits with stabilizing effects
# also used in PaLM as one of the measures
lse = 0.
for attn in pre_softmax_attns:
lse = lse + attn.logsumexp(dim = -1)
loss = torch.square(lse)
loss = reduce(loss, 'b h n -> b n', 'sum')
if not exists(mask):
return loss.mean() * weight
loss = loss[mask].sum() / mask.sum().clamp(min = 1e-5)
return loss * weight
# init helpers
def init_zero_(layer):
nn.init.constant_(layer.weight, 0.)
if exists(layer.bias):
nn.init.constant_(layer.bias, 0.)
# keyword argument helpers
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
def group_dict_by_key(cond, d):
return_val = [dict(),dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# structured dropout, more effective than traditional attention dropouts
def dropout_seq(seq, mask, dropout):
b, n, *_, device = *seq.shape, seq.device
logits = torch.randn(b, n, device = device)
if exists(mask):
mask_value = max_neg_value(logits)
logits = logits.masked_fill(~mask, mask_value)
keep_prob = 1. - dropout
num_keep = max(1, int(keep_prob * n))
keep_indices = logits.topk(num_keep, dim = 1).indices
batch_indices = torch.arange(b, device = device)
batch_indices = rearrange(batch_indices, 'b -> b 1')
seq = seq[batch_indices, keep_indices]
if exists(mask):
seq_counts = mask.sum(dim = -1)
seq_keep_counts = torch.ceil(seq_counts * keep_prob).int()
keep_mask = torch.arange(num_keep, device = device) < rearrange(seq_keep_counts, 'b -> b 1')
mask = mask[batch_indices, keep_indices] & keep_mask
return seq, mask
# activations
class ReluSquared(nn.Module):
def forward(self, x):
return F.relu(x) ** 2
# embedding
class TokenEmbedding(nn.Module):
def __init__(self, dim, num_tokens, l2norm_embed = False):
super().__init__()
self.l2norm_embed = l2norm_embed
self.emb = nn.Embedding(num_tokens, dim)
def forward(self, x):
token_emb = self.emb(x)
return l2norm(token_emb) if self.l2norm_embed else token_emb
# positional embeddings
class AbsolutePositionalEmbedding(nn.Module):
def __init__(self, dim, max_seq_len, l2norm_embed = False):
super().__init__()
self.scale = dim ** -0.5 if not l2norm_embed else 1.
self.max_seq_len = max_seq_len
self.l2norm_embed = l2norm_embed
self.emb = nn.Embedding(max_seq_len, dim)
def forward(self, x, pos = None, seq_start_pos = None):
seq_len, device = x.shape[1], x.device
assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
if not exists(pos):
pos = torch.arange(seq_len, device = device)
if exists(seq_start_pos):
pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
pos_emb = self.emb(pos)
pos_emb = pos_emb * self.scale
return l2norm(pos_emb) if self.l2norm_embed else pos_emb
class ScaledSinusoidalEmbedding(nn.Module):
def __init__(self, dim, theta = 10000):
super().__init__()
assert divisible_by(dim, 2)
self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
half_dim = dim // 2
freq_seq = torch.arange(half_dim).float() / half_dim
inv_freq = theta ** -freq_seq
self.register_buffer('inv_freq', inv_freq, persistent = False)
def forward(self, x, pos = None, seq_start_pos = None):
seq_len, device = x.shape[1], x.device
if not exists(pos):
pos = torch.arange(seq_len, device = device)
if exists(seq_start_pos):
pos = pos - seq_start_pos[..., None]
emb = einsum('i, j -> i j', pos, self.inv_freq)
emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
return emb * self.scale
class RelativePositionBias(nn.Module):
def __init__(self, scale, causal = False, num_buckets = 32, max_distance = 128, heads = 8):
super().__init__()
self.scale = scale
self.causal = causal
self.num_buckets = num_buckets
self.max_distance = max_distance
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
@staticmethod
def _relative_position_bucket(relative_position, causal = True, num_buckets = 32, max_distance = 128):
ret = 0
n = -relative_position
if not causal:
num_buckets //= 2
ret += (n < 0).long() * num_buckets
n = torch.abs(n)
else:
n = torch.max(n, torch.zeros_like(n))
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).long()
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
@property
def device(self):
return next(self.parameters()).device
def forward(self, i, j):
device = self.device
q_pos = torch.arange(j - i, j, dtype = torch.long, device = device)
k_pos = torch.arange(j, dtype = torch.long, device = device)
rel_pos = k_pos[None, :] - q_pos[:, None]
rp_bucket = self._relative_position_bucket(rel_pos, causal = self.causal, num_buckets = self.num_buckets, max_distance = self.max_distance)
values = self.relative_attention_bias(rp_bucket)
bias = rearrange(values, 'i j h -> h i j')
return bias * self.scale
class DynamicPositionBias(nn.Module):
def __init__(self, dim, *, heads, depth, log_distance = False, norm = False):
super().__init__()
assert depth >= 1, 'depth for dynamic position bias MLP must be greater or equal to 1'
self.log_distance = log_distance
self.mlp = nn.ModuleList([])
self.mlp.append(Sequential(
nn.Linear(1, dim),
nn.LayerNorm(dim) if norm else None,
nn.SiLU()
))
for _ in range(depth - 1):
self.mlp.append(Sequential(
nn.Linear(dim, dim),
nn.LayerNorm(dim) if norm else None,
nn.SiLU()
))
self.mlp.append(nn.Linear(dim, heads))
@property
def device(self):
return next(self.parameters()).device
def forward(self, i, j):
assert i == j
n, device = j, self.device
# get the (n x n) matrix of distances
seq_arange = torch.arange(n, device = device)
context_arange = torch.arange(n, device = device)
indices = rearrange(seq_arange, 'i -> i 1') - rearrange(context_arange, 'j -> 1 j')
indices += (n - 1)
# input to continuous positions MLP
pos = torch.arange(-n + 1, n, device = device).float()
pos = rearrange(pos, '... -> ... 1')
if self.log_distance:
pos = torch.sign(pos) * torch.log(pos.abs() + 1) # log of distance is sign(rel_pos) * log(abs(rel_pos) + 1)
for layer in self.mlp:
pos = layer(pos)
# get position biases
bias = pos[indices]
bias = rearrange(bias, 'i j h -> h i j')
return bias
class AlibiPositionalBias(nn.Module):
def __init__(self, heads, total_heads, **kwargs):
super().__init__()
self.heads = heads
self.total_heads = total_heads
slopes = Tensor(self._get_slopes(heads))
slopes = rearrange(slopes, 'h -> h 1 1')
self.register_buffer('slopes', slopes, persistent = False)
self.register_buffer('bias', None, persistent = False)
def get_bias(self, i, j, device):
i_arange = torch.arange(j - i, j, device = device)
j_arange = torch.arange(j, device = device)
bias = -torch.abs(rearrange(j_arange, 'j -> 1 1 j') - rearrange(i_arange, 'i -> 1 i 1'))
return bias
@staticmethod
def _get_slopes(heads):
def get_slopes_power_of_2(n):
start = (2**(-2**-(math.log2(n)-3)))
ratio = start
return [start*ratio**i for i in range(n)]
if math.log2(heads).is_integer():
return get_slopes_power_of_2(heads)
closest_power_of_2 = 2 ** math.floor(math.log2(heads))
return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][:heads-closest_power_of_2]
@property
def device(self):
return next(self.buffers()).device
def forward(self, i, j):
h, device = self.total_heads, self.device
if exists(self.bias) and self.bias.shape[-1] >= j and self.bias.shape[-2] >= i:
return self.bias[..., -i:, -j:]
bias = self.get_bias(i, j, device)
bias = bias * self.slopes
num_heads_unalibied = h - bias.shape[0]
bias = pad_at_dim(bias, (0, num_heads_unalibied), dim = 0)
self.register_buffer('bias', bias, persistent = False)
return self.bias
class RotaryEmbedding(nn.Module):
def __init__(
self,
dim,
use_xpos = False,
scale_base = 512,
interpolation_factor = 1.,
base = 10000,
base_rescale_factor = 1.
):
super().__init__()
# proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
# has some connection to NTK literature
# https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
base *= base_rescale_factor ** (dim / (dim - 2))
inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
assert interpolation_factor >= 1.
self.interpolation_factor = interpolation_factor
if not use_xpos:
self.register_buffer('scale', None)
return
scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
self.scale_base = scale_base
self.register_buffer('scale', scale)
def forward(self, seq_len):
device = self.inv_freq.device
t = torch.arange(seq_len, device = device).type_as(self.inv_freq)
t = t / self.interpolation_factor
freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
freqs = torch.cat((freqs, freqs), dim = -1)
if not exists(self.scale):
return freqs, 1.
power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
scale = self.scale ** rearrange(power, 'n -> n 1')
scale = torch.cat((scale, scale), dim = -1)
return freqs, scale
def rotate_half(x):
x = rearrange(x, '... (j d) -> ... j d', j = 2)
x1, x2 = x.unbind(dim = -2)
return torch.cat((-x2, x1), dim = -1)
def apply_rotary_pos_emb(t, freqs, scale = 1):
rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
freqs = freqs[-seq_len:, :]
if t.ndim == 4 and freqs.ndim == 3:
freqs = rearrange(freqs, 'b n d -> b 1 n d')
# partial rotary embeddings, Wang et al. GPT-J
t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
return torch.cat((t, t_unrotated), dim = -1)
# norms
class Scale(nn.Module):
def __init__(self, value, fn):
super().__init__()
self.value = value
self.fn = fn
def forward(self, x, **kwargs):
out = self.fn(x, **kwargs)
scale_fn = lambda t: t * self.value
if not isinstance(out, tuple):
return scale_fn(out)
return (scale_fn(out[0]), *out[1:])
class ScaleNorm(nn.Module):
def __init__(self, dim, eps = 1e-5):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1) * (dim ** -0.5))
def forward(self, x):
norm = torch.norm(x, dim = -1, keepdim = True)
return x / norm.clamp(min = self.eps) * self.g
class RMSNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.scale = dim ** 0.5
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
return F.normalize(x, dim = -1) * self.scale * self.g
class SimpleRMSNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.scale = dim ** 0.5
def forward(self, x):
return F.normalize(x, dim = -1) * self.scale
# residual and residual gates
class Residual(nn.Module):
def __init__(self, dim, scale_residual = False, scale_residual_constant = 1.):
super().__init__()
self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
self.scale_residual_constant = scale_residual_constant
def forward(self, x, residual):
if exists(self.residual_scale):
residual = residual * self.residual_scale
if self.scale_residual_constant != 1:
residual = residual * self.scale_residual_constant
return x + residual
class GRUGating(nn.Module):
def __init__(self, dim, scale_residual = False, **kwargs):
super().__init__()
self.gru = nn.GRUCell(dim, dim)
self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
def forward(self, x, residual):
if exists(self.residual_scale):
residual = residual * self.residual_scale
gated_output = self.gru(
rearrange(x, 'b n d -> (b n) d'),
rearrange(residual, 'b n d -> (b n) d')
)
return gated_output.reshape_as(x)
# token shifting
def shift(t, amount, mask = None):
if amount == 0:
return t
else:
amount = min(amount, t.shape[1])
if exists(mask):
t = t.masked_fill(~mask[..., None], 0.)
return pad_at_dim(t, (amount, -amount), dim = - 2, value = 0.)
class ShiftTokens(nn.Module):
def __init__(self, shifts, fn):
super().__init__()
self.fn = fn
self.shifts = tuple(shifts)
def forward(self, x, **kwargs):
mask = kwargs.get('mask', None)
shifts = self.shifts
segments = len(shifts)
feats_per_shift = x.shape[-1] // segments
splitted = x.split(feats_per_shift, dim = -1)
segments_to_shift, rest = splitted[:segments], splitted[segments:]
segments_to_shift = list(map(lambda args: shift(*args, mask = mask), zip(segments_to_shift, shifts)))
x = torch.cat((*segments_to_shift, *rest), dim = -1)
return self.fn(x, **kwargs)
# feedforward
class GLU(nn.Module):
def __init__(
self,
dim_in,
dim_out,
activation: Callable,
mult_bias = False
):
super().__init__()
self.act = activation
self.proj = nn.Linear(dim_in, dim_out * 2)
self.mult_bias = nn.Parameter(torch.ones(dim_out)) if mult_bias else 1.
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim = -1)
return x * self.act(gate) * self.mult_bias
class FeedForward(nn.Module):
def __init__(
self,
dim,
dim_out = None,
mult = 4,
glu = False,
glu_mult_bias = False,
swish = False,
relu_squared = False,
post_act_ln = False,
dropout = 0.,
no_bias = False,
zero_init_output = False
):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
if relu_squared:
activation = ReluSquared()
elif swish:
activation = nn.SiLU()
else:
activation = nn.GELU()
if glu:
project_in = GLU(dim, inner_dim, activation, mult_bias = glu_mult_bias)
else:
project_in = nn.Sequential(
nn.Linear(dim, inner_dim, bias = not no_bias),
activation
)
self.ff = Sequential(
project_in,
nn.LayerNorm(inner_dim) if post_act_ln else None,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out, bias = not no_bias)
)
# init last linear layer to 0
if zero_init_output:
init_zero_(self.ff[-1])
def forward(self, x):
return self.ff(x)
# attention. it is all we need
class Attention(nn.Module):
def __init__(
self,
dim,
dim_head = DEFAULT_DIM_HEAD,
heads = 8,
causal = False,
flash = False,
talking_heads = False,
head_scale = False,
sparse_topk = None,
num_mem_kv = 0,
dropout = 0.,
on_attn = False,
gate_value_heads = False,
gate_values = False,
zero_init_output = False,
max_attend_past = None,
qk_norm = False,
qk_norm_groups = 1,
qk_norm_scale = 10,
qk_norm_dim_scale = False,
one_kv_head = False,
kv_heads = None,
shared_kv = False,
value_dim_head = None,
tensor_product = False, # https://arxiv.org/abs/2208.06061
add_zero_kv = False, # same as add_zero_attn in pytorch
rotary_embed_values = False,
onnxable = False
):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
self.causal = causal
self.max_attend_past = max_attend_past
assert not (exists(kv_heads) and one_kv_head), 'either attn_one_kv_head is set to True (in which case kv_heads is set to 1), or attn_kv_heads is set, but not both'
value_dim_head = default(value_dim_head, dim_head)
kv_heads = default(kv_heads, heads)
kv_heads = 1 if one_kv_head else kv_heads
assert divisible_by(heads, kv_heads)
self.kv_heads = kv_heads
q_dim = dim_head * heads
k_dim = dim_head * kv_heads
v_dim = value_dim_head * kv_heads
out_dim = value_dim_head * heads
self.to_q = nn.Linear(dim, q_dim, bias = False)
self.to_k = nn.Linear(dim, k_dim, bias = False)
# shared key / values, for further memory savings during inference
assert not (shared_kv and value_dim_head != dim_head), 'key and value head dimensions must be equal for shared key / values'
self.to_v = nn.Linear(dim, v_dim, bias = False) if not shared_kv else None
# relations projection from tp-attention
self.to_r = nn.Linear(dim, v_dim, bias = False) if tensor_product else None
# add GLU gating for aggregated values, from alphafold2
self.to_v_gate = None
if gate_values:
self.to_v_gate = nn.Linear(dim, out_dim)
nn.init.constant_(self.to_v_gate.weight, 0)
nn.init.constant_(self.to_v_gate.bias, 10)
# add per head gating of the output values, from 'Attend to nothing' paper
self.to_v_head_gate = None
if gate_value_heads:
self.to_v_head_gate = nn.Linear(dim, heads)
nn.init.constant_(self.to_v_head_gate.weight, 0)
nn.init.constant_(self.to_v_head_gate.bias, 10)
# cosine sim attention
self.qk_norm = qk_norm
self.qk_norm_groups = qk_norm_groups
self.qk_norm_scale = qk_norm_scale
# whether to use the rmsnorm (equivalent to cosine sim attention when scale is equal to 1) - https://arxiv.org/abs/2302.05442
self.qk_norm_dim_scale = qk_norm_dim_scale
self.qk_norm_q_scale = self.qk_norm_k_scale = 1
if qk_norm and qk_norm_dim_scale:
self.qk_norm_q_scale = nn.Parameter(torch.ones(heads, 1, dim_head))
self.qk_norm_k_scale = nn.Parameter(torch.ones(heads, 1, dim_head))
assert (not qk_norm) or divisible_by(dim_head, qk_norm_groups), 'dimension per attention head must be divisible by the qk norm groups'
assert not (qk_norm and (dim_head // qk_norm_groups) <= 2), 'the group dimension may be too small (2 was too small in my tests, but 4 still works, surprisingly)'
# attend class - includes core attention algorithm + talking heads
self.attend = Attend(
heads = heads,
causal = causal,
talking_heads = talking_heads,
dropout = dropout,
sparse_topk = sparse_topk,
qk_norm = qk_norm,
scale = qk_norm_scale if qk_norm else self.scale,
add_zero_kv = add_zero_kv,
flash = flash,
onnxable = onnxable
)
# 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
# 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
self.to_out = nn.Sequential(nn.Linear(out_dim, dim * 2, bias = False), nn.GLU()) if on_attn else nn.Linear(out_dim, dim, bias = False)
# whether to rotate positions into values, for absolute positions in addition to relative
self.rotary_embed_values = rotary_embed_values
# 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,
rel_pos = None,
rotary_pos_emb = None,
prev_attn = None,
mem = None,
return_intermediates = False,
cache: Optional[Intermediates] = None,
):
b, n, _, h, kv_h, head_scale, device, has_context = *x.shape, self.heads, self.kv_heads, self.head_scale, x.device, exists(context)
kv_input = default(context, x)
q_input = x
k_input = kv_input
v_input = kv_input
r_input = x
if exists(mem):
k_input, mem_packed_shape = pack([mem, k_input], 'b * d')
v_input, _ = pack([mem, v_input], 'b * d')
q = self.to_q(q_input)
k = self.to_k(k_input)
v = self.to_v(v_input) if exists(self.to_v) else k
r = self.to_r(r_input) if exists(self.to_r) else None
q = rearrange(q, 'b n (h d) -> b h n d', h = h)
k, v, r = map(lambda t: maybe(rearrange)(t, 'b n (h d) -> b h n d', h = kv_h), (k, v, r))
if exists(cache) and not has_context:
ck, cv = cache.cached_kv
if exists(mem):
mk, k = unpack(k, mem_packed_shape, 'b h * d')
mv, v = unpack(v, mem_packed_shape, 'b h * d')
k = torch.cat((ck, k), dim = -2)
v = torch.cat((cv, v), dim = -2)
if exists(mem):
k = torch.cat((mk, k), dim = -2)
v = torch.cat((mv, v), dim = -2)
if return_intermediates:
mem_len = mem.shape[-2] if exists(mem) else 0
cached_kv = (k[..., mem_len:, :], v[..., mem_len:, :])
if self.qk_norm:
qk_l2norm = partial(l2norm, groups = self.qk_norm_groups)
q, k = map(qk_l2norm, (q, k))
scale = self.qk_norm_scale
q = q * self.qk_norm_q_scale
k = k * self.qk_norm_k_scale
if exists(rotary_pos_emb) and not has_context:
freqs, xpos_scale = rotary_pos_emb
q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if exists(xpos_scale) else (1., 1.)
q = apply_rotary_pos_emb(q, freqs, q_xpos_scale)
k = apply_rotary_pos_emb(k, freqs, k_xpos_scale)
if self.rotary_embed_values:
v = apply_rotary_pos_emb(v, freqs, k_xpos_scale)
input_mask = context_mask
if not exists(input_mask) and not has_context:
input_mask = 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))
if self.qk_norm:
mem_k = l2norm(mem_k)
mem_k = mem_k * self.qk_norm_k_scale
k = torch.cat((mem_k, k), dim = -2)
v = torch.cat((mem_v, v), dim = -2)
if exists(input_mask):
input_mask = pad_at_dim(input_mask, (self.num_mem_kv, 0), dim = -1, value = True)
i, j = map(lambda t: t.shape[-2], (q, k))
# determine masking
mask_value = max_neg_value(q)
masks = []
final_attn_mask = None
if exists(input_mask):
input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
masks.append(~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 -> 1 1 i j')
elif attn_mask.ndim == 3:
attn_mask = rearrange(attn_mask, 'h i j -> 1 h i j')
masks.append(~attn_mask)
if exists(self.max_attend_past):
range_q = torch.arange(j - i, j, device = device)
range_k = torch.arange(j, device = device)
dist = rearrange(range_q, 'i -> 1 1 i 1') - rearrange(range_k, 'j -> 1 1 1 j')
max_attend_past_mask = dist > self.max_attend_past
masks.append(max_attend_past_mask)
if len(masks) > 0:
final_attn_mask = ~or_reduce(masks)
# prepare relative positional bias, if needed
attn_bias = None
if exists(rel_pos):
attn_bias = rel_pos(i, j)
# attention is all we need
out, intermediates = self.attend(
q, k, v,
mask = final_attn_mask,
attn_bias = attn_bias,
prev_attn = prev_attn
)
# https://arxiv.org/abs/2208.06061 proposes to add a residual for better gradients
if exists(r):
out = out * r + out
# normformer scaling of heads
if head_scale:
out = out * self.head_scale_params
# per head gating, from https://arxiv.org/abs/2306.12929
if exists(self.to_v_head_gate):
head_gate = self.to_v_head_gate(x)
out = out * rearrange(head_gate, 'b n h -> b h n 1').sigmoid()
# merge heads
out = rearrange(out, 'b h n d -> b n (h d)')
# alphafold2 styled gating of the values
if exists(self.to_v_gate):
gates = self.to_v_gate(x)
out = out * gates.sigmoid()
# combine the heads
out = self.to_out(out)
if exists(mask):
mask = rearrange(mask, 'b n -> b n 1')
out = out.masked_fill(~mask, 0.)
if not return_intermediates:
return out
intermediates.cached_kv = cached_kv
return out, intermediates
class AttentionLayers(nn.Module):
def __init__(
self,
dim,
depth,
heads = 8,
causal = False,
cross_attend = False,
only_cross = False,
use_scalenorm = False,
use_rmsnorm = False,
use_simple_rmsnorm = False,
alibi_pos_bias = False,
alibi_num_heads = None,
rel_pos_bias = False,
rel_pos_num_buckets = 32,
rel_pos_max_distance = 128,
dynamic_pos_bias = False,
dynamic_pos_bias_log_distance = False,
dynamic_pos_bias_mlp_depth = 2,
dynamic_pos_bias_norm = False,
rotary_pos_emb = False,
rotary_emb_dim = None,
rotary_xpos = False,
rotary_interpolation_factor = 1.,
rotary_xpos_scale_base = 512,
rotary_base_rescale_factor = 1.,
custom_layers = None,
sandwich_coef = None,
par_ratio = None,
weight_tie_layers = False, # Albert - https://arxiv.org/abs/1909.11942
layers_execute_order = None, # generalizes weight tying, can do arbitrary layer execution orders
residual_attn = False,
cross_residual_attn = False,
macaron = False,
pre_norm = True,
pre_norm_has_final_norm = True,
gate_residual = False,
scale_residual = False,
scale_residual_constant = 1.,
shift_tokens = 0,
sandwich_norm = False,
resi_dual = False,
resi_dual_scale = 1.,
zero_init_branch_output = False,
layer_dropout = 0.,
cross_attn_tokens_dropout = 0.,
**kwargs
):
super().__init__()
rotary_pos_emb = rotary_pos_emb or rotary_xpos
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
attn_kwargs, kwargs = groupby_prefix_and_trim('attn_', kwargs)
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
self.dim = dim
self.depth = depth
self.causal = causal
self.layers = nn.ModuleList([])
self.has_pos_emb = rel_pos_bias or rotary_pos_emb
rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32)
assert not (rotary_xpos and not causal), 'rotary xpos is not compatible with bidirectional attention'
self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim, use_xpos = rotary_xpos, scale_base = rotary_xpos_scale_base, interpolation_factor = rotary_interpolation_factor, base_rescale_factor = rotary_base_rescale_factor) 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'
# relative positional bias
flash_attn = attn_kwargs.get('flash', False)
assert (int(rel_pos_bias) + int(dynamic_pos_bias) + int(alibi_pos_bias)) <= 1, 'you can only choose up to one of t5, alibi, or dynamic positional bias'
self.rel_pos = None
if rel_pos_bias:
assert not flash_attn, 'flash attention not compatible with t5 relative positional 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 dynamic_pos_bias:
assert not flash_attn, 'flash attention not compatible with dynamic positional bias'
self.rel_pos = DynamicPositionBias(dim = dim // 4, heads = heads, log_distance = dynamic_pos_bias_log_distance, depth = dynamic_pos_bias_mlp_depth, norm = dynamic_pos_bias_norm)
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'
self.rel_pos = AlibiPositionalBias(heads = alibi_num_heads, total_heads = heads)
assert (int(sandwich_norm) + int(resi_dual)) <= 1, 'either sandwich norm or resiDual is selected, but not both'
assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm'
if resi_dual:
pre_norm = False
self.pre_norm = pre_norm
self.sandwich_norm = sandwich_norm
self.resi_dual = resi_dual
assert 0 < resi_dual_scale <= 1., 'resiDual prenorm residual must be scaled by a factor greater than 0 and less than or equal to 1.'
self.resi_dual_scale = resi_dual_scale
self.residual_attn = residual_attn
self.cross_residual_attn = cross_residual_attn
assert not (flash_attn and (residual_attn or cross_residual_attn)), 'flash attention is not compatible with residual attention'
self.cross_attend = cross_attend
assert (int(use_scalenorm) + int(use_rmsnorm) + int(use_simple_rmsnorm)) <= 1, 'you can only use either scalenorm, rmsnorm, or simple rmsnorm'
if use_scalenorm:
norm_class = ScaleNorm
elif use_rmsnorm:
norm_class = RMSNorm
elif use_simple_rmsnorm:
norm_class = SimpleRMSNorm
else:
norm_class = nn.LayerNorm
norm_fn = partial(norm_class, dim)
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
# zero init
if zero_init_branch_output:
attn_kwargs = {**attn_kwargs, 'zero_init_output': True}
ff_kwargs = {**ff_kwargs, 'zero_init_output': True}
# setup weight tying, which is a special case of `layer_execute_order`
assert not (weight_tie_layers and any([*map(exists, (custom_layers, par_ratio, sandwich_coef))]))
if weight_tie_layers:
assert not exists(layers_execute_order)
layers_execute_order = tuple(range(len(default_block))) * depth
depth = 1
# 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.layers_execute_order = default(layers_execute_order, tuple(range(len(layer_types))))
assert all([i < len(self.layer_types) for i in self.layers_execute_order])
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
# stochastic depth
self.layer_dropouts = cast_tuple(layer_dropout, len(layer_types))
# structured dropout for cross attending
self.cross_attn_tokens_dropout = cross_attn_tokens_dropout
# calculate token shifting
shift_tokens = cast_tuple(shift_tokens, len(layer_types))
# whether it has post norm
self.final_norm = norm_fn() if pre_norm or resi_dual else nn.Identity()
# 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)
residual_fn = GRUGating if gate_residual else Residual
residual = residual_fn(dim, scale_residual = scale_residual, scale_residual_constant = scale_residual_constant)
pre_branch_norm = norm_fn() if pre_norm else None
post_branch_norm = norm_fn() if sandwich_norm else None
post_main_norm = norm_fn() if not pre_norm 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,
mask = None,
context_mask = None,
attn_mask = None,
self_attn_kv_mask = None,
mems = None,
seq_start_pos: Optional[Tensor] = None,
cache: Optional[LayerIntermediates] = None,
cache_age = 1,
return_hiddens = False
):
assert not (self.cross_attend ^ exists(context)), 'context must be passed in if cross_attend is set to True'
# initialize accums
hiddens = []
layer_hiddens = []
intermediates = []
prev_attn = None
prev_cross_attn = None
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
# handle left padded sequences
if exists(seq_start_pos):
seq_arange = torch.arange(x.shape[-2], device = x.device, dtype = torch.long)
left_pad_mask = seq_arange >= seq_start_pos[..., None]
if exists(self_attn_kv_mask):
self_attn_kv_mask = self_attn_kv_mask & left_pad_mask
else:
self_attn_kv_mask = left_pad_mask
# rotary positions
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)
# assume cached key / values
attn_cache = []
if exists(cache):
assert not self.training and self.causal and not any([*map(exists, (mask, attn_mask))])
if cache_age > 0:
x = x[:, -cache_age:] # for spec decoding, may be greater than 1
attn_cache = cache.attn_intermediates
iter_attn_cache = iter(attn_cache)
# outer residual - for resiDual paper
outer_residual = x * self.resi_dual_scale
# get layers to be executed
layer_variables = (
self.layer_types,
self.layers,
self.layer_dropouts
)
layer_variables = tuple(tuple(layer_variable[i] for i in self.layers_execute_order) for layer_variable in layer_variables)
# go through the attention and feedforward layers
for ind, (layer_type, (norm, block, residual_fn), layer_dropout) in enumerate(zip(*layer_variables)):
is_last = ind == (len(self.layers) - 1)
if self.training and layer_dropout > 0. and random() < layer_dropout:
continue
if layer_type == 'a':
if return_hiddens:
hiddens.append(x)
layer_mem = mems.pop(0) if mems else None
if layer_type == 'c':
if self.training and self.cross_attn_tokens_dropout > 0.:
context, context_mask = dropout_seq(context, context_mask, self.cross_attn_tokens_dropout)
inner_residual = x
if return_hiddens:
layer_hiddens.append(x)
pre_norm, post_branch_norm, post_main_norm = norm
if exists(pre_norm):
x = pre_norm(x)
if layer_type == 'a':
out, inter = block(x, mask = mask, context_mask = self_attn_kv_mask, attn_mask = attn_mask, rel_pos = self.rel_pos, rotary_pos_emb = rotary_pos_emb, prev_attn = prev_attn, cache = next(iter_attn_cache, None), mem = layer_mem, return_intermediates = True)
elif layer_type == 'c':
out, inter = block(x, context = context, mask = mask, context_mask = context_mask, prev_attn = prev_cross_attn, cache = next(iter_attn_cache, None), return_intermediates = True)
elif layer_type == 'f':
out = block(x)
if self.resi_dual:
outer_residual = outer_residual + out * self.resi_dual_scale
if exists(post_branch_norm):
out = post_branch_norm(out)
x = residual_fn(out, inner_residual)
if layer_type in ('a', 'c') and return_hiddens:
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)
if return_hiddens:
layer_hiddens.append(x)
if self.resi_dual:
x = x + self.final_norm(outer_residual)
else:
x = self.final_norm(x)
if not return_hiddens:
return x
intermediates = LayerIntermediates(
hiddens = hiddens,
attn_intermediates = intermediates,
layer_hiddens = layer_hiddens
)
return x, intermediates
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,
channels = 3,
num_classes = None,
post_emb_norm = False,
num_register_tokens = 0,
emb_dropout = 0.
):
super().__init__()
assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder'
assert divisible_by(image_size, patch_size), 'image dimensions must be divisible by the patch size'
dim = attn_layers.dim
num_patches = (image_size // patch_size) ** 2
patch_dim = channels * patch_size ** 2
self.patch_size = patch_size
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
has_register_tokens = num_register_tokens > 0
self.has_register_tokens = has_register_tokens
if has_register_tokens:
self.register_tokens = nn.Parameter(torch.randn(num_register_tokens, dim))
self.patch_to_embedding = nn.Sequential(
nn.LayerNorm(patch_dim),
nn.Linear(patch_dim, dim),
nn.LayerNorm(dim)
)
self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
self.dropout = nn.Dropout(emb_dropout)
self.attn_layers = attn_layers
self.mlp_head = nn.Linear(dim, num_classes) if exists(num_classes) else nn.Identity()
def forward(
self,
img,
return_embeddings = False
):
b, p = img.shape[0], 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)
n = x.shape[1]
x = x + self.pos_embedding[:, :n]
x = self.post_emb_norm(x)
x = self.dropout(x)
if self.has_register_tokens:
r = repeat(self.register_tokens, 'n d -> b n d', b = b)
x, ps = pack((x, r), 'b * d')
x = self.attn_layers(x)
if self.has_register_tokens:
x, _ = unpack(x, ps, 'b * d')
if not exists(self.mlp_head) or return_embeddings:
return x
x = x.mean(dim = -2)
return self.mlp_head(x)
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.,
post_emb_norm = False,
num_memory_tokens = None,
memory_tokens_interspersed_every = None,
tie_embedding = False,
logits_dim = None,
use_abs_pos_emb = True,
scaled_sinu_pos_emb = False,
l2norm_embed = False,
emb_frac_gradient = 1., # GLM-130B and Cogview successfully used this, set at 0.1
attn_z_loss_weight = 1e-4,
):
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.emb_dim = emb_dim
self.num_tokens = num_tokens
self.max_seq_len = max_seq_len
self.max_mem_len = max_mem_len
self.shift_mem_down = shift_mem_down
self.l2norm_embed = l2norm_embed
self.token_emb = TokenEmbedding(emb_dim, num_tokens, l2norm_embed = l2norm_embed)
if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
self.pos_emb = always(0)
elif scaled_sinu_pos_emb:
self.pos_emb = ScaledSinusoidalEmbedding(emb_dim)
else:
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len, l2norm_embed = l2norm_embed)
self.emb_frac_gradient = emb_frac_gradient # fraction of the gradient that should go to the embedding, https://arxiv.org/abs/2105.13290
self.post_emb_norm = nn.LayerNorm(emb_dim) if post_emb_norm else nn.Identity()
self.emb_dropout = nn.Dropout(emb_dropout)
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
self.attn_layers = attn_layers
self.init_()
logits_dim = default(logits_dim, num_tokens)
self.to_logits = nn.Linear(dim, logits_dim) if not tie_embedding else lambda t: t @ self.token_emb.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))
self.memory_tokens_interspersed_every = memory_tokens_interspersed_every
# whether can do cached kv decoding
self.can_cache_kv = self.num_memory_tokens == 0
def init_(self):
if self.l2norm_embed:
nn.init.normal_(self.token_emb.emb.weight, std = 1e-5)
if not isinstance(self.pos_emb, always):
nn.init.normal_(self.pos_emb.emb.weight, std = 1e-5)
return
nn.init.kaiming_normal_(self.token_emb.emb.weight)
def forward(
self,
x,
return_embeddings = False,
return_logits_and_embeddings = False,
return_intermediates = False,
mask = None,
return_mems = False,
return_attn = False,
mems = None,
pos = None,
prepend_embeds = None,
sum_embeds = None,
return_attn_z_loss = False,
attn_z_loss_weight = 1e-4,
seq_start_pos = None,
cache: Optional[LayerIntermediates] = None,
**kwargs
):
b, n, device, num_mems, has_memory_tokens, emb_frac_gradient = *x.shape, x.device, self.num_memory_tokens, self.num_memory_tokens > 0, self.emb_frac_gradient
return_hiddens = return_mems | return_attn | return_intermediates | return_attn_z_loss
# absolute positional embedding
external_pos_emb = exists(pos) and pos.dtype != torch.long
pos_emb = self.pos_emb(x, pos = pos, seq_start_pos = seq_start_pos) if not external_pos_emb else pos
x = self.token_emb(x) + pos_emb
# for summing embeddings passed externally - needs this for self-conditioning in non-autoregressive training
if exists(sum_embeds):
x = x + sum_embeds
# post embedding norm, purportedly leads to greater stabilization
x = self.post_emb_norm(x)
# whether to append embeds, as in PaLI, for image embeddings
if exists(prepend_embeds):
prepend_seq, prepend_dim = prepend_embeds.shape[1:]
assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as text model dimensions'
x = torch.cat((prepend_embeds, x), dim = -2)
# whether to reduce the gradient going to the embedding, from cogview paper, corroborated by GLM-130B model
if emb_frac_gradient < 1:
assert emb_frac_gradient > 0
x = x * emb_frac_gradient + x.detach() * (1 - emb_frac_gradient)
# embedding dropout
x = self.emb_dropout(x)
x = self.project_emb(x)
if has_memory_tokens:
mem_every = self.memory_tokens_interspersed_every
if exists(mem_every):
assert mem_every > 0
assert isinstance(self.attn_layers, Decoder), 'only for decoder'
next_seq_len = math.ceil(n / mem_every) * mem_every
x = pad_at_dim(x, (0, next_seq_len - n), dim = -2, value = 0.)
x = rearrange(x, 'b (n m) d -> (b n) m d', m = mem_every)
mem = repeat(self.memory_tokens, 'n d -> b n d', b = x.shape[0])
x, mem_packed_shape = pack((mem, x), 'b * d')
# auto-handle masking after appending memory tokens
if not exists(mem_every) and exists(mask):
mask = pad_at_dim(mask, (num_mems, 0), dim = -1, value = True)
if exists(mem_every):
x = rearrange(x, '(b n) m d -> b (n m) d', b = b)
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, cache = cache, return_hiddens = True, seq_start_pos = seq_start_pos, **kwargs)
if has_memory_tokens:
if exists(mem_every):
x = rearrange(x, 'b (n m) d -> (b n) m d', m = (mem_every + num_mems))
mem, x = unpack(x, mem_packed_shape, 'b * d')
if exists(mem_every):
x = rearrange(x, '(b n) m d -> b (n m) d', b = b)
x = x[:, :n]
if return_logits_and_embeddings:
out = (self.to_logits(x), x)
elif return_embeddings:
out = x
else:
out = self.to_logits(x)
if return_attn_z_loss:
pre_softmax_attns = list(map(lambda t: t.pre_softmax_attn, intermediates.attn_intermediates))
intermediates.attn_z_loss = calc_z_loss(pre_softmax_attns, weight = attn_z_loss_weight)
return_intermediates = True
if return_mems:
hiddens = intermediates.hiddens
new_mems = list(map(lambda pair: torch.cat(pair, dim = -2), zip(mems, hiddens))) if exists(mems) else hiddens
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
if not return_intermediates:
return out, new_mems
intermediates.mems = new_mems
if return_intermediates:
return out, intermediates
if return_attn:
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
return out, attn_maps
return out
class ContinuousTransformerWrapper(nn.Module):
def __init__(
self,
*,
max_seq_len,
attn_layers,
dim_in = None,
dim_out = None,
emb_dim = None,
max_mem_len = 0,
post_emb_norm = False,
emb_dropout = 0.,
use_abs_pos_emb = True,
scaled_sinu_pos_emb = False
):
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.max_mem_len = max_mem_len
if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
self.pos_emb = always(0)
elif scaled_sinu_pos_emb:
self.pos_emb = ScaledSinusoidalEmbedding(dim)
else:
self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len)
self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
self.emb_dropout = nn.Dropout(emb_dropout)
self.project_in = nn.Linear(dim_in, dim) if exists(dim_in) else nn.Identity()
self.attn_layers = attn_layers
self.project_out = nn.Linear(dim, dim_out) if exists(dim_out) else nn.Identity()
def forward(
self,
x,
return_embeddings = False,
return_intermediates = False,
return_mems = False,
mask = None,
return_attn = False,
mems = None,
pos = None,
prepend_embeds = None,
**kwargs
):
x = self.project_in(x)
x = x + self.pos_emb(x, pos = pos)
x = self.post_emb_norm(x)
# whether to append embeds, as in PaLI, for image embeddings
if exists(prepend_embeds):
_, prepend_dim = prepend_embeds.shape[1:]
assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as model dimensions'
x = torch.cat((prepend_embeds, x), dim = -2)
x = self.emb_dropout(x)
x, intermediates = self.attn_layers(x, mask = mask, mems = mems, return_hiddens = True, **kwargs)
out = self.project_out(x) if not return_embeddings else x
if return_intermediates:
return out, intermediates
if return_mems:
hiddens = intermediates.hiddens
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), hiddens))
return out, new_mems
if return_attn:
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
return out, attn_maps
return out
class XTransformer(nn.Module):
def __init__(
self,
*,
dim,
tie_token_emb = False,
ignore_index = -100,
pad_value = 0,
cross_attn_tokens_dropout = 0.,
**kwargs
):
super().__init__()
enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
enc_transformer_kwargs['scaled_sinu_pos_emb'] = enc_kwargs.pop('scaled_sinu_pos_emb', False)
enc_transformer_kwargs['use_abs_pos_emb'] = enc_kwargs.pop('use_abs_pos_emb', True)
dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
dec_transformer_kwargs['scaled_sinu_pos_emb'] = dec_kwargs.pop('scaled_sinu_pos_emb', False)
dec_transformer_kwargs['use_abs_pos_emb'] = dec_kwargs.pop('use_abs_pos_emb', True)
self.cross_attn_tokens_dropout = cross_attn_tokens_dropout # how many tokens from the encoder to dropout when cross attending from decoder - seen in a couple papers, including Perceiver AR - this will also be very effective regularization when cross attending to very long memories
self.encoder = TransformerWrapper(
**enc_transformer_kwargs,
attn_layers = Encoder(dim = dim, **enc_kwargs)
)
self.decoder = TransformerWrapper(
**dec_transformer_kwargs,
attn_layers = Decoder(dim = dim, cross_attend = True, **dec_kwargs)
)
if tie_token_emb:
self.decoder.token_emb = self.encoder.token_emb
self.decoder = AutoregressiveWrapper(self.decoder, ignore_index=ignore_index, pad_value=pad_value)
@torch.no_grad()
def generate(self, seq_in, seq_out_start, seq_len, mask = None, attn_mask = None, **kwargs):
encodings = self.encoder(seq_in, mask = mask, attn_mask = attn_mask, return_embeddings = True)
return self.decoder.generate(seq_out_start, seq_len, context = encodings, context_mask = mask, **kwargs)
def forward(self, src, tgt, mask = None, attn_mask = None, src_prepend_embeds = None):
if exists(src_prepend_embeds) and exists(mask):
mask = pad_at_dim(mask, (src_prepend_embeds.shape[-2], 0), dim = -1, value = True)
enc = self.encoder(src, mask = mask, attn_mask = attn_mask, prepend_embeds = src_prepend_embeds, return_embeddings = True)
if self.training and self.cross_attn_tokens_dropout > 0:
enc, mask = dropout_seq(enc, mask, self.cross_attn_tokens_dropout)
out = self.decoder(tgt, context = enc, context_mask = mask)
return out |