import torch
import torch.nn as nn
import numpy as np
from math import sqrt
#from utils.masking import TriangularCausalMask, ProbMask
#from reformer_pytorch import LSHSelfAttention
from einops import rearrange
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class TriangularCausalMask():
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def __init__(self, B, L, device="cpu"):
mask_shape = [B, 1, L, L]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
@property
def mask(self):
return self._mask
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class ProbMask():
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def __init__(self, B, H, L, index, scores, device="cpu"):
_mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1)
_mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1])
indicator = _mask_ex[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
index, :].to(device)
self._mask = indicator.view(scores.shape).to(device)
@property
def mask(self):
return self._mask
# Code implementation from https://github.com/thuml/Flowformer
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class FlowAttention(nn.Module):
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def __init__(self, attention_dropout=0.1):
super(FlowAttention, self).__init__()
self.dropout = nn.Dropout(attention_dropout)
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def kernel_method(self, x):
return torch.sigmoid(x)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
queries = queries.transpose(1, 2)
keys = keys.transpose(1, 2)
values = values.transpose(1, 2)
# kernel
queries = self.kernel_method(queries)
keys = self.kernel_method(keys)
# incoming and outgoing
normalizer_row = 1.0 / (torch.einsum("nhld,nhd->nhl", queries + 1e-6, keys.sum(dim=2) + 1e-6))
normalizer_col = 1.0 / (torch.einsum("nhsd,nhd->nhs", keys + 1e-6, queries.sum(dim=2) + 1e-6))
# reweighting
normalizer_row_refine = (
torch.einsum("nhld,nhd->nhl", queries + 1e-6, (keys * normalizer_col[:, :, :, None]).sum(dim=2) + 1e-6))
normalizer_col_refine = (
torch.einsum("nhsd,nhd->nhs", keys + 1e-6, (queries * normalizer_row[:, :, :, None]).sum(dim=2) + 1e-6))
# competition and allocation
normalizer_row_refine = torch.sigmoid(
normalizer_row_refine * (float(queries.shape[2]) / float(keys.shape[2])))
normalizer_col_refine = torch.softmax(normalizer_col_refine, dim=-1) * keys.shape[2] # B h L vis
# multiply
kv = keys.transpose(-2, -1) @ (values * normalizer_col_refine[:, :, :, None])
x = (((queries @ kv) * normalizer_row[:, :, :, None]) * normalizer_row_refine[:, :, :, None]).transpose(1,
2).contiguous()
return x, None
# Code implementation from https://github.com/shreyansh26/FlashAttention-PyTorch
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class FlashAttention(nn.Module):
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FlashAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
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def flash_attention_forward(self, Q, K, V, mask=None):
BLOCK_SIZE = 32
NEG_INF = -1e10 # -infinity
EPSILON = 1e-10
# mask = torch.randint(0, 2, (128, 8)).to(device='cuda')
O = torch.zeros_like(Q, requires_grad=True)
l = torch.zeros(Q.shape[:-1])[..., None]
m = torch.ones(Q.shape[:-1])[..., None] * NEG_INF
O = O.to(device='cuda')
l = l.to(device='cuda')
m = m.to(device='cuda')
Q_BLOCK_SIZE = min(BLOCK_SIZE, Q.shape[-1])
KV_BLOCK_SIZE = BLOCK_SIZE
Q_BLOCKS = torch.split(Q, Q_BLOCK_SIZE, dim=2)
K_BLOCKS = torch.split(K, KV_BLOCK_SIZE, dim=2)
V_BLOCKS = torch.split(V, KV_BLOCK_SIZE, dim=2)
if mask is not None:
mask_BLOCKS = list(torch.split(mask, KV_BLOCK_SIZE, dim=1))
Tr = len(Q_BLOCKS)
Tc = len(K_BLOCKS)
O_BLOCKS = list(torch.split(O, Q_BLOCK_SIZE, dim=2))
l_BLOCKS = list(torch.split(l, Q_BLOCK_SIZE, dim=2))
m_BLOCKS = list(torch.split(m, Q_BLOCK_SIZE, dim=2))
for j in range(Tc):
Kj = K_BLOCKS[j]
Vj = V_BLOCKS[j]
if mask is not None:
maskj = mask_BLOCKS[j]
for i in range(Tr):
Qi = Q_BLOCKS[i]
Oi = O_BLOCKS[i]
li = l_BLOCKS[i]
mi = m_BLOCKS[i]
scale = 1 / np.sqrt(Q.shape[-1])
Qi_scaled = Qi * scale
S_ij = torch.einsum('... i d, ... j d -> ... i j', Qi_scaled, Kj)
if mask is not None:
# Masking
maskj_temp = rearrange(maskj, 'b j -> b 1 1 j')
S_ij = torch.where(maskj_temp > 0, S_ij, NEG_INF)
m_block_ij, _ = torch.max(S_ij, dim=-1, keepdims=True)
P_ij = torch.exp(S_ij - m_block_ij)
if mask is not None:
# Masking
P_ij = torch.where(maskj_temp > 0, P_ij, 0.)
l_block_ij = torch.sum(P_ij, dim=-1, keepdims=True) + EPSILON
P_ij_Vj = torch.einsum('... i j, ... j d -> ... i d', P_ij, Vj)
mi_new = torch.maximum(m_block_ij, mi)
li_new = torch.exp(mi - mi_new) * li + torch.exp(m_block_ij - mi_new) * l_block_ij
O_BLOCKS[i] = (li / li_new) * torch.exp(mi - mi_new) * Oi + (
torch.exp(m_block_ij - mi_new) / li_new) * P_ij_Vj
l_BLOCKS[i] = li_new
m_BLOCKS[i] = mi_new
O = torch.cat(O_BLOCKS, dim=2)
l = torch.cat(l_BLOCKS, dim=2)
m = torch.cat(m_BLOCKS, dim=2)
return O, l, m
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
res = \
self.flash_attention_forward(queries.permute(0, 2, 1, 3), keys.permute(0, 2, 1, 3), values.permute(0, 2, 1, 3),
attn_mask)[0]
return res.permute(0, 2, 1, 3).contiguous(), None
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class FullAttention(nn.Module):
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FullAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L, H, E = queries.shape
_, S, _, D = values.shape
scale = self.scale or 1. / sqrt(E)
scores = torch.einsum("blhe,bshe->bhls", queries, keys)
if self.mask_flag:
if attn_mask is None:
attn_mask = TriangularCausalMask(B, L, device=queries.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1))
V = torch.einsum("bhls,bshd->blhd", A, values)
if self.output_attention:
return (V.contiguous(), A)
else:
return (V.contiguous(), None)
# Code implementation from https://github.com/zhouhaoyi/Informer2020
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class ProbAttention(nn.Module):
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(ProbAttention, self).__init__()
self.factor = factor
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q)
# Q [B, H, L, D]
B, H, L_K, E = K.shape
_, _, L_Q, _ = Q.shape
# calculate the sampled Q_K
K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
# real U = U_part(factor*ln(L_k))*L_q
index_sample = torch.randint(L_K, (L_Q, sample_k))
K_sample = K_expand[:, :, torch.arange(
L_Q).unsqueeze(1), index_sample, :]
Q_K_sample = torch.matmul(
Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
# find the Top_k query with sparisty measurement
M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
M_top = M.topk(n_top, sorted=False)[1]
# use the reduced Q to calculate Q_K
Q_reduce = Q[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
M_top, :] # factor*ln(L_q)
Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k
return Q_K, M_top
def _get_initial_context(self, V, L_Q):
B, H, L_V, D = V.shape
if not self.mask_flag:
# V_sum = V.sum(dim=-2)
V_sum = V.mean(dim=-2)
contex = V_sum.unsqueeze(-2).expand(B, H,
L_Q, V_sum.shape[-1]).clone()
else: # use mask
# requires that L_Q == L_V, i.e. for self-attention only
assert (L_Q == L_V)
contex = V.cumsum(dim=-2)
return contex
def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
B, H, L_V, D = V.shape
if self.mask_flag:
attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores)
context_in[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
index, :] = torch.matmul(attn, V).type_as(context_in)
if self.output_attention:
attns = (torch.ones([B, H, L_V, L_V]) /
L_V).type_as(attn).to(attn.device)
attns[torch.arange(B)[:, None, None], torch.arange(H)[
None, :, None], index, :] = attn
return (context_in, attns)
else:
return (context_in, None)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L_Q, H, D = queries.shape
_, L_K, _, _ = keys.shape
queries = queries.transpose(2, 1)
keys = keys.transpose(2, 1)
values = values.transpose(2, 1)
U_part = self.factor * \
np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k)
u = self.factor * \
np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q)
U_part = U_part if U_part < L_K else L_K
u = u if u < L_Q else L_Q
scores_top, index = self._prob_QK(
queries, keys, sample_k=U_part, n_top=u)
# add scale factor
scale = self.scale or 1. / sqrt(D)
if scale is not None:
scores_top = scores_top * scale
# get the context
context = self._get_initial_context(values, L_Q)
# update the context with selected top_k queries
context, attn = self._update_context(
context, values, scores_top, index, L_Q, attn_mask)
return context.contiguous(), attn
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class AttentionLayer(nn.Module):
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def __init__(self, attention, d_model, n_heads, d_keys=None,
d_values=None):
super(AttentionLayer, self).__init__()
d_keys = d_keys or (d_model // n_heads)
d_values = d_values or (d_model // n_heads)
self.inner_attention = attention
self.query_projection = nn.Linear(d_model, d_keys * n_heads)
self.key_projection = nn.Linear(d_model, d_keys * n_heads)
self.value_projection = nn.Linear(d_model, d_values * n_heads)
self.out_projection = nn.Linear(d_values * n_heads, d_model)
self.n_heads = n_heads
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L, _ = queries.shape
_, S, _ = keys.shape
H = self.n_heads
queries = self.query_projection(queries).view(B, L, H, -1)
keys = self.key_projection(keys).view(B, S, H, -1)
values = self.value_projection(values).view(B, S, H, -1)
out, attn = self.inner_attention(
queries,
keys,
values,
attn_mask,
tau=tau,
delta=delta
)
out = out.view(B, L, -1)
return self.out_projection(out), attn
'''
class ReformerLayer(nn.Module):
def __init__(self, attention, d_model, n_heads, d_keys=None,
d_values=None, causal=False, bucket_size=4, n_hashes=4):
super().__init__()
self.bucket_size = bucket_size
self.attn = LSHSelfAttention(
dim=d_model,
heads=n_heads,
bucket_size=bucket_size,
n_hashes=n_hashes,
causal=causal
)
def fit_length(self, queries):
# inside reformer: assert N % (bucket_size * 2) == 0
B, N, C = queries.shape
if N % (self.bucket_size * 2) == 0:
return queries
else:
# fill the time series
fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
def forward(self, queries, keys, values, attn_mask, tau, delta):
# in Reformer: defalut queries=keys
B, N, C = queries.shape
queries = self.attn(self.fit_length(queries))[:, :N, :]
return queries, None
'''