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import torch
from torch import nn
import math
from functools import partial
from einops import rearrange, repeat
from local_attention import LocalAttention
import torch.nn.functional as F
#import fast_transformers.causal_product.causal_product_cuda
def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None):
b, h, *_ = data.shape
# (batch size, head, length, model_dim)
# normalize model dim
data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.
# what is ration?, projection_matrix.shape[0] --> 266
ratio = (projection_matrix.shape[0] ** -0.5)
projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h)
projection = projection.type_as(data)
#data_dash = w^T x
data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection)
# diag_data = D**2
diag_data = data ** 2
diag_data = torch.sum(diag_data, dim=-1)
diag_data = (diag_data / 2.0) * (data_normalizer ** 2)
diag_data = diag_data.unsqueeze(dim=-1)
#print ()
if is_query:
data_dash = ratio * (
torch.exp(data_dash - diag_data -
torch.max(data_dash, dim=-1, keepdim=True).values) + eps)
else:
data_dash = ratio * (
torch.exp(data_dash - diag_data + eps))#- torch.max(data_dash)) + eps)
return data_dash.type_as(data)
def orthogonal_matrix_chunk(cols, qr_uniform_q = False, device = None):
unstructured_block = torch.randn((cols, cols), device = device)
q, r = torch.linalg.qr(unstructured_block.cpu(), mode='reduced')
q, r = map(lambda t: t.to(device), (q, r))
# proposed by @Parskatt
# to make sure Q is uniform https://arxiv.org/pdf/math-ph/0609050.pdf
if qr_uniform_q:
d = torch.diag(r, 0)
q *= d.sign()
return q.t()
def exists(val):
return val is not None
def empty(tensor):
return tensor.numel() == 0
def default(val, d):
return val if exists(val) else d
def cast_tuple(val):
return (val,) if not isinstance(val, tuple) else val
class PCmer(nn.Module):
"""The encoder that is used in the Transformer model."""
def __init__(self,
num_layers,
num_heads,
dim_model,
dim_keys,
dim_values,
residual_dropout,
attention_dropout):
super().__init__()
self.num_layers = num_layers
self.num_heads = num_heads
self.dim_model = dim_model
self.dim_values = dim_values
self.dim_keys = dim_keys
self.residual_dropout = residual_dropout
self.attention_dropout = attention_dropout
self._layers = nn.ModuleList([_EncoderLayer(self) for _ in range(num_layers)])
# METHODS ########################################################################################################
def forward(self, phone, mask=None):
# apply all layers to the input
for (i, layer) in enumerate(self._layers):
phone = layer(phone, mask)
# provide the final sequence
return phone
# ==================================================================================================================== #
# CLASS _ E N C O D E R L A Y E R #
# ==================================================================================================================== #
class _EncoderLayer(nn.Module):
"""One layer of the encoder.
Attributes:
attn: (:class:`mha.MultiHeadAttention`): The attention mechanism that is used to read the input sequence.
feed_forward (:class:`ffl.FeedForwardLayer`): The feed-forward layer on top of the attention mechanism.
"""
def __init__(self, parent: PCmer):
"""Creates a new instance of ``_EncoderLayer``.
Args:
parent (Encoder): The encoder that the layers is created for.
"""
super().__init__()
self.conformer = ConformerConvModule(parent.dim_model)
self.norm = nn.LayerNorm(parent.dim_model)
self.dropout = nn.Dropout(parent.residual_dropout)
# selfatt -> fastatt: performer!
self.attn = SelfAttention(dim = parent.dim_model,
heads = parent.num_heads,
causal = False)
# METHODS ########################################################################################################
def forward(self, phone, mask=None):
# compute attention sub-layer
phone = phone + (self.attn(self.norm(phone), mask=mask))
phone = phone + (self.conformer(phone))
return phone
def calc_same_padding(kernel_size):
pad = kernel_size // 2
return (pad, pad - (kernel_size + 1) % 2)
# helper classes
class Swish(nn.Module):
def forward(self, x):
return x * x.sigmoid()
class Transpose(nn.Module):
def __init__(self, dims):
super().__init__()
assert len(dims) == 2, 'dims must be a tuple of two dimensions'
self.dims = dims
def forward(self, x):
return x.transpose(*self.dims)
class GLU(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
out, gate = x.chunk(2, dim=self.dim)
return out * gate.sigmoid()
class DepthWiseConv1d(nn.Module):
def __init__(self, chan_in, chan_out, kernel_size, padding):
super().__init__()
self.padding = padding
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups = chan_in)
def forward(self, x):
x = F.pad(x, self.padding)
return self.conv(x)
class ConformerConvModule(nn.Module):
def __init__(
self,
dim,
causal = False,
expansion_factor = 2,
kernel_size = 31,
dropout = 0.):
super().__init__()
inner_dim = dim * expansion_factor
padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
self.net = nn.Sequential(
nn.LayerNorm(dim),
Transpose((1, 2)),
nn.Conv1d(dim, inner_dim * 2, 1),
GLU(dim=1),
DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
#nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
Swish(),
nn.Conv1d(inner_dim, dim, 1),
Transpose((1, 2)),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
def linear_attention(q, k, v):
if v is None:
#print (k.size(), q.size())
out = torch.einsum('...ed,...nd->...ne', k, q)
return out
else:
k_cumsum = k.sum(dim = -2)
#k_cumsum = k.sum(dim = -2)
D_inv = 1. / (torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) + 1e-8)
context = torch.einsum('...nd,...ne->...de', k, v)
#print ("TRUEEE: ", context.size(), q.size(), D_inv.size())
out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv)
return out
def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, qr_uniform_q = False, device = None):
nb_full_blocks = int(nb_rows / nb_columns)
#print (nb_full_blocks)
block_list = []
for _ in range(nb_full_blocks):
q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
block_list.append(q)
# block_list[n] is a orthogonal matrix ... (model_dim * model_dim)
#print (block_list[0].size(), torch.einsum('...nd,...nd->...n', block_list[0], torch.roll(block_list[0],1,1)))
#print (nb_rows, nb_full_blocks, nb_columns)
remaining_rows = nb_rows - nb_full_blocks * nb_columns
#print (remaining_rows)
if remaining_rows > 0:
q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
#print (q[:remaining_rows].size())
block_list.append(q[:remaining_rows])
final_matrix = torch.cat(block_list)
if scaling == 0:
multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1)
elif scaling == 1:
multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device)
else:
raise ValueError(f'Invalid scaling {scaling}')
return torch.diag(multiplier) @ final_matrix
class FastAttention(nn.Module):
def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, no_projection = False):
super().__init__()
nb_features = default(nb_features, int(dim_heads * math.log(dim_heads)))
self.dim_heads = dim_heads
self.nb_features = nb_features
self.ortho_scaling = ortho_scaling
self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling, qr_uniform_q = qr_uniform_q)
projection_matrix = self.create_projection()
self.register_buffer('projection_matrix', projection_matrix)
self.generalized_attention = generalized_attention
self.kernel_fn = kernel_fn
# if this is turned on, no projection will be used
# queries and keys will be softmax-ed as in the original efficient attention paper
self.no_projection = no_projection
self.causal = causal
if causal:
try:
import fast_transformers.causal_product.causal_product_cuda
self.causal_linear_fn = partial(causal_linear_attention)
except ImportError:
print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version')
self.causal_linear_fn = causal_linear_attention_noncuda
@torch.no_grad()
def redraw_projection_matrix(self):
projections = self.create_projection()
self.projection_matrix.copy_(projections)
del projections
def forward(self, q, k, v):
device = q.device
if self.no_projection:
q = q.softmax(dim = -1)
k = torch.exp(k) if self.causal else k.softmax(dim = -2)
elif self.generalized_attention:
create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device)
q, k = map(create_kernel, (q, k))
else:
create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device)
q = create_kernel(q, is_query = True)
k = create_kernel(k, is_query = False)
attn_fn = linear_attention if not self.causal else self.causal_linear_fn
if v is None:
out = attn_fn(q, k, None)
return out
else:
out = attn_fn(q, k, v)
return out
class SelfAttention(nn.Module):
def __init__(self, dim, causal = False, heads = 8, dim_head = 64, local_heads = 0, local_window_size = 256, nb_features = None, feature_redraw_interval = 1000, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, dropout = 0., no_projection = False):
super().__init__()
assert dim % heads == 0, 'dimension must be divisible by number of heads'
dim_head = default(dim_head, dim // heads)
inner_dim = dim_head * heads
self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, qr_uniform_q = qr_uniform_q, no_projection = no_projection)
self.heads = heads
self.global_heads = heads - local_heads
self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None
#print (heads, nb_features, dim_head)
#name_embedding = torch.zeros(110, heads, dim_head, dim_head)
#self.name_embedding = nn.Parameter(name_embedding, requires_grad=True)
self.to_q = nn.Linear(dim, inner_dim)
self.to_k = nn.Linear(dim, inner_dim)
self.to_v = nn.Linear(dim, inner_dim)
self.to_out = nn.Linear(inner_dim, dim)
self.dropout = nn.Dropout(dropout)
@torch.no_grad()
def redraw_projection_matrix(self):
self.fast_attention.redraw_projection_matrix()
#torch.nn.init.zeros_(self.name_embedding)
#print (torch.sum(self.name_embedding))
def forward(self, x, context = None, mask = None, context_mask = None, name=None, inference=False, **kwargs):
b, n, _, h, gh = *x.shape, self.heads, self.global_heads
cross_attend = exists(context)
context = default(context, x)
context_mask = default(context_mask, mask) if not cross_attend else context_mask
#print (torch.sum(self.name_embedding))
q, k, v = self.to_q(x), self.to_k(context), self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
(q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))
attn_outs = []
#print (name)
#print (self.name_embedding[name].size())
if not empty(q):
if exists(context_mask):
global_mask = context_mask[:, None, :, None]
v.masked_fill_(~global_mask, 0.)
if cross_attend:
pass
#print (torch.sum(self.name_embedding))
#out = self.fast_attention(q,self.name_embedding[name],None)
#print (torch.sum(self.name_embedding[...,-1:]))
else:
out = self.fast_attention(q, k, v)
attn_outs.append(out)
if not empty(lq):
assert not cross_attend, 'local attention is not compatible with cross attention'
out = self.local_attn(lq, lk, lv, input_mask = mask)
attn_outs.append(out)
out = torch.cat(attn_outs, dim = 1)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return self.dropout(out)