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import torch
from torch import nn, cat
import torch.nn.functional as F
from torch.nn import Module, ModuleList, Parameter, ParameterList
from einops import rearrange
# functions
def l2norm(t):
return F.normalize(t, dim = -1)
# norms
class LayerNorm(Module):
def __init__(
self,
dim
):
super().__init__()
self.ln = nn.LayerNorm(dim, elementwise_affine = False)
self.gamma = Parameter(torch.zeros(dim))
def forward(self, x):
gamma = self.gamma
if gamma.ndim == 2:
gamma = rearrange(gamma, 'b d -> b 1 d')
return self.ln(x) * (gamma + 1.)
# norm + residual wrapper, as used in original TTT paper
# but could be removed
class ResidualNorm(Module):
def __init__(
self,
dim,
model: Module
):
super().__init__()
self.norm = LayerNorm(dim)
self.model = model
def forward(self, x):
out = self.model(x)
return self.norm(out) + x
# memory mlp proposed in TTT
class MemoryMLP(Module):
def __init__(
self,
dim,
depth,
expansion_factor = 2.
):
super().__init__()
dim_hidden = int(dim * expansion_factor)
dims = (dim, *((dim_hidden,) * (depth - 1)), dim)
self.weights = ParameterList([Parameter(torch.randn(dim_in, dim_out)) for dim_in, dim_out in zip(dims[:-1], dims[1:])])
for weight in self.weights:
nn.init.xavier_uniform_(weight)
def forward(
self,
x
):
for ind, weight in enumerate(self.weights):
is_first = ind == 0
if not is_first:
x = F.gelu(x)
x = x @ weight
return x
# memory mlp, but with gated residual + final projection
class GatedResidualMemoryMLP(Module):
def __init__(
self,
dim,
depth,
expansion_factor = 4.
):
super().__init__()
dim_hidden = int(dim * expansion_factor)
self.weights = ParameterList([
ParameterList([
Parameter(torch.randn(dim, dim_hidden)),
Parameter(torch.randn(dim_hidden, dim)),
Parameter(torch.randn(dim * 2, dim)),
]) for _ in range(depth)
])
self.final_proj = Parameter(torch.randn(dim, dim))
for param in self.parameters():
nn.init.xavier_uniform_(param)
def forward(
self,
x
):
for weight1, weight2, to_gates in self.weights:
res = x
hidden = x @ weight1
hidden = F.gelu(hidden)
branch_out = hidden @ weight2
# gated residual
gates = cat((branch_out, res), dim = -1) @ to_gates
x = res.lerp(branch_out, gates.sigmoid())
return x @ self.final_proj
# memory mlp with factorized weights
# so can tradeoff capacity for smaller chunk sizes
class FactorizedMemoryMLP(Module):
def __init__(
self,
dim,
depth,
k = 32
):
super().__init__()
self.weights = ParameterList([
ParameterList([
Parameter(torch.randn(dim, k)),
Parameter(torch.randn(k, dim)),
]) for _ in range(depth)
])
for weight1, weight2 in self.weights:
nn.init.xavier_uniform_(weight1)
nn.init.xavier_uniform_(weight2)
def forward(
self,
x
):
for ind, (weight1, weight2) in enumerate(self.weights):
is_first = ind == 0
if not is_first:
x = F.gelu(x)
x = x @ weight1 @ weight2
return x
# an MLP modelled after the popular swiglu ff in modern transformers
class MemorySwiGluMLP(Module):
def __init__(
self,
dim,
depth = 1, # default to 2 layer MLP from TTT, depth of 2 would be 4 layer MLP, but done as 2 feedforwards with residual
expansion_factor = 4.
):
super().__init__()
dim_inner = int(dim * expansion_factor * 2 / 3)
weights = []
for _ in range(depth):
weights.append(ParameterList([
Parameter(torch.randn(dim, dim_inner * 2)),
Parameter(torch.randn(dim_inner, dim)),
]))
self.weights = ParameterList(weights)
self.norm = LayerNorm(dim)
def forward(self, x):
for w1, w2 in self.weights:
residual = x
x, gates = (x @ w1).chunk(2, dim = -1)
x = x * F.gelu(gates)
x = x @ w2
x = x + residual
return self.norm(x)
# improvised attention as memory module
class MemoryAttention(Module):
def __init__(
self,
dim,
scale = 8.,
expansion_factor = 2.
):
super().__init__()
self.scale = scale
dim_ff_hidden = int(dim * expansion_factor)
self.weights = ParameterList([
Parameter(torch.randn(dim, dim)), # queries
Parameter(torch.randn(dim, dim)), # keys
Parameter(torch.randn(dim, dim)), # values
Parameter(torch.randn(dim, dim_ff_hidden)), # ff w1
Parameter(torch.randn(dim_ff_hidden, dim)), # ff w2
])
for weight in self.weights:
nn.init.xavier_uniform_(weight)
def forward(self, x):
wq, wk, wv, ffw1, ffw2 = self.weights
q = l2norm(x @ wq)
k = l2norm(x @ wk)
v = x @ wv
attn_out = F.scaled_dot_product_attention(
q, k, v,
scale = self.scale,
is_causal = True
)
# parallel attention + feedforward block
# as in PaLM + Gpt-J
h = F.gelu(x @ ffw1)
ff_out = h @ ffw2
return attn_out + ff_out
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