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import math
from typing import Optional
import torch
import torch.nn as nn
from shap_e.models.nn.checkpoint import checkpoint
from .transformer import MLP, Transformer, init_linear
from .util import timestep_embedding
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
n_data: int,
width: int,
heads: int,
init_scale: float,
data_width: Optional[int] = None,
):
super().__init__()
self.n_ctx = n_ctx
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, device=device, dtype=dtype)
self.c_kv = nn.Linear(self.data_width, width * 2, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadCrossAttention(
device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, n_data=n_data
)
init_linear(self.c_q, init_scale)
init_linear(self.c_kv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadCrossAttention(nn.Module):
def __init__(
self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, n_data: int
):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_ctx = n_ctx
self.n_data = n_data
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(math.sqrt(attn_ch))
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
n_data: int,
width: int,
heads: int,
data_width: Optional[int] = None,
init_scale: float = 1.0,
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
init_scale=init_scale,
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x
class SimplePerceiver(nn.Module):
"""
Only does cross attention
"""
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
n_data: int,
width: int,
layers: int,
heads: int,
init_scale: float = 0.25,
data_width: Optional[int] = None,
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
init_scale = init_scale * math.sqrt(1.0 / width)
self.resblocks = nn.ModuleList(
[
ResidualCrossAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
n_data=n_data,
width=width,
heads=heads,
init_scale=init_scale,
data_width=data_width,
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor, data: torch.Tensor):
for block in self.resblocks:
x = block(x, data)
return x
class PointDiffusionPerceiver(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
input_channels: int = 3,
output_channels: int = 3,
n_ctx: int = 1024,
n_latent: int = 128,
width: int = 512,
encoder_layers: int = 12,
latent_layers: int = 12,
decoder_layers: int = 12,
heads: int = 8,
init_scale: float = 0.25,
):
super().__init__()
self.time_embed = MLP(
device=device, dtype=dtype, width=width, init_scale=init_scale * math.sqrt(1.0 / width)
)
self.latent_embed = MLP(
device=device, dtype=dtype, width=width, init_scale=init_scale * math.sqrt(1.0 / width)
)
self.n_latent = n_latent
self.ln_pre = nn.LayerNorm(width, device=device, dtype=dtype)
self.encoder = SimplePerceiver(
device=device,
dtype=dtype,
n_ctx=n_latent,
n_data=n_ctx,
width=width,
layers=encoder_layers,
heads=heads,
init_scale=init_scale,
)
self.processor = Transformer(
device=device,
dtype=dtype,
n_ctx=n_latent,
width=width,
layers=latent_layers,
heads=heads,
init_scale=init_scale,
)
self.decoder = SimplePerceiver(
device=device,
dtype=dtype,
n_ctx=n_ctx,
n_data=n_latent,
width=width,
layers=decoder_layers,
heads=heads,
init_scale=init_scale,
)
self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
self.input_proj = nn.Linear(input_channels, width, device=device, dtype=dtype)
self.output_proj = nn.Linear(width, output_channels, device=device, dtype=dtype)
with torch.no_grad():
self.output_proj.weight.zero_()
self.output_proj.bias.zero_()
def forward(self, x: torch.Tensor, t: torch.Tensor):
"""
:param x: an [N x C x T] tensor.
:param t: an [N] tensor.
:return: an [N x C' x T] tensor.
"""
assert x.shape[-1] == self.decoder.n_ctx
t_embed = self.time_embed(timestep_embedding(t, self.encoder.width))
data = self.input_proj(x.permute(0, 2, 1)) + t_embed[:, None]
data = self.ln_pre(data)
l = torch.arange(self.n_latent).to(x.device)
h = self.latent_embed(timestep_embedding(l, self.decoder.width))
h = h.unsqueeze(0).repeat(x.shape[0], 1, 1)
h = self.encoder(h, data)
h = self.processor(h)
h = self.decoder(data, h)
h = self.ln_post(h)
h = self.output_proj(h)
return h.permute(0, 2, 1)
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