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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
#
import math
from typing import Optional
import torch
from fairseq2.nn.projection import Linear
from fairseq2.typing import DataType, Device
from torch import Tensor
from torch.nn import Module
from lcm.nn.initialization import parse_activation_fn
class DiTTimestepEncoder(Module):
"""
Embeds scalar timesteps into vector representations.
Based on DiT's `TimestepEmbedder`
https://github.com/facebookresearch/DiT/blob/main/models.py
"""
def __init__(
self,
embedding_dim: int,
frequency_embedding_size: int = 256,
activation_fn_name: str = "silu",
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
):
super().__init__()
self.dtype = dtype
self.device = device
self.embedding_dim = embedding_dim
self.frequency_embedding_size = frequency_embedding_size
self.fc1 = Linear(
frequency_embedding_size,
embedding_dim,
bias=True,
device=device,
dtype=dtype,
)
self.nonlin = parse_activation_fn(activation_fn_name)
self.fc2 = Linear(
embedding_dim,
embedding_dim,
bias=True,
device=device,
dtype=dtype,
)
self.reset_parameters()
def reset_parameters(self) -> None:
"""Reset the parameters and buffers of the module."""
torch.nn.init.normal_(self.fc1.weight, std=0.02)
torch.nn.init.normal_(self.fc2.weight, std=0.02)
if self.fc1.bias is not None:
torch.nn.init.zeros_(self.fc1.bias)
if self.fc2.bias is not None:
torch.nn.init.zeros_(self.fc2.bias)
@staticmethod
def sinusoidal_timestep_embedding(
timestep, frequency_embedding_size, max_period=10000
):
"""
Create sinusoidal timestep embeddings.
:param timestep: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param frequency_embedding_size: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
Based on DiT's `TimestepEmbedder`
https://github.com/facebookresearch/DiT/blob/main/models.py
"""
half = frequency_embedding_size // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=timestep.device)
args = timestep[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if frequency_embedding_size % 2:
embedding = torch.cat(
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
)
return embedding
def forward(self, timesteps: Tensor) -> Tensor:
initial_size = timesteps.size()
flat_timesteps = timesteps.view(-1, 1)
t_freq = self.sinusoidal_timestep_embedding(
flat_timesteps, self.frequency_embedding_size
).to(self.dtype)
t_emb = self.fc1(t_freq)
if self.nonlin is not None:
t_emb = self.nonlin(t_emb)
t_emb = self.fc2(t_emb)
return t_emb.view(*initial_size, self.embedding_dim)
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