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# Copyright 2024 the Llamole team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import math
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t = t.view(-1)
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_freq = t_freq.to(dtype=next(self.mlp.parameters()).dtype)
t_emb = self.mlp(t_freq)
return t_emb
class ConditionEmbedder(nn.Module):
def __init__(self, input_size, hidden_size, dropout_prob):
super().__init__()
self.embedding_drop = nn.Embedding(input_size, hidden_size)
self.mlps = nn.ModuleList([
nn.Sequential(
nn.Linear(1, hidden_size, bias=True),
nn.Softmax(dim=1),
nn.Linear(hidden_size, hidden_size, bias=False)
) for _ in range(input_size)
])
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
def forward(self, labels, train, unconditioned):
embeddings = 0
for dim in range(labels.shape[1]):
label = labels[:, dim]
if unconditioned:
drop_ids = torch.ones_like(label).bool()
else:
drop_ids = torch.isnan(label)
if train:
random_tensor = torch.rand(label.shape).type_as(labels)
probability_mask = random_tensor < self.dropout_prob
drop_ids = drop_ids | probability_mask
label = label.unsqueeze(1)
embedding = torch.zeros((label.shape[0], self.hidden_size)).type_as(labels)
mlp_out = self.mlps[dim](label[~drop_ids])
embedding[~drop_ids] = mlp_out.type_as(embedding)
embedding[drop_ids] += self.embedding_drop.weight[dim]
if train:
embedding = embedding + torch.randn_like(embedding)
embeddings += embedding
return embeddings
class TextEmbedder(nn.Module):
def __init__(self, input_size, hidden_size, dropout_prob):
super().__init__()
self.embedding_drop = nn.Embedding(1, hidden_size)
self.linear = nn.Linear(input_size, hidden_size)
self.dropout_prob = dropout_prob
self.hidden_size = hidden_size
def forward(self, text_emb, train, unconditioned):
if unconditioned:
drop_ids = torch.ones(text_emb.shape[0]).bool().to(text_emb.device)
else:
drop_ids = torch.isnan(text_emb.sum(dim=1))
if train:
random_tensor = torch.rand(text_emb.shape[0]).type_as(text_emb)
probability_mask = random_tensor < self.dropout_prob
drop_ids = drop_ids | probability_mask
embeddings = torch.zeros((text_emb.shape[0], self.hidden_size)).type_as(text_emb)
linear_out = self.linear(text_emb[~drop_ids])
embeddings[~drop_ids] = linear_out.type_as(embeddings)
embeddings[drop_ids] += self.embedding_drop.weight[0]
return embeddings |