# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from typing import Optional
from ..functional import (ACT2FN, Tensor, chunk, group_norm, layer_norm,
rms_norm, unsqueeze)
from ..mapping import Mapping
from ..module import Module
from ..parameter import Parameter
from .embedding import CombinedTimestepLabelEmbeddings, Embedding
from .linear import Linear
[docs]
class LayerNorm(Module):
def __init__(self,
normalized_shape,
eps=1e-05,
elementwise_affine=True,
bias=True,
dtype=None,
tp_size=1,
tp_dim=-1):
super().__init__()
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape, )
self.normalized_shape = tuple(normalized_shape)
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
if bias:
self.bias = Parameter(shape=self.normalized_shape, dtype=dtype)
else:
self.register_parameter('bias', None)
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.eps = eps
self.dtype = dtype
self.tp_size = tp_size
self.tp_dim = tp_dim
[docs]
def forward(self, x, normalized_shape=None):
weight = 1. if self.weight is None else self.weight.value
bias = 0. if self.bias is None else self.bias.value
if normalized_shape is None:
normalized_shape = self.normalized_shape
return layer_norm(x, normalized_shape, weight, bias, self.eps)
[docs]
class RmsNorm(Module):
def __init__(self,
normalized_shape,
num_groups=1,
eps=1e-06,
elementwise_affine=True,
dtype=None):
super().__init__()
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape, )
self.normalized_shape = tuple(normalized_shape)
self.elementwise_affine = elementwise_affine
self.num_groups = num_groups
num_channels = normalized_shape[-1]
if num_channels % num_groups != 0:
raise ValueError('num_channels must be divisible by num_groups')
if self.elementwise_affine:
self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
else:
self.register_parameter('weight', None)
self.eps = eps
self.dtype = dtype
[docs]
def forward(self, x, normalized_shape=None):
weight = None if self.weight is None else self.weight.value
if normalized_shape is None:
normalized_shape = self.normalized_shape
return rms_norm(x, normalized_shape, self.num_groups, weight, self.eps)
[docs]
class GroupNorm(Module):
def __init__(self,
num_groups,
num_channels,
eps=1e-05,
affine=True,
dtype=None):
super().__init__()
if num_channels % num_groups != 0:
raise ValueError('num_channels must be divisible by num_groups')
self.num_groups = num_groups
self.num_channels = num_channels
self.affine = affine
if self.affine:
self.weight = Parameter(shape=(self.num_channels, ), dtype=dtype)
self.bias = Parameter(shape=(self.num_channels, ), dtype=dtype)
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.eps = eps
[docs]
def forward(self, x):
weight = None if self.weight is None else self.weight.value
bias = None if self.bias is None else self.bias.value
return group_norm(x, self.num_groups, weight, bias, self.eps)
[docs]
class AdaLayerNorm(Module):
def __init__(self,
embedding_dim: int,
num_embeddings: Optional[int] = None,
output_dim: Optional[int] = None,
norm_elementwise_affine: bool = False,
norm_eps: float = 1e-5,
chunk_dim: int = 0,
mapping=Mapping(),
dtype=None):
super().__init__()
self.chunk_dim = chunk_dim
output_dim = output_dim or embedding_dim * 2
if num_embeddings is not None:
self.emb = Embedding(num_embeddings, embedding_dim, dtype=dtype)
else:
self.emb = None
self.silu = ACT2FN['silu']
self.linear = Linear(embedding_dim,
output_dim,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
self.norm = LayerNorm(output_dim // 2,
eps=norm_eps,
elementwise_affine=norm_elementwise_affine,
dtype=dtype)
[docs]
def forward(self,
x: Tensor,
timestep: Optional[Tensor] = None,
temb: Optional[Tensor] = None):
assert timestep is not None or temb is not None
if self.emb is not None and timestep is not None:
temb = self.emb(timestep)
temb = self.linear(self.silu(temb))
if self.chunk_dim == 1:
shift, scale = chunk(temb, 2, dim=1)
shift = unsqueeze(shift, 1)
scale = unsqueeze(scale, 1)
else:
scale, shift = chunk(temb, 2, dim=0)
x = self.norm(x) * (1 + scale) + shift
return x
[docs]
class AdaLayerNormZero(Module):
def __init__(self,
embedding_dim: int,
num_embeddings: Optional[int] = None,
norm_type: str = "layer_norm",
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
if num_embeddings is not None:
self.emb = CombinedTimestepLabelEmbeddings(num_embeddings,
embedding_dim,
dtype=dtype)
else:
self.emb = None
self.silu = ACT2FN['silu']
self.linear = Linear(embedding_dim,
6 * embedding_dim,
bias=bias,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim,
elementwise_affine=False,
eps=1e-6,
dtype=dtype)
elif norm_type == "fp32_layer_norm":
self.norm = LayerNorm(embedding_dim,
elementwise_affine=False,
bias=False,
dtype=dtype)
else:
raise ValueError(
f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
)
[docs]
def forward(self,
x: Tensor,
timestep: Optional[Tensor] = None,
class_labels: Optional[Tensor] = None,
hidden_dtype: str = None,
emb: Optional[Tensor] = None):
assert emb is not None or self.emb is not None
if self.emb is not None:
emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = chunk(
emb, 6, dim=1)
x = self.norm(x) * (1 + unsqueeze(scale_msa, 1)) + unsqueeze(
shift_msa, 1)
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
[docs]
class AdaLayerNormZeroSingle(Module):
def __init__(self,
embedding_dim: int,
norm_type: str = "layer_norm",
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.silu = ACT2FN['silu']
self.linear = Linear(embedding_dim,
3 * embedding_dim,
bias=bias,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim,
elementwise_affine=False,
eps=1e-6)
else:
raise ValueError(
f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
)
[docs]
def forward(self, x: Tensor, emb: Optional[Tensor] = None):
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa = chunk(emb, 3, dim=1)
x = self.norm(x) * (1 + unsqueeze(scale_msa, 1)) + unsqueeze(
shift_msa, 1)
return x, gate_msa
[docs]
class AdaLayerNormContinuous(Module):
def __init__(self,
embedding_dim: int,
conditioning_embedding_dim: int,
elementwise_affine: bool = True,
eps: float = 1e-5,
bias: bool = True,
norm_type: str = "layer_norm",
mapping=Mapping(),
dtype=None):
super().__init__()
self.silu = ACT2FN['silu']
self.linear = Linear(conditioning_embedding_dim,
embedding_dim * 2,
bias=bias,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim,
eps=eps,
elementwise_affine=elementwise_affine,
bias=bias,
dtype=dtype)
elif norm_type == "rms_norm":
self.norm = RmsNorm(embedding_dim,
eps=eps,
elementwise_affine=elementwise_affine,
dtype=dtype)
else:
raise ValueError(f"unknown norm_type {norm_type}")
[docs]
def forward(self, x: Tensor, conditioning_embedding: Tensor):
# convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
emb = self.linear(self.silu(conditioning_embedding).cast(x.dtype))
scale, shift = chunk(emb, 2, dim=1)
x = self.norm(x) * unsqueeze((1 + scale), 1) + unsqueeze(shift, 1)
return x
[docs]
class SD35AdaLayerNormZeroX(Module):
def __init__(self,
embedding_dim: int,
norm_type: str = "layer_norm",
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.silu = ACT2FN['silu']
self.linear = Linear(embedding_dim,
9 * embedding_dim,
bias=bias,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim,
elementwise_affine=False,
eps=1e-6,
dtype=dtype)
else:
raise ValueError(
f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm'."
)
[docs]
def forward(self, hidden_states: Tensor, emb: Tensor):
emb = self.linear(self.silu(emb).cast(hidden_states.dtype))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = chunk(
emb, 9, dim=1)
norm_hidden_states = self.norm(hidden_states)
hidden_states = norm_hidden_states * (
1 + unsqueeze(scale_msa, 1)) + unsqueeze(shift_msa, 1)
norm_hidden_states2 = norm_hidden_states * (
1 + unsqueeze(scale_msa2, 1)) + unsqueeze(shift_msa2, 1)
return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2
