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# 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.
import math
from typing import Optional
from ..._common import precision
from ...functional import geglu, matmul, softmax, split
from ...layers import Conv2d, GroupNorm, LayerNorm, Linear
from ...module import Module, ModuleList
class AttentionBlock(Module):
def __init__(self,
channels: int,
num_head_channels: Optional[int] = None,
num_groups: int = 32,
rescale_output_factor: float = 1.0,
eps: float = 1e-5):
super().__init__()
self.channels = channels
self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
self.num_head_size = num_head_channels
self.group_norm = GroupNorm(num_channels=channels,
num_groups=num_groups,
eps=eps,
affine=True)
self.qkv = Linear(channels, channels * 3)
self.rescale_output_factor = rescale_output_factor
self.proj_attn = Linear(channels, channels, 1)
def transpose_for_scores(self, projection):
new_projection_shape = projection.size()[:-1] + (self.num_heads,
self.num_head_size)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(
[0, 2, 1, 3])
return new_projection
def forward(self, hidden_states):
assert not hidden_states.is_dynamic()
residual = hidden_states
batch, channel, height, width = hidden_states.size()
# norm
hidden_states = self.group_norm(hidden_states)
hidden_states = hidden_states.view([batch, channel,
height * width]).transpose(1, 2)
# proj to q, k, v
qkv_proj = self.qkv(hidden_states)
query_proj, key_proj, value_proj = split(qkv_proj, channel, dim=2)
# transpose
query_states = self.transpose_for_scores(query_proj)
key_states = self.transpose_for_scores(key_proj)
value_states = self.transpose_for_scores(value_proj)
# get scores
with precision('float32'):
attention_scores = matmul(query_states,
(key_states).transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(
self.channels / self.num_heads)
attention_probs = softmax(attention_scores, dim=-1)
# compute attention output
hidden_states = matmul(attention_probs, value_states)
hidden_states = hidden_states.permute([0, 2, 1, 3])
new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels, )
hidden_states = hidden_states.view(new_hidden_states_shape)
# compute next hidden_states
hidden_states = self.proj_attn(hidden_states)
hidden_states = hidden_states.transpose(-1, -2).view(
[batch, channel, height, width])
# res connect and rescale
hidden_states = (hidden_states + residual) / self.rescale_output_factor
return hidden_states
def _transpose_for_scores(tensor, heads):
batch_size, seq_len, dim = tensor.size()
tensor = tensor.view([batch_size, seq_len, heads, dim // heads])
tensor = tensor.permute([0, 2, 1, 3])
return tensor
def _attention(query, key, value, scale):
attention_scores = matmul(query, key.transpose(-1, -2))
attention_scores = attention_scores * scale
attention_probs = softmax(attention_scores, dim=-1)
hidden_states = matmul(attention_probs, value)
hidden_states = hidden_states.permute([0, 2, 1, 3])
return hidden_states
class SelfAttention(Module):
def __init__(self, query_dim: int, heads: int = 8, dim_head: int = 64):
super().__init__()
self.inner_dim = dim_head * heads
self.scale = dim_head**-0.5
self.heads = heads
self._slice_size = None
self.to_qkv = Linear(query_dim, 3 * self.inner_dim, bias=False)
self.to_out = Linear(self.inner_dim, query_dim)
def forward(self, hidden_states, mask=None):
assert not hidden_states.is_dynamic()
qkv = self.to_qkv(hidden_states)
query, key, value = split(qkv, self.inner_dim, dim=2)
query = _transpose_for_scores(query, self.heads)
key = _transpose_for_scores(key, self.heads)
value = _transpose_for_scores(value, self.heads)
hidden_states = _attention(query, key, value, self.scale)
batch_size, seq_len, head_size, head_dim = hidden_states.size()
hidden_states = hidden_states.view(
[batch_size, seq_len, head_size * head_dim])
return self.to_out(hidden_states)
class CrossAttention(Module):
def __init__(self,
query_dim: int,
context_dim: Optional[int] = None,
heads: int = 8,
dim_head: int = 64):
super().__init__()
self.inner_dim = dim_head * heads
context_dim = context_dim if context_dim is not None else query_dim
self.scale = dim_head**-0.5
self.heads = heads
self._slice_size = None
self.to_q = Linear(query_dim, self.inner_dim, bias=False)
self.to_kv = Linear(context_dim, 2 * self.inner_dim, bias=False)
self.to_out = Linear(self.inner_dim, query_dim)
def forward(self, hidden_states, context=None, mask=None):
assert not hidden_states.is_dynamic()
query = self.to_q(hidden_states)
is_cross_attn = context is not None
context = context if is_cross_attn else hidden_states
assert not context.is_dynamic()
kv = self.to_kv(context)
query = _transpose_for_scores(query, self.heads)
key, value = split(kv, self.inner_dim, dim=2)
key = _transpose_for_scores(key, self.heads)
value = _transpose_for_scores(value, self.heads)
hidden_states = _attention(query, key, value, self.scale)
batch_size, seq_len, head_size, head_dim = hidden_states.size()
hidden_states = hidden_states.view(
[batch_size, seq_len, head_size * head_dim])
return self.to_out(hidden_states)
class FeedForward(Module):
def __init__(self, dim: int, dim_out: Optional[int] = None, mult: int = 4):
super().__init__()
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
self.proj_in = Linear(dim, inner_dim * 2)
self.proj_out = Linear(inner_dim, dim_out)
def forward(self, hidden_states):
x = self.proj_in(hidden_states)
x = geglu(x)
return self.proj_out(x)
class BasicTransformerBlock(Module):
def __init__(
self,
dim: int,
n_heads: int,
d_head: int,
context_dim: Optional[int] = None,
):
super().__init__()
self.attn1 = SelfAttention(query_dim=dim,
heads=n_heads,
dim_head=d_head) # is a self-attention
self.ff = FeedForward(dim)
self.attn2 = CrossAttention(
query_dim=dim,
context_dim=context_dim,
heads=n_heads,
dim_head=d_head) # is self-attn if context is none
self.norm1 = LayerNorm(dim)
self.norm2 = LayerNorm(dim)
self.norm3 = LayerNorm(dim)
def forward(self, hidden_states, context=None):
hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
hidden_states = self.attn2(self.norm2(hidden_states),
context=context) + hidden_states
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
return hidden_states
class Transformer2DModel(Module):
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
num_layers: int = 1,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = None,
):
super().__init__()
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
self.norm = GroupNorm(num_groups=norm_num_groups,
num_channels=in_channels,
eps=1e-6,
affine=True)
self.proj_in = Conv2d(in_channels,
inner_dim,
kernel_size=(1, 1),
stride=(1, 1),
padding=(0, 0))
self.transformer_blocks = ModuleList([
BasicTransformerBlock(inner_dim,
num_attention_heads,
attention_head_dim,
context_dim=cross_attention_dim)
for d in range(num_layers)
])
self.proj_out = Conv2d(inner_dim,
in_channels,
kernel_size=(1, 1),
stride=(1, 1),
padding=(0, 0))
def forward(self, hidden_states, context=None):
assert not hidden_states.is_dynamic()
batch, _, height, weight = hidden_states.size()
residual = hidden_states
hidden_states = self.norm(hidden_states)
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.size()[1]
hidden_states = hidden_states.permute([0, 2, 3, 1]).view(
[batch, height * weight, inner_dim])
for block in self.transformer_blocks:
hidden_states = block(hidden_states, context=context)
hidden_states = hidden_states.view([batch, height, weight,
inner_dim]).permute([0, 3, 1, 2])
hidden_states = self.proj_out(hidden_states)
return hidden_states + residual
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