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# SPDX-FileCopyrightText: Copyright (c) 2025 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 warnings
import torch
from torch import Tensor
from torch import nn
from itertools import repeat
import collections.abc
from einops import rearrange
from flash_attn import flash_attn_func
try:
# Needed since changing args to function causes recompiles
torch._dynamo.config.cache_size_limit = 1000
from torch.nn.attention.flex_attention import flex_attention as flex_attn_func
flex_attn_func_compiled = torch.compile(flex_attn_func)
except:
warnings.warn("flex_attn is not available")
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import math
from functools import partial
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return x
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f'drop_prob={round(self.drop_prob,3):0.3f}'
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class Attention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
use_qk_norm: bool = False,
) -> None:
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.use_qk_norm = use_qk_norm
if self.use_qk_norm:
norm_layer=partial(nn.RMSNorm, eps=1e-6)
self.q_norm = norm_layer(head_dim)
self.k_norm = norm_layer(head_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x: Tensor) -> Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class CrossAttention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
use_qk_norm: bool = False,
) -> None:
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.to_q = nn.Linear(dim, dim, bias=qkv_bias)
self.to_k = nn.Linear(dim, dim, bias=qkv_bias)
self.to_v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.use_qk_norm = use_qk_norm
if self.use_qk_norm:
norm_layer=partial(nn.RMSNorm, eps=1e-6)
self.q_norm = norm_layer(head_dim)
self.k_norm = norm_layer(head_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x: Tensor, y: Tensor) -> Tensor:
B, N, C = x.shape
# qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
# q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
q = self.to_q(x).reshape(B, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)[0] * self.scale
k = self.to_k(y).reshape(B, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)[0]
v = self.to_v(y).reshape(B, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)[0]
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class MemEffAttentionFlash(Attention):
def forward(self, x: Tensor, attn_bias=None) -> Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = torch.unbind(qkv, 2)
if self.use_qk_norm:
q = self.q_norm(q).to(v.dtype)
k = self.k_norm(k).to(v.dtype)
x = flash_attn_func(q, k, v)
x = x.reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class MemEffCrossAttentionFlash(CrossAttention):
def forward(self, q: Tensor, k: torch.Tensor, v: torch.Tensor, attn_bias=None) -> Tensor:
B, N, C = q.shape
B_k, N_k, C_k = k.shape
B_v, N_v, C_v = v.shape
assert B == B_k == B_v
assert C == C_k == C_v
q = self.to_q(q).reshape(B, N, self.num_heads, C // self.num_heads)
k = self.to_k(k).reshape(B, N_k, self.num_heads, C // self.num_heads)
v = self.to_q(v).reshape(B, N_v, self.num_heads, C // self.num_heads)
if self.use_qk_norm:
q = self.q_norm(q).to(v.dtype)
k = self.k_norm(k).to(v.dtype)
x = flash_attn_func(q, k, v)
x = x.reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class MemEffAttentionFlex(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
flex_attn_block_mask=None,
use_qk_norm=False,
) -> None:
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.use_qk_norm = use_qk_norm
if self.use_qk_norm:
norm_layer=partial(nn.RMSNorm, eps=1e-6)
self.q_norm = norm_layer(head_dim)
self.k_norm = norm_layer(head_dim)
self.apply(self._init_weights)
self.flex_attn_block_mask = flex_attn_block_mask
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x: Tensor, attn_bias=None) -> Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = torch.unbind(qkv, 2)
if self.use_qk_norm:
q = self.q_norm(q).to(v.dtype)
k = self.k_norm(k).to(v.dtype)
q = q.permute(0,2,1,3)
k = k.permute(0,2,1,3)
v = v.permute(0,2,1,3)
x = flex_attn_func_compiled(q, k, v, block_mask=self.flex_attn_block_mask)
x = x.permute(0, 2, 1, 3)
x = x.reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, rope=None,
use_flex_attention=False, flex_attn_block_mask=None, use_qk_norm=False):
super().__init__()
self.norm1 = norm_layer(dim)
if use_flex_attention:
self.attn = MemEffAttentionFlex(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, flex_attn_block_mask=flex_attn_block_mask, use_qk_norm=use_qk_norm)
else:
self.attn = MemEffAttentionFlash(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, use_qk_norm=use_qk_norm)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class CrossAttentionBlock(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, rope=None,
flex_attn_block_mask=None, use_qk_norm=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = MemEffCrossAttentionFlash(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.norm_y = norm_layer(dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x: torch.Tensor, y: torch.Tensor):
y = self.norm_y(y)
x = x + self.drop_path(self.attn(self.norm1(x), y, y))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" just adding _init_weights + position getter compared to timm.models.layers.patch_embed.PatchEmbed"""
def __init__(self, patch_size=8, in_chans=3, embed_dim=1024, norm_layer=nn.LayerNorm, flatten=True, zero_init=False):
super().__init__()
self.patch_size = patch_size
self.flatten = flatten
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
self.apply(self._init_weights)
if zero_init:
self.proj.weight.data.fill_(0.0)
self.proj.bias.data.fill_(0.0)
def forward(self, x):
B, C, H, W = x.shape
assert H % self.patch_size[0] == 0, f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
assert W % self.patch_size[1] == 0, f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
class PatchEmbed3D(nn.Module):
""" just adding _init_weights + position getter compared to timm.models.layers.patch_embed.PatchEmbed"""
def __init__(self, patch_size=8, in_chans=3, embed_dim=1024, norm_layer=nn.LayerNorm, flatten=True, zero_init=False, padding=0, stride=None):
super().__init__()
patch_size = to_3tuple(patch_size)
self.patch_size = patch_size
self.flatten = flatten
if stride is None:
stride = patch_size
self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
self.apply(self._init_weights)
if zero_init:
self.proj.weight.data.fill_(0.0)
self.proj.bias.data.fill_(0.0)
def forward(self, x):
B, T, C, H, W = x.shape
x = rearrange(x, 'b t c h w -> b c t h w')
x = self.proj(x)
if self.flatten:
x = rearrange(x, 'b c t h w -> b (t h w) c')
x = self.norm(x)
return x
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Conv3d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_() |