Remove dead line

timm/layers/__init__.py

@@ -28,7 +28,7 @@ from .linear import Linear
 from .mixed_conv2d import MixedConv2d
 from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp, GlobalResponseNormMlp
 from .non_local_attn import NonLocalAttn, BatNonLocalAttn
-from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
+from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
 from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\
     SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
 from .padding import get_padding, get_same_padding, pad_same

timm/layers/fast_norm.py

@@ -17,6 +17,12 @@ try:
 except ImportError:
     has_apex = False
 
+try:
+    from apex.normalization.fused_layer_norm import fused_rms_norm_affine, fused_rms_norm
+    has_apex_rmsnorm = True
+except ImportError:
+    has_apex_rmsnorm = False
+
 
 # fast (ie lower precision LN) can be disabled with this flag if issues crop up
 _USE_FAST_NORM = False  # defaulting to False for now
@@ -76,3 +82,45 @@ def fast_layer_norm(
 
     with torch.cuda.amp.autocast(enabled=False):
         return F.layer_norm(x, normalized_shape, weight, bias, eps)
+
+
+def rms_norm(
+    x: torch.Tensor,
+    normalized_shape: List[int],
+    weight: Optional[torch.Tensor] = None,
+    eps: float = 1e-5,
+):
+    norm_ndim = len(normalized_shape)
+    if torch.jit.is_scripting():
+        # ndim = len(x.shape)
+        # dims = list(range(ndim - norm_ndim, ndim))  # this doesn't work on pytorch <= 1.13.x
+        # NOTE -ve dims cause torchscript to crash in some cases, out of options to work around
+        assert norm_ndim == 1
+        v = torch.var(x, dim=-1).unsqueeze(-1)  # ts crashes with -ve dim + keepdim=True
+    else:
+        dims = tuple(range(-1, -norm_ndim - 1, -1))
+        v = torch.var(x, dim=dims, keepdim=True)
+    x = x * torch.rsqrt(v + eps)
+    if weight is not None:
+        x = x * weight
+    return x
+
+
+def fast_rms_norm(
+    x: torch.Tensor,
+    normalized_shape: List[int],
+    weight: Optional[torch.Tensor] = None,
+    eps: float = 1e-5,
+) -> torch.Tensor:
+    if torch.jit.is_scripting():
+        # this must be by itself, cannot merge with has_apex_rmsnorm
+        return rms_norm(x, normalized_shape, weight, eps)
+
+    if has_apex_rmsnorm:
+        if weight is None:
+            return fused_rms_norm(x, normalized_shape, eps)
+        else:
+            return fused_rms_norm_affine(x, weight, normalized_shape, eps)
+
+    # fallback
+    return rms_norm(x, normalized_shape, weight, eps)

timm/layers/norm.py

@@ -4,12 +4,14 @@ Norm layer definitions that support fast norm and consistent channel arg order (
 
 Hacked together by / Copyright 2022 Ross Wightman
 """
+import numbers
+from typing import Tuple
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm
+from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm
 
 
 class GroupNorm(nn.GroupNorm):
@@ -115,3 +117,39 @@ class LayerNormExp2d(nn.LayerNorm):
         else:
             x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
         return x
+
+
+class RmsNorm(nn.Module):
+    """ RmsNorm w/ fast (apex) norm if available
+    """
+    __constants__ = ['normalized_shape', 'eps', 'elementwise_affine']
+    normalized_shape: Tuple[int, ...]
+    eps: float
+    elementwise_affine: bool
+
+    def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__()
+        normalized_shape = channels
+        if isinstance(normalized_shape, numbers.Integral):
+            # mypy error: incompatible types in assignment
+            normalized_shape = (normalized_shape,)  # type: ignore[assignment]
+        self.normalized_shape = tuple(normalized_shape)  # type: ignore[arg-type]
+        self.eps = eps
+        self.elementwise_affine = affine
+        if self.elementwise_affine:
+            self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
+        else:
+            self.register_parameter('weight', None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        if self.elementwise_affine:
+            nn.init.ones_(self.weight)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # NOTE fast norm fallback needs our rms norm impl, so both paths through here.
+        # Since there is no built-in PyTorch impl, always use APEX RmsNorm if is installed.
+        x = fast_rms_norm(x, self.normalized_shape, self.weight, self.eps)
+        return x

timm/layers/pos_embed_rel.py

@@ -83,8 +83,8 @@ def gen_relative_log_coords(
         pretrained_win_size: Tuple[int, int] = (0, 0),
         mode='swin',
 ):
-    assert mode in ('swin', 'cr', 'rw')
-    # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well
+    assert mode in ('swin', 'cr')
+    # as per official swin-v2 impl, supporting timm specific 'cr' log coords as well
     relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32)
     relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32)
     relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
@@ -100,18 +100,9 @@ def gen_relative_log_coords(
         relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
             1.0 + relative_coords_table.abs()) / math.log2(8)
     else:
-        if mode == 'rw':
-            # cr w/ window size normalization -> [-1,1] log coords
-            relative_coords_table[:, :, 0] /= (win_size[0] - 1)
-            relative_coords_table[:, :, 1] /= (win_size[1] - 1)
-            relative_coords_table *= 8  # scale to -8, 8
-            relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-                1.0 + relative_coords_table.abs())
-            relative_coords_table /= math.log2(9)   # -> [-1, 1]
-        else:
-            # mode == 'cr'
-            relative_coords_table = torch.sign(relative_coords_table) * torch.log(
-                1.0 + relative_coords_table.abs())
+        # mode == 'cr'
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log(
+            1.0 + relative_coords_table.abs())
 
     return relative_coords_table
 
@@ -141,10 +132,6 @@ class RelPosMlp(nn.Module):
             self.bias_act = nn.Sigmoid()
             self.bias_gain = 16
             mlp_bias = (True, False)
-        elif mode == 'rw':
-            self.bias_act = nn.Tanh()
-            self.bias_gain = 4
-            mlp_bias = True
         else:
             self.bias_act = nn.Identity()
             self.bias_gain = None

timm/models/maxxvit.py

@@ -42,6 +42,7 @@ from typing import Callable, Optional, Union, Tuple, List
 
 import torch
 from torch import nn
+from torch.jit import Final
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.layers import Mlp, ConvMlp, DropPath, LayerNorm, ClassifierHead, NormMlpClassifierHead
@@ -140,6 +141,8 @@ class MaxxVitCfg:
 
 
 class Attention2d(nn.Module):
+    fast_attn: Final[bool]
+
     """ multi-head attention for 2D NCHW tensors"""
     def __init__(
             self,
@@ -160,6 +163,7 @@ class Attention2d(nn.Module):
         self.dim_head = dim_head
         self.head_first = head_first
         self.scale = dim_head ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
 
         self.qkv = nn.Conv2d(dim, dim_attn * 3, 1, bias=bias)
         self.rel_pos = rel_pos_cls(num_heads=self.num_heads) if rel_pos_cls else None
@@ -175,15 +179,31 @@ class Attention2d(nn.Module):
         else:
             q, k, v = self.qkv(x).reshape(B, 3, self.num_heads, self.dim_head, -1).unbind(1)
 
-        attn = (q.transpose(-2, -1) @ k) * self.scale
-        if self.rel_pos is not None:
-            attn = self.rel_pos(attn)
-        elif shared_rel_pos is not None:
-            attn = attn + shared_rel_pos
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
+        if self.fast_attn:
+            if self.rel_pos is not None:
+                attn_bias = self.rel_pos.get_bias()
+            elif shared_rel_pos is not None:
+                attn_bias = shared_rel_pos
+            else:
+                attn_bias = None
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q.transpose(-1, -2),
+                k.transpose(-1, -2),
+                v.transpose(-1, -2),
+                attn_mask=attn_bias,
+                dropout_p=self.attn_drop.p,
+            ).transpose(-1, -2).reshape(B, -1, H, W)
+        else:
+            q = q * self.scale
+            attn = q.transpose(-2, -1) @ k
+            if self.rel_pos is not None:
+                attn = self.rel_pos(attn)
+            elif shared_rel_pos is not None:
+                attn = attn + shared_rel_pos
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (v @ attn.transpose(-2, -1)).view(B, -1, H, W)
 
-        x = (v @ attn.transpose(-2, -1)).view(B, -1, H, W)
         x = self.proj(x)
         x = self.proj_drop(x)
         return x
@@ -191,6 +211,8 @@ class Attention2d(nn.Module):
 
 class AttentionCl(nn.Module):
     """ Channels-last multi-head attention (B, ..., C) """
+    fast_attn: Final[bool]
+
     def __init__(
             self,
             dim: int,
@@ -211,6 +233,7 @@ class AttentionCl(nn.Module):
         self.dim_head = dim_head
         self.head_first = head_first
         self.scale = dim_head ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
 
         self.qkv = nn.Linear(dim, dim_attn * 3, bias=bias)
         self.rel_pos = rel_pos_cls(num_heads=self.num_heads) if rel_pos_cls else None
@@ -227,15 +250,30 @@ class AttentionCl(nn.Module):
         else:
             q, k, v = self.qkv(x).reshape(B, -1, 3, self.num_heads, self.dim_head).transpose(1, 3).unbind(2)
 
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        if self.rel_pos is not None:
-            attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
-        elif shared_rel_pos is not None:
-            attn = attn + shared_rel_pos
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(restore_shape + (-1,))
+        if self.fast_attn:
+            if self.rel_pos is not None:
+                attn_bias = self.rel_pos.get_bias()
+            elif shared_rel_pos is not None:
+                attn_bias = shared_rel_pos
+            else:
+                attn_bias = None
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v,
+                attn_mask=attn_bias,
+                dropout_p=self.attn_drop.p,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            if self.rel_pos is not None:
+                attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
+            elif shared_rel_pos is not None:
+                attn = attn + shared_rel_pos
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(restore_shape + (-1,))
         x = self.proj(x)
         x = self.proj_drop(x)
         return x

timm/models/vision_transformer.py

@@ -33,11 +33,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.checkpoint
+from torch.jit import Final
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD, \
     OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
 from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, resample_patch_embed, \
-    resample_abs_pos_embed
+    resample_abs_pos_embed, RmsNorm
 from ._builder import build_model_with_cfg
 from ._manipulate import named_apply, checkpoint_seq, adapt_input_conv
 from ._pretrained import generate_default_cfgs
@@ -51,28 +52,51 @@ _logger = logging.getLogger(__name__)
 
 
 class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+    fast_attn: Final[bool]
+
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            attn_drop=0.,
+            proj_drop=0.,
+            norm_layer=nn.LayerNorm,
+    ):
         super().__init__()
         assert dim % num_heads == 0, 'dim should be divisible by num_heads'
         self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
 
         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
         self.attn_drop = nn.Dropout(attn_drop)
         self.proj = nn.Linear(dim, dim)
         self.proj_drop = nn.Dropout(proj_drop)
 
     def forward(self, x):
         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.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.fast_attn:
+            x = F.scaled_dot_product_attention(
+                q, k, v,
+                dropout_p=self.attn_drop.p,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
 
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = x.transpose(1, 2).reshape(B, N, C)
         x = self.proj(x)
         x = self.proj_drop(x)
         return x
@@ -96,6 +120,7 @@ class Block(nn.Module):
             num_heads,
             mlp_ratio=4.,
             qkv_bias=False,
+            qk_norm=False,
             drop=0.,
             attn_drop=0.,
             init_values=None,
@@ -105,13 +130,25 @@ class Block(nn.Module):
     ):
         super().__init__()
         self.norm1 = norm_layer(dim)
-        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            norm_layer=norm_layer,
+        )
         self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
         self.norm2 = norm_layer(dim)
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop,
+        )
         self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
@@ -129,6 +166,7 @@ class ResPostBlock(nn.Module):
             num_heads,
             mlp_ratio=4.,
             qkv_bias=False,
+            qk_norm=False,
             drop=0.,
             attn_drop=0.,
             init_values=None,
@@ -139,11 +177,24 @@ class ResPostBlock(nn.Module):
         super().__init__()
         self.init_values = init_values
 
-        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            norm_layer=norm_layer,
+        )
         self.norm1 = norm_layer(dim)
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop,
+        )
         self.norm2 = norm_layer(dim)
         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
@@ -161,8 +212,105 @@ class ResPostBlock(nn.Module):
         return x
 
 
-class ParallelBlock(nn.Module):
+class ParallelScalingBlock(nn.Module):
+    """ Parallel ViT block (MLP & Attention in parallel)
+    Based on:
+      'Scaling Vision Transformers to 22 Billion Parameters` - https://arxiv.org/abs/2302.05442
+    """
+    fast_attn: Final[bool]
 
+    def __init__(
+            self,
+            dim,
+            num_heads,
+            mlp_ratio=4.,
+            qkv_bias=False,
+            qk_norm=False,
+            drop=0.,
+            attn_drop=0.,
+            init_values=None,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
+        mlp_hidden_dim = int(mlp_ratio * dim)
+        in_proj_out_dim = mlp_hidden_dim + 3 * dim
+
+        self.in_norm = norm_layer(dim)
+        self.in_proj = nn.Linear(dim, in_proj_out_dim, bias=qkv_bias)
+        self.in_split = [mlp_hidden_dim] + [dim] * 3
+        if qkv_bias:
+            self.register_buffer('qkv_bias', None)
+            self.register_parameter('mlp_bias', None)
+        else:
+            self.register_buffer('qkv_bias', torch.zeros(3 * dim), persistent=False)
+            self.mlp_bias = nn.Parameter(torch.zeros(mlp_hidden_dim))
+
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.attn_out_proj = nn.Linear(dim, dim)
+
+        self.mlp_drop = nn.Dropout(drop)
+        self.mlp_act = act_layer()
+        self.mlp_out_proj = nn.Linear(mlp_hidden_dim, dim)
+
+        self.ls = LayerScale(dim, init_values=init_values) if init_values is not None else nn.Identity()
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        B, N, C = x.shape
+
+        # Combined MLP fc1 & qkv projections
+        y = self.in_norm(x)
+        if self.mlp_bias is not None:
+            # Concat constant zero-bias for qkv w/ trainable mlp_bias.
+            # Appears faster than adding to x_mlp separately
+            y = F.linear(y, self.in_proj.weight, torch.cat((self.qkv_bias, self.mlp_bias)))
+        else:
+            y = self.in_proj(y)
+        x_mlp, q, k, v = torch.split(y, self.in_split, dim=-1)
+
+        # Dot product attention w/ qk norm
+        q = self.q_norm(q.view(B, N, self.num_heads, self.head_dim)).transpose(1, 2)
+        k = self.k_norm(k.view(B, N, self.num_heads, self.head_dim)).transpose(1, 2)
+        v = v.view(B, N, self.num_heads, self.head_dim).transpose(1, 2)
+        if self.fast_attn:
+            x_attn = F.scaled_dot_product_attention(
+                q, k, v,
+                dropout_p=self.attn_drop.p,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x_attn = attn @ v
+        x_attn = x_attn.transpose(1, 2).reshape(B, N, C)
+        x_attn = self.attn_out_proj(x_attn)
+
+        # MLP activation, dropout, fc2
+        x_mlp = self.mlp_act(x_mlp)
+        x_mlp = self.mlp_drop(x_mlp)
+        x_mlp = self.mlp_out_proj(x_mlp)
+
+        # Add residual w/ drop path & layer scale applied
+        y = self.drop_path(self.ls(x_attn + x_mlp))
+        x = x + y
+        return x
+
+
+class ParallelThingsBlock(nn.Module):
+    """ Parallel ViT block (N parallel attention followed by N parallel MLP)
+    Based on:
+      `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
+    """
     def __init__(
             self,
             dim,
@@ -170,6 +318,7 @@ class ParallelBlock(nn.Module):
             num_parallel=2,
             mlp_ratio=4.,
             qkv_bias=False,
+            qk_norm=False,
             init_values=None,
             drop=0.,
             attn_drop=0.,
@@ -184,13 +333,26 @@ class ParallelBlock(nn.Module):
         for _ in range(num_parallel):
             self.attns.append(nn.Sequential(OrderedDict([
                 ('norm', norm_layer(dim)),
-                ('attn', Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)),
+                ('attn', Attention(
+                    dim,
+                    num_heads=num_heads,
+                    qkv_bias=qkv_bias,
+                    qk_norm=qk_norm,
+                    attn_drop=attn_drop,
+                    proj_drop=drop,
+                    norm_layer=norm_layer,
+                )),
                 ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
                 ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
             ])))
             self.ffns.append(nn.Sequential(OrderedDict([
                 ('norm', norm_layer(dim)),
-                ('mlp', Mlp(dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)),
+                ('mlp', Mlp(
+                    dim,
+                    hidden_features=int(dim * mlp_ratio),
+                    act_layer=act_layer,
+                    drop=drop,
+                )),
                 ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
                 ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
             ])))
@@ -232,6 +394,7 @@ class VisionTransformer(nn.Module):
             num_heads=12,
             mlp_ratio=4.,
             qkv_bias=True,
+            qk_norm=False,
             init_values=None,
             class_token=True,
             no_embed_class=False,
@@ -305,6 +468,7 @@ class VisionTransformer(nn.Module):
                 num_heads=num_heads,
                 mlp_ratio=mlp_ratio,
                 qkv_bias=qkv_bias,
+                qk_norm=qk_norm,
                 init_values=init_values,
                 drop=drop_rate,
                 attn_drop=attn_drop_rate,
@@ -641,9 +805,8 @@ def checkpoint_filter_fn(
     """ convert patch embedding weight from manual patchify + linear proj to conv"""
     import re
     out_dict = {}
-    if 'model' in state_dict:
-        # For deit models
-        state_dict = state_dict['model']
+    state_dict = state_dict.get('model', state_dict)
+    state_dict = state_dict.get('state_dict', state_dict)
 
     if 'visual.class_embedding' in state_dict:
         return _convert_openai_clip(state_dict, model)
@@ -1129,6 +1292,10 @@ default_cfgs = generate_default_cfgs({
         url='https://storage.googleapis.com/big_vision/flexivit/vit_b30_i21k_300ep.npz', custom_load=True,
         hf_hub_id='timm/',
         input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
+
+    'vit_base_patch16_xp_224.untrained': _cfg(url=''),
+    'vit_large_patch14_xp_224.untrained': _cfg(url=''),
+    'vit_huge_patch14_xp_224.untrained': _cfg(url=''),
 })
 
 
@@ -1566,7 +1733,7 @@ def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
     Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock)
+        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelThingsBlock)
     model = _create_vision_transformer(
         'vit_small_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
@@ -1577,7 +1744,8 @@ def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
     """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock)
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelThingsBlock)
     model = _create_vision_transformer(
         'vit_base_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
@@ -1625,3 +1793,42 @@ def flexivit_large(pretrained=False, **kwargs):
     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True)
     model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **dict(model_kwargs, **kwargs))
     return model
+
+
+@register_model
+def vit_base_patch16_xp_224(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/14) w/ parallel blocks and qk norm enabled.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, no_embed_class=True,
+        norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_xp_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
+    return model
+
+
+@register_model
+def vit_large_patch14_xp_224(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/14) w/ parallel blocks and qk norm enabled.
+    """
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, no_embed_class=True,
+        norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
+    )
+    model = _create_vision_transformer(
+        'vit_large_patch14_xp_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
+    return model
+
+
+@register_model
+def vit_huge_patch14_xp_224(pretrained=False, **kwargs):
+    """ ViT-Huge model (ViT-H/14) w/ parallel blocks and qk norm enabled.
+    """
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, no_embed_class=True,
+        norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
+    )
+    model = _create_vision_transformer(
+        'vit_huge_patch14_xp_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
+    return model

timm/models/vision_transformer_relpos.py

@@ -11,6 +11,7 @@ from typing import Optional, Tuple
 
 import torch
 import torch.nn as nn
+from torch.jit import Final
 from torch.utils.checkpoint import checkpoint
 
 from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
@@ -25,14 +26,29 @@ _logger = logging.getLogger(__name__)
 
 
 class RelPosAttention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.):
+    fast_attn: Final[bool]
+
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            rel_pos_cls=None,
+            attn_drop=0.,
+            proj_drop=0.,
+            norm_layer=nn.LayerNorm,
+    ):
         super().__init__()
         assert dim % num_heads == 0, 'dim should be divisible by num_heads'
         self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
 
         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
         self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
         self.attn_drop = nn.Dropout(attn_drop)
         self.proj = nn.Linear(dim, dim)
@@ -40,18 +56,35 @@ class RelPosAttention(nn.Module):
 
     def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
         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.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        if self.rel_pos is not None:
-            attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
-        elif shared_rel_pos is not None:
-            attn = attn + shared_rel_pos
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if self.fast_attn:
+            if self.rel_pos is not None:
+                attn_bias = self.rel_pos.get_bias()
+            elif shared_rel_pos is not None:
+                attn_bias = shared_rel_pos
+            else:
+                attn_bias = None
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v,
+                attn_mask=attn_bias,
+                dropout_p=self.attn_drop.p,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            if self.rel_pos is not None:
+                attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
+            elif shared_rel_pos is not None:
+                attn = attn + shared_rel_pos
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
         x = self.proj(x)
         x = self.proj_drop(x)
         return x
@@ -70,18 +103,42 @@ class LayerScale(nn.Module):
 class RelPosBlock(nn.Module):
 
     def __init__(
-            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
-            drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+            self,
+            dim,
+            num_heads,
+            mlp_ratio=4.,
+            qkv_bias=False,
+            qk_norm=False,
+            rel_pos_cls=None,
+            init_values=None,
+            drop=0.,
+            attn_drop=0.,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+    ):
         super().__init__()
         self.norm1 = norm_layer(dim)
         self.attn = RelPosAttention(
-            dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
+            dim,
+            num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            rel_pos_cls=rel_pos_cls,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
         self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
         # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
         self.norm2 = norm_layer(dim)
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop,
+        )
         self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
@@ -94,17 +151,41 @@ class RelPosBlock(nn.Module):
 class ResPostRelPosBlock(nn.Module):
 
     def __init__(
-            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
-            drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+            self,
+            dim,
+            num_heads,
+            mlp_ratio=4.,
+            qkv_bias=False,
+            qk_norm=False,
+            rel_pos_cls=None,
+            init_values=None,
+            drop=0.,
+            attn_drop=0.,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+    ):
         super().__init__()
         self.init_values = init_values
 
         self.attn = RelPosAttention(
-            dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
+            dim,
+            num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            rel_pos_cls=rel_pos_cls,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
         self.norm1 = norm_layer(dim)
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop,
+        )
         self.norm2 = norm_layer(dim)
         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
@@ -144,6 +225,7 @@ class VisionTransformerRelPos(nn.Module):
             num_heads=12,
             mlp_ratio=4.,
             qkv_bias=True,
+            qk_norm=False,
             init_values=1e-6,
             class_token=False,
             fc_norm=False,
@@ -171,6 +253,7 @@ class VisionTransformerRelPos(nn.Module):
             num_heads (int): number of attention heads
             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
             qkv_bias (bool): enable bias for qkv if True
+            qk_norm (bool): Enable normalization of query and key in attention
             init_values: (float): layer-scale init values
             class_token (bool): use class token (default: False)
             fc_norm (bool): use pre classifier norm instead of pre-pool
@@ -197,18 +280,19 @@ class VisionTransformerRelPos(nn.Module):
         self.grad_checkpointing = False
 
         self.patch_embed = embed_layer(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
         feat_size = self.patch_embed.grid_size
 
         rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens)
         if rel_pos_type.startswith('mlp'):
             if rel_pos_dim:
                 rel_pos_args['hidden_dim'] = rel_pos_dim
-            # FIXME experimenting with different relpos log coord configs
             if 'swin' in rel_pos_type:
                 rel_pos_args['mode'] = 'swin'
-            elif 'rw' in rel_pos_type:
-                rel_pos_args['mode'] = 'rw'
             rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
         else:
             rel_pos_cls = partial(RelPosBias, **rel_pos_args)
@@ -223,9 +307,19 @@ class VisionTransformerRelPos(nn.Module):
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
         self.blocks = nn.ModuleList([
             block_fn(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls,
-                init_values=init_values, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i],
-                norm_layer=norm_layer, act_layer=act_layer)
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_norm=qk_norm,
+                rel_pos_cls=rel_pos_cls,
+                init_values=init_values,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+            )
             for i in range(depth)])
         self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()