More work on FastViT, use own impl of MobileOne, validation working with remapped weight, more refactor TODO

timm/models/fastvit.py

@@ -12,9 +12,255 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.layers import DropPath, trunc_normal_
+from timm.layers import DropPath, trunc_normal_, create_conv2d, ConvNormAct, SqueezeExcite, use_fused_attn
 from ._registry import register_model
-from .byobnet import MobileOneBlock
+
+
+def num_groups(group_size, channels):
+    if not group_size:  # 0 or None
+        return 1  # normal conv with 1 group
+    else:
+        # NOTE group_size == 1 -> depthwise conv
+        assert channels % group_size == 0
+        return channels // group_size
+
+
+class MobileOneBlock(nn.Module):
+    """MobileOne building block.
+
+    This block has a multi-branched architecture at train-time
+    and plain-CNN style architecture at inference time
+    For more details, please refer to our paper:
+    `An Improved One millisecond Mobile Backbone` -
+    https://arxiv.org/pdf/2206.04040.pdf
+    """
+
+    def __init__(
+        self,
+        in_chs: int,
+        out_chs: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+        group_size: int = 0,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        use_act: bool = True,
+        use_scale_branch: bool = True,
+        num_conv_branches: int = 1,
+        act_layer: nn.Module = nn.GELU,
+    ) -> None:
+        """Construct a MobileOneBlock module.
+
+        Args:
+            in_chs: Number of channels in the input.
+            out_chs: Number of channels produced by the block.
+            kernel_size: Size of the convolution kernel.
+            stride: Stride size.
+            dilation: Kernel dilation factor.
+            groups: Group number.
+            inference_mode: If True, instantiates model in inference mode.
+            use_se: Whether to use SE-ReLU activations.
+            use_act: Whether to use activation. Default: ``True``
+            use_scale_branch: Whether to use scale branch. Default: ``True``
+            num_conv_branches: Number of linear conv branches.
+        """
+        super(MobileOneBlock, self).__init__()
+        self.inference_mode = inference_mode
+        self.groups = num_groups(group_size, in_chs)
+        self.stride = stride
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.in_chs = in_chs
+        self.out_chs = out_chs
+        self.num_conv_branches = num_conv_branches
+
+        # Check if SE-ReLU is requested
+        self.se = SqueezeExcite(out_chs) if use_se else nn.Identity()
+
+        if inference_mode:
+            self.reparam_conv = create_conv2d(
+                in_chs,
+                out_chs,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                groups=self.groups,
+                bias=True,
+            )
+        else:
+            # Re-parameterizable skip connection
+            self.reparam_conv = None
+
+            self.rbr_skip = (
+                nn.BatchNorm2d(num_features=in_chs)
+                if out_chs == in_chs and stride == 1
+                else None
+            )
+
+            # Re-parameterizable conv branches
+            if num_conv_branches > 0:
+                rbr_conv = list()
+                for _ in range(self.num_conv_branches):
+                    rbr_conv.append(ConvNormAct(
+                        self.in_chs,
+                        self.out_chs,
+                        kernel_size=kernel_size,
+                        stride=self.stride,
+                        groups=self.groups,
+                        apply_act=False,
+                    ))
+                self.rbr_conv = nn.ModuleList(rbr_conv)
+            else:
+                self.rbr_conv = None
+
+            # Re-parameterizable scale branch
+            self.rbr_scale = None
+            if kernel_size > 1 and use_scale_branch:
+                self.rbr_scale = ConvNormAct(
+                    self.in_chs,
+                    self.out_chs,
+                    kernel_size=1,
+                    stride=self.stride,
+                    groups=self.groups,
+                    apply_act=False
+                )
+
+        self.act = act_layer() if use_act else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        # Inference mode forward pass.
+        if self.reparam_conv is not None:
+            return self.act(self.se(self.reparam_conv(x)))
+
+        # Multi-branched train-time forward pass.
+        # Skip branch output
+        identity_out = 0
+        if self.rbr_skip is not None:
+            identity_out = self.rbr_skip(x)
+
+        # Scale branch output
+        scale_out = 0
+        if self.rbr_scale is not None:
+            scale_out = self.rbr_scale(x)
+
+        # Other branches
+        out = scale_out + identity_out
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                out += self.rbr_conv[ix](x)
+
+        return self.act(self.se(out))
+
+    def reparameterize(self):
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
+        https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
+        architecture used at training time to obtain a plain CNN-like structure
+        for inference.
+        """
+        if self.inference_mode:
+            return
+        kernel, bias = self._get_kernel_bias()
+        self.reparam_conv = create_conv2d(
+            in_channels=self.in_chs,
+            out_channels=self.out_chs,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            dilation=self.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = kernel
+        self.reparam_conv.bias.data = bias
+
+        # Delete un-used branches
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("rbr_conv")
+        self.__delattr__("rbr_scale")
+        if hasattr(self, "rbr_skip"):
+            self.__delattr__("rbr_skip")
+
+        self.inference_mode = True
+
+    def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        # get weights and bias of scale branch
+        kernel_scale = 0
+        bias_scale = 0
+        if self.rbr_scale is not None:
+            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
+            # Pad scale branch kernel to match conv branch kernel size.
+            pad = self.kernel_size // 2
+            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
+
+        # get weights and bias of skip branch
+        kernel_identity = 0
+        bias_identity = 0
+        if self.rbr_skip is not None:
+            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
+
+        # get weights and bias of conv branches
+        kernel_conv = 0
+        bias_conv = 0
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
+                kernel_conv += _kernel
+                bias_conv += _bias
+
+        kernel_final = kernel_conv + kernel_scale + kernel_identity
+        bias_final = bias_conv + bias_scale + bias_identity
+        return kernel_final, bias_final
+
+    def _fuse_bn_tensor(
+        self, branch: Union[nn.Sequential, nn.BatchNorm2d]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with preceeding conv layer.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
+
+        Args:
+            branch: Sequence of ops to be fused.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        if isinstance(branch, nn.Sequential):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        else:
+            assert isinstance(branch, nn.BatchNorm2d)
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.in_chs // self.groups
+                kernel_value = torch.zeros(
+                    (self.in_chs, input_dim, self.kernel_size, self.kernel_size),
+                    dtype=branch.weight.dtype,
+                    device=branch.weight.device,
+                )
+                for i in range(self.in_chs):
+                    kernel_value[
+                        i, i % input_dim, self.kernel_size // 2, self.kernel_size // 2
+                    ] = 1
+                self.id_tensor = kernel_value
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
 
 
 class ReparamLargeKernelConv(nn.Module):
@@ -32,10 +278,10 @@ class ReparamLargeKernelConv(nn.Module):
         out_chs: int,
         kernel_size: int,
         stride: int,
-        groups: int,
-        small_kernel: int,
+        group_size: int,
+        small_kernel: Optional[int] = None,
         inference_mode: bool = False,
-        act_layer: nn.Module = nn.GELU(),
+        act_layer: Optional[nn.Module] = None,
     ) -> None:
         """Construct a ReparamLargeKernelConv module.
 
@@ -44,55 +290,63 @@ class ReparamLargeKernelConv(nn.Module):
             out_chs: Number of output channels.
             kernel_size: Kernel size of the large kernel conv branch.
             stride: Stride size. Default: 1
-            groups: Group number. Default: 1
+            group_size: Group size. Default: 1
             small_kernel: Kernel size of small kernel conv branch.
             inference_mode: If True, instantiates model in inference mode. Default: ``False``
             act_layer: Activation module. Default: ``nn.GELU``
         """
         super(ReparamLargeKernelConv, self).__init__()
-
         self.stride = stride
-        self.groups = groups
+        self.groups = num_groups(group_size, in_chs)
         self.in_chs = in_chs
         self.out_chs = out_chs
-        self.act_layer = act_layer
 
         self.kernel_size = kernel_size
         self.small_kernel = small_kernel
-        self.padding = kernel_size // 2
         if inference_mode:
-            self.lkb_reparam = nn.Conv2d(
-                in_chs=in_chs,
-                out_chs=out_chs,
+            self.lkb_reparam = create_conv2d(
+                in_chs,
+                out_chs,
                 kernel_size=kernel_size,
                 stride=stride,
-                padding=self.padding,
                 dilation=1,
-                groups=groups,
+                groups=self.groups,
                 bias=True,
             )
         else:
-            self.lkb_origin = self._conv_bn(
-                kernel_size=kernel_size, padding=self.padding
+            self.lkb_reparam = None
+            self.lkb_origin = ConvNormAct(
+                in_chs,
+                out_chs,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                groups=self.groups,
+                apply_act=False,
             )
             if small_kernel is not None:
                 assert (
                     small_kernel <= kernel_size
                 ), "The kernel size for re-param cannot be larger than the large kernel!"
-                self.small_conv = self._conv_bn(
-                    kernel_size=small_kernel, padding=small_kernel // 2
+                self.small_conv = ConvNormAct(
+                    in_chs,
+                    out_chs,
+                    kernel_size=small_kernel,
+                    stride=self.stride,
+                    groups=self.groups,
+                    apply_act=False,
                 )
+        # FIXME output of this act was not used in original impl, likely due to bug
+        self.act = act_layer() if act_layer is not None else nn.Identity()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply forward pass."""
-        if hasattr(self, "lkb_reparam"):
+        if self.lkb_reparam is not None:
             out = self.lkb_reparam(x)
         else:
             out = self.lkb_origin(x)
-            if hasattr(self, "small_conv"):
-                out += self.small_conv(x)
-
-        self.act_layer(out)
+            if self.small_conv is not None:
+                out = out + self.small_conv(x)
+        out = self.act(out)
         return out
 
     def get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -119,12 +373,11 @@ class ReparamLargeKernelConv(nn.Module):
         for inference.
         """
         eq_k, eq_b = self.get_kernel_bias()
-        self.lkb_reparam = nn.Conv2d(
-            in_chs=self.in_chs,
-            out_chs=self.out_chs,
+        self.lkb_reparam = create_conv2d(
+            self.in_chs,
+            self.out_chs,
             kernel_size=self.kernel_size,
             stride=self.stride,
-            padding=self.padding,
             dilation=self.lkb_origin.conv.dilation,
             groups=self.groups,
             bias=True,
@@ -159,35 +412,11 @@ class ReparamLargeKernelConv(nn.Module):
         t = (gamma / std).reshape(-1, 1, 1, 1)
         return kernel * t, beta - running_mean * gamma / std
 
-    def _conv_bn(self, kernel_size: int, padding: int = 0) -> nn.Sequential:
-        """Helper method to construct conv-batchnorm layers.
-
-        Args:
-            kernel_size: Size of the convolution kernel.
-            padding: Zero-padding size.
-
-        Returns:
-            A nn.Sequential Conv-BN module.
-        """
-        mod_list = nn.Sequential()
-        mod_list.add_module(
-            "conv",
-            nn.Conv2d(
-                in_chs=self.in_chs,
-                out_chs=self.out_chs,
-                kernel_size=kernel_size,
-                stride=self.stride,
-                padding=padding,
-                groups=self.groups,
-                bias=False,
-            ),
-        )
-        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_chs))
-        return mod_list
-
 
 def convolutional_stem(
-    in_chs: int, out_chs: int, inference_mode: bool = False
+        in_chs: int,
+        out_chs: int,
+        inference_mode: bool = False
 ) -> nn.Sequential:
     """Build convolutional stem with MobileOne blocks.
 
@@ -206,8 +435,6 @@ def convolutional_stem(
             kernel_size=3,
             stride=2,
             inference_mode=inference_mode,
-            use_se=False,
-            num_conv_branches=1,
         ),
         MobileOneBlock(
             in_chs=out_chs,
@@ -216,8 +443,6 @@ def convolutional_stem(
             stride=2,
             group_size=1,
             inference_mode=inference_mode,
-            use_se=False,
-            num_conv_branches=1,
         ),
         MobileOneBlock(
             in_chs=out_chs,
@@ -225,8 +450,6 @@ def convolutional_stem(
             kernel_size=1,
             stride=1,            
             inference_mode=inference_mode,
-            use_se=False,
-            num_conv_branches=1,
         ),
     )
 
@@ -237,6 +460,7 @@ class Attention(nn.Module):
     Source modified from:
     https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
     """
+    fused_attn: torch.jit.Final[bool]
 
     def __init__(
         self,
@@ -259,7 +483,8 @@ class Attention(nn.Module):
         assert dim % head_dim == 0, "dim should be divisible by head_dim"
         self.head_dim = head_dim
         self.num_heads = dim // head_dim
-        self.scale = head_dim**-0.5
+        self.scale = head_dim ** -0.5
+        self.fused_attn = use_fused_attn()
 
         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
         self.attn_drop = nn.Dropout(attn_drop)
@@ -267,11 +492,9 @@ class Attention(nn.Module):
         self.proj_drop = nn.Dropout(proj_drop)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        shape = x.shape
-        B, C, H, W = shape
+        B, C, H, W = x.shape
         N = H * W
-        if len(shape) == 4:
-            x = torch.flatten(x, start_dim=2).transpose(-2, -1)  # (B, N, C)
+        x = x.flatten(2).transpose(-2, -1)  # (B, N, C)
         qkv = (
             self.qkv(x)
             .reshape(B, N, 3, self.num_heads, self.head_dim)
@@ -279,16 +502,22 @@ class Attention(nn.Module):
         )
         q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
 
-        # trick here to make q@k.t more stable
-        attn = (q * self.scale) @ k.transpose(-2, -1)
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
+        if self.fused_attn:
+            x = torch.nn.functional.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)
-        if len(shape) == 4:
-            x = x.transpose(-2, -1).reshape(B, C, H, W)
+        x = x.transpose(-2, -1).reshape(B, C, H, W)
 
         return x
 
@@ -314,32 +543,25 @@ class PatchEmbed(nn.Module):
             inference_mode: Flag to instantiate model in inference mode. Default: ``False``
         """
         super().__init__()
-        block = list()
-        block.append(
+        self.proj = nn.Sequential(
             ReparamLargeKernelConv(
                 in_chs=in_chs,
                 out_chs=embed_dim,
                 kernel_size=patch_size,
                 stride=stride,
-                groups=in_chs,
+                group_size=1,
                 small_kernel=3,
                 inference_mode=inference_mode,
-            )
-        )
-        block.append(
+                act_layer=None,  # activation was not used in original impl
+            ),
             MobileOneBlock(
                 in_chs=embed_dim,
                 out_chs=embed_dim,
                 kernel_size=1,
                 stride=1,
-                padding=0,
-                groups=1,
                 inference_mode=inference_mode,
-                use_se=False,
-                num_conv_branches=1,
             )
         )
-        self.proj = nn.Sequential(*block)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.proj(x)
@@ -377,8 +599,8 @@ class RepMixer(nn.Module):
 
         if inference_mode:
             self.reparam_conv = nn.Conv2d(
-                in_chs=self.dim,
-                out_chs=self.dim,
+                self.dim,
+                self.dim,
                 kernel_size=self.kernel_size,
                 stride=1,
                 padding=self.kernel_size // 2,
@@ -386,6 +608,7 @@ class RepMixer(nn.Module):
                 bias=True,
             )
         else:
+            self.reparam_conv = None
             self.norm = MobileOneBlock(
                 dim,
                 dim,
@@ -404,12 +627,10 @@ class RepMixer(nn.Module):
             )
             self.use_layer_scale = use_layer_scale
             if use_layer_scale:
-                self.layer_scale = nn.Parameter(
-                    layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
-                )
+                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if hasattr(self, "reparam_conv"):
+        if self.reparam_conv is not None:
             x = self.reparam_conv(x)
             return x
         else:
@@ -444,12 +665,11 @@ class RepMixer(nn.Module):
             )
             b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
 
-        self.reparam_conv = nn.Conv2d(
-            in_chs=self.dim,
-            out_chs=self.dim,
+        self.reparam_conv = create_conv2d(
+            self.dim,
+            self.dim,
             kernel_size=self.kernel_size,
             stride=1,
-            padding=self.kernel_size // 2,
             groups=self.dim,
             bias=True,
         )
@@ -487,19 +707,26 @@ class ConvMlp(nn.Module):
         super().__init__()
         out_chs = out_chs or in_chs
         hidden_channels = hidden_channels or in_chs
-        self.conv = nn.Sequential()
-        self.conv.add_module(
-            "conv",
-            nn.Conv2d(
-                in_chs=in_chs,
-                out_chs=out_chs,
-                kernel_size=7,
-                padding=3,
-                groups=in_chs,
-                bias=False,
-            ),
+        # self.conv = nn.Sequential()
+        # self.conv.add_module(
+        #     "conv",
+        #     nn.Conv2d(
+        #         in_chs,
+        #         out_chs,
+        #         kernel_size=7,
+        #         padding=3,
+        #         groups=in_chs,
+        #         bias=False,
+        #     ),
+        # )
+        # self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_chs))
+        self.conv = ConvNormAct(
+            in_chs,
+            out_chs,
+            kernel_size=7,
+            groups=in_chs,
+            apply_act=False,
         )
-        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_chs))
         self.fc1 = nn.Conv2d(in_chs, hidden_channels, kernel_size=1)
         self.act = act_layer()
         self.fc2 = nn.Conv2d(hidden_channels, out_chs, kernel_size=1)
@@ -565,27 +792,28 @@ class RepCPE(nn.Module):
 
         if inference_mode:
             self.reparam_conv = nn.Conv2d(
-                in_chs=self.in_chs,
-                out_chs=self.embed_dim,
+                self.in_chs,
+                self.embed_dim,
                 kernel_size=self.spatial_shape,
                 stride=1,
-                padding=int(self.spatial_shape[0] // 2),
+                padding=spatial_shape[0] // 2,
                 groups=self.embed_dim,
                 bias=True,
             )
         else:
+            self.reparam_conv = None
             self.pe = nn.Conv2d(
                 in_chs,
                 embed_dim,
                 spatial_shape,
                 1,
                 int(spatial_shape[0] // 2),
-                bias=True,
                 groups=embed_dim,
+                bias=True,
             )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if hasattr(self, "reparam_conv"):
+        if self.reparam_conv is not None:
             x = self.reparam_conv(x)
             return x
         else:
@@ -620,8 +848,8 @@ class RepCPE(nn.Module):
 
         # Introduce reparam conv
         self.reparam_conv = nn.Conv2d(
-            in_chs=self.in_chs,
-            out_chs=self.embed_dim,
+            self.in_chs,
+            self.embed_dim,
             kernel_size=self.spatial_shape,
             stride=1,
             padding=int(self.spatial_shape[0] // 2),
@@ -682,10 +910,9 @@ class RepMixerBlock(nn.Module):
         assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
             mlp_ratio
         )
-        mlp_hidden_dim = int(dim * mlp_ratio)
         self.convffn = ConvMlp(
             in_chs=dim,
-            hidden_channels=mlp_hidden_dim,
+            hidden_channels=int(dim * mlp_ratio),
             act_layer=act_layer,
             drop=drop,
         )
@@ -696,9 +923,7 @@ class RepMixerBlock(nn.Module):
         # Layer Scale
         self.use_layer_scale = use_layer_scale
         if use_layer_scale:
-            self.layer_scale = nn.Parameter(
-                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
-            )
+            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
 
     def forward(self, x):
         if self.use_layer_scale:
@@ -763,12 +988,8 @@ class AttentionBlock(nn.Module):
         # Layer Scale
         self.use_layer_scale = use_layer_scale
         if use_layer_scale:
-            self.layer_scale_1 = nn.Parameter(
-                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
-            )
-            self.layer_scale_2 = nn.Parameter(
-                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
-            )
+            self.layer_scale_1 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
+            self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
 
     def forward(self, x):
         if self.use_layer_scale:
@@ -823,32 +1044,28 @@ def basic_blocks(
             / (sum(num_blocks) - 1)
         )
         if token_mixer_type == "repmixer":
-            blocks.append(
-                RepMixerBlock(
-                    dim,
-                    kernel_size=kernel_size,
-                    mlp_ratio=mlp_ratio,
-                    act_layer=act_layer,
-                    drop=drop_rate,
-                    drop_path=block_dpr,
-                    use_layer_scale=use_layer_scale,
-                    layer_scale_init_value=layer_scale_init_value,
-                    inference_mode=inference_mode,
-                )
-            )
+            blocks.append(RepMixerBlock(
+                dim,
+                kernel_size=kernel_size,
+                mlp_ratio=mlp_ratio,
+                act_layer=act_layer,
+                drop=drop_rate,
+                drop_path=block_dpr,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+                inference_mode=inference_mode,
+            ))
         elif token_mixer_type == "attention":
-            blocks.append(
-                AttentionBlock(
-                    dim,
-                    mlp_ratio=mlp_ratio,
-                    act_layer=act_layer,
-                    norm_layer=norm_layer,
-                    drop=drop_rate,
-                    drop_path=block_dpr,
-                    use_layer_scale=use_layer_scale,
-                    layer_scale_init_value=layer_scale_init_value,
-                )
-            )
+            blocks.append(AttentionBlock(
+                dim,
+                mlp_ratio=mlp_ratio,
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                drop=drop_rate,
+                drop_path=block_dpr,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+            ))
         else:
             raise ValueError(
                 "Token mixer type: {} not supported".format(token_mixer_type)
@@ -932,15 +1149,13 @@ class FastVit(nn.Module):
 
             # Patch merging/downsampling between stages.
             if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
-                network.append(
-                    PatchEmbed(
-                        patch_size=down_patch_size,
-                        stride=down_stride,
-                        in_chs=embed_dims[i],
-                        embed_dim=embed_dims[i + 1],
-                        inference_mode=inference_mode,
-                    )
-                )
+                network += [PatchEmbed(
+                    patch_size=down_patch_size,
+                    stride=down_stride,
+                    in_chs=embed_dims[i],
+                    embed_dim=embed_dims[i + 1],
+                    inference_mode=inference_mode,
+                )]
 
         self.network = nn.ModuleList(network)
 
@@ -1054,6 +1269,8 @@ default_cfgs = {
     "fastvit_t": _cfg(crop_pct=0.9),
     "fastvit_s": _cfg(crop_pct=0.9),
     "fastvit_m": _cfg(crop_pct=0.95),
+    'fastvit_t8': _cfg(
+        url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_t8.pth.tar')
 }