3d version of navit nested tensor

fix tests

.github/workflows/python-test.yml

@@ -28,6 +28,7 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install pytest
         python -m pip install wheel
+        python -m pip install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cpu
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Test with pytest
       run: |

README.md

@@ -198,6 +198,38 @@ preds = v(
 ) # (5, 1000)
 ```
 
+Finally, if you would like to make use of a flavor of NaViT using <a href="https://pytorch.org/tutorials/prototype/nestedtensor.html">nested tensors</a> (which will omit a lot of the masking and padding altogether), make sure you are on version `2.4` and import as follows
+
+```python
+import torch
+from vit_pytorch.na_vit_nested_tensor import NaViT
+
+v = NaViT(
+    image_size = 256,
+    patch_size = 32,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 16,
+    mlp_dim = 2048,
+    dropout = 0.,
+    emb_dropout = 0.,
+    token_dropout_prob = 0.1
+)
+
+# 5 images of different resolutions - List[Tensor]
+
+images = [
+    torch.randn(3, 256, 256), torch.randn(3, 128, 128),
+    torch.randn(3, 128, 256), torch.randn(3, 256, 128),
+    torch.randn(3, 64, 256)
+]
+
+preds = v(images)
+
+assert preds.shape == (5, 1000)
+```
+
 ## Distillation
 
 <img src="./images/distill.png" width="300px"></img>
@@ -2081,6 +2113,17 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@article{Bao2022AllAW,
+    title   = {All are Worth Words: A ViT Backbone for Diffusion Models},
+    author  = {Fan Bao and Shen Nie and Kaiwen Xue and Yue Cao and Chongxuan Li and Hang Su and Jun Zhu},
+    journal = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year    = {2022},
+    pages   = {22669-22679},
+    url     = {https://api.semanticscholar.org/CorpusID:253581703}
+}
+```
+
 ```bibtex
 @misc{Rubin2024,
     author  = {Ohad Rubin},

setup.py

@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.7.2',
+  version = '1.7.10',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description=long_description,
@@ -29,8 +29,8 @@ setup(
   ],
   tests_require=[
     'pytest',
-    'torch==1.12.1',
-    'torchvision==0.13.1'
+    'torch==2.4.0',
+    'torchvision==0.19.0'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

vit_pytorch/look_vit.py

@@ -66,6 +66,7 @@ class Attention(Module):
         heads = 8,
         dim_head = 64,
         dropout = 0.,
+        cross_attend = False,
         reuse_attention = False
     ):
         super().__init__()
@@ -74,10 +75,13 @@ class Attention(Module):
         self.scale = dim_head ** -0.5
         self.heads = heads
         self.reuse_attention = reuse_attention
+        self.cross_attend = cross_attend
 
         self.split_heads = Rearrange('b n (h d) -> b h n d', h = heads)
 
         self.norm = LayerNorm(dim) if not reuse_attention else nn.Identity()
+        self.norm_context = LayerNorm(dim) if cross_attend else nn.Identity()
+
         self.attend = nn.Softmax(dim = -1)
         self.dropout = nn.Dropout(dropout)
 
@@ -95,11 +99,17 @@ class Attention(Module):
         self,
         x,
         context = None,
-        return_attn = False,
-        attn = None
+        return_qk_sim = False,
+        qk_sim = None
     ):
         x = self.norm(x)
-        context = default(context, x)
+
+        assert not (exists(context) ^ self.cross_attend)
+
+        if self.cross_attend:
+            context = self.norm_context(context)
+        else:
+            context = x
 
         v = self.to_v(context)
         v = self.split_heads(v)
@@ -109,20 +119,21 @@ class Attention(Module):
             q, k = tuple(self.split_heads(t) for t in qk)
 
             q = q * self.scale
-            sim = einsum(q, k, 'b h i d, b h j d -> b h i j')
+            qk_sim = einsum(q, k, 'b h i d, b h j d -> b h i j')
 
-            attn = self.attend(sim)
-            attn = self.dropout(attn)
         else:
-            assert exists(attn), 'attention matrix must be passed in for reusing previous attention'
+            assert exists(qk_sim), 'qk sim matrix must be passed in for reusing previous attention'
+
+        attn = self.attend(qk_sim)
+        attn = self.dropout(attn)
 
         out = einsum(attn, v, 'b h i j, b h j d -> b h i d')
         out = self.to_out(out)
 
-        if not return_attn:
+        if not return_qk_sim:
             return out
 
-        return out, attn
+        return out, qk_sim
 
 # LookViT
 
@@ -179,8 +190,8 @@ class LookViT(Module):
             layers.append(ModuleList([
                 Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = dropout),
                 MLP(dim = dim, factor = mlp_factor, dropout = dropout),
-                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout),
-                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout, reuse_attention = True),
+                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout, cross_attend = True),
+                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout, cross_attend = True, reuse_attention = True),
                 LayerNorm(dim),
                 MLP(dim = dim, factor = highres_mlp_factor, dropout = dropout)
             ]))
@@ -218,7 +229,7 @@ class LookViT(Module):
 
             # main tokens cross attends (lookup) on the high res tokens
 
-            lookup_out, lookup_attn = lookup_cross_attn(tokens, highres_tokens, return_attn = True)  # return attention as they reuse the attention matrix
+            lookup_out, qk_sim = lookup_cross_attn(tokens, highres_tokens, return_qk_sim = True)  # return attention as they reuse the attention matrix
             tokens = lookup_out + tokens
 
             tokens = attn(tokens) + tokens
@@ -226,9 +237,9 @@ class LookViT(Module):
 
             # attention-reuse
 
-            lookup_attn = rearrange(lookup_attn, 'b h i j -> b h j i') # transpose for reverse cross attention
+            qk_sim = rearrange(qk_sim, 'b h i j -> b h j i') # transpose for reverse cross attention
 
-            highres_tokens = highres_attn(highres_tokens, tokens, attn = lookup_attn) + highres_tokens
+            highres_tokens = highres_attn(highres_tokens, tokens, qk_sim = qk_sim) + highres_tokens
             highres_tokens = highres_norm(highres_tokens)
 
             highres_tokens = highres_mlp(highres_tokens) + highres_tokens

vit_pytorch/na_vit.py

@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 from functools import partial
-from typing import List, Union
+from typing import List
 
 import torch
 import torch.nn.functional as F
@@ -245,7 +247,7 @@ class NaViT(nn.Module):
 
     def forward(
         self,
-        batched_images: Union[List[Tensor], List[List[Tensor]]], # assume different resolution images already grouped correctly
+        batched_images: List[Tensor] | List[List[Tensor]], # assume different resolution images already grouped correctly
         group_images = False,
         group_max_seq_len = 2048
     ):
@@ -264,6 +266,11 @@ class NaViT(nn.Module):
                 max_seq_len = group_max_seq_len
             )
 
+        # if List[Tensor] is not grouped -> List[List[Tensor]]
+
+        if torch.is_tensor(batched_images[0]):
+            batched_images = [batched_images]
+
         # process images into variable lengthed sequences with attention mask
 
         num_images = []

vit_pytorch/na_vit_nested_tensor.py

@@ -0,0 +1,325 @@
+from __future__ import annotations
+
+from typing import List
+from functools import partial
+
+import torch
+import packaging.version as pkg_version
+
+if pkg_version.parse(torch.__version__) < pkg_version.parse('2.4'):
+    print('nested tensor NaViT was tested on pytorch 2.4')
+
+from torch import nn, Tensor
+import torch.nn.functional as F
+from torch.nn import Module, ModuleList
+from torch.nested import nested_tensor
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def divisible_by(numer, denom):
+    return (numer % denom) == 0
+
+# feedforward
+
+def FeedForward(dim, hidden_dim, dropout = 0.):
+    return nn.Sequential(
+        nn.LayerNorm(dim, bias = False),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Dropout(dropout),
+        nn.Linear(hidden_dim, dim),
+        nn.Dropout(dropout)
+    )
+
+class Attention(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim, bias = False)
+
+        dim_inner = heads * dim_head
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_queries = nn.Linear(dim, dim_inner, bias = False)
+        self.to_keys = nn.Linear(dim, dim_inner, bias = False)
+        self.to_values = nn.Linear(dim, dim_inner, bias = False)
+
+        # in the paper, they employ qk rmsnorm, a way to stabilize attention
+        # will use layernorm in place of rmsnorm, which has been shown to work in certain papers. requires l2norm on non-ragged dimension to be supported in nested tensors
+
+        self.query_norm = nn.LayerNorm(dim_head, bias = False)
+        self.key_norm = nn.LayerNorm(dim_head, bias = False)
+
+        self.dropout = dropout
+
+        self.to_out = nn.Linear(dim_inner, dim, bias = False)
+
+    def forward(
+        self, 
+        x,
+        context: Tensor | None = None
+    ):
+        x = self.norm(x)
+
+        # for attention pooling, one query pooling to entire sequence
+
+        context = default(context, x)
+
+        # queries, keys, values
+
+        query = self.to_queries(x)
+        key = self.to_keys(context)
+        value = self.to_values(context)
+
+        # split heads
+
+        def split_heads(t):
+            return t.unflatten(-1, (self.heads, self.dim_head))
+
+        def transpose_head_seq(t):
+            return t.transpose(1, 2)
+
+        query, key, value = map(split_heads, (query, key, value))
+
+        # qk norm for attention stability
+
+        query = self.query_norm(query)
+        key = self.key_norm(key)
+
+        query, key, value = map(transpose_head_seq, (query, key, value))
+
+        # attention
+
+        out = F.scaled_dot_product_attention(
+            query, key, value,
+            dropout_p = self.dropout if self.training else 0.
+        )
+
+        # merge heads
+
+        out = out.transpose(1, 2).flatten(-2)
+
+        return self.to_out(out)
+
+class Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = ModuleList([])
+
+        for _ in range(depth):
+            self.layers.append(ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
+                FeedForward(dim, mlp_dim, dropout = dropout)
+            ]))
+
+        self.norm = nn.LayerNorm(dim, bias = False)
+
+    def forward(self, x):
+
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+
+        return self.norm(x)
+
+class NaViT(Module):
+    def __init__(
+        self,
+        *,
+        image_size,
+        patch_size,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        mlp_dim,
+        channels = 3,
+        dim_head = 64,
+        dropout = 0.,
+        emb_dropout = 0.,
+        token_dropout_prob: float | None = None
+    ):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+
+        # what percent of tokens to dropout
+        # if int or float given, then assume constant dropout prob
+        # otherwise accept a callback that in turn calculates dropout prob from height and width
+
+        self.token_dropout_prob = token_dropout_prob
+
+        # calculate patching related stuff
+
+        assert divisible_by(image_height, patch_size) and divisible_by(image_width, patch_size), 'Image dimensions must be divisible by the patch size.'
+
+        patch_height_dim, patch_width_dim = (image_height // patch_size), (image_width // patch_size)
+        patch_dim = channels * (patch_size ** 2)
+
+        self.channels = channels
+        self.patch_size = patch_size
+        self.to_patches = Rearrange('c (h p1) (w p2) -> h w (c p1 p2)', p1 = patch_size, p2 = patch_size)
+
+        self.to_patch_embedding = nn.Sequential(
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.pos_embed_height = nn.Parameter(torch.randn(patch_height_dim, dim))
+        self.pos_embed_width = nn.Parameter(torch.randn(patch_width_dim, dim))
+
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        # final attention pooling queries
+
+        self.attn_pool_queries = nn.Parameter(torch.randn(dim))
+        self.attn_pool = Attention(dim = dim, dim_head = dim_head, heads = heads)
+
+        # output to logits
+
+        self.to_latent = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim, bias = False),
+            nn.Linear(dim, num_classes, bias = False)
+        )
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(
+        self,
+        images: List[Tensor], # different resolution images
+    ):
+        batch, device = len(images), self.device
+        arange = partial(torch.arange, device = device)
+
+        assert all([image.ndim == 3 and image.shape[0] == self.channels for image in images]), f'all images must have {self.channels} channels and number of dimensions of 3 (channels, height, width)'
+
+        all_patches = [self.to_patches(image) for image in images]
+
+        # prepare factorized positional embedding height width indices
+
+        positions = []
+
+        for patches in all_patches:
+            patch_height, patch_width = patches.shape[:2]
+            hw_indices = torch.stack(torch.meshgrid((arange(patch_height), arange(patch_width)), indexing = 'ij'), dim = -1)
+            hw_indices = rearrange(hw_indices, 'h w c -> (h w) c')
+            positions.append(hw_indices)
+
+        # need the sizes to compute token dropout + positional embedding
+
+        tokens = [rearrange(patches, 'h w d -> (h w) d') for patches in all_patches]
+
+        # handle token dropout
+
+        seq_lens = torch.tensor([i.shape[0] for i in tokens], device = device)
+
+        if self.training and self.token_dropout_prob > 0:
+
+            keep_seq_lens = ((1. - self.token_dropout_prob) * seq_lens).int().clamp(min = 1)
+
+            kept_tokens = []
+            kept_positions = []
+
+            for one_image_tokens, one_image_positions, seq_len, num_keep in zip(tokens, positions, seq_lens, keep_seq_lens):
+                keep_indices = torch.randn((seq_len,), device = device).topk(num_keep, dim = -1).indices
+
+                one_image_kept_tokens = one_image_tokens[keep_indices]
+                one_image_kept_positions = one_image_positions[keep_indices]
+
+                kept_tokens.append(one_image_kept_tokens)
+                kept_positions.append(one_image_kept_positions)
+
+            tokens, positions, seq_lens = kept_tokens, kept_positions, keep_seq_lens
+
+        # add all height and width factorized positions
+
+        height_indices, width_indices = torch.cat(positions).unbind(dim = -1)
+        height_embed, width_embed = self.pos_embed_height[height_indices], self.pos_embed_width[width_indices]
+
+        pos_embed = height_embed + width_embed
+
+        # use nested tensor for transformers and save on padding computation
+
+        tokens = torch.cat(tokens)
+
+        # linear projection to patch embeddings
+
+        tokens = self.to_patch_embedding(tokens)
+
+        # absolute positions
+
+        tokens = tokens + pos_embed
+
+        tokens = nested_tensor(tokens.split(seq_lens.tolist()), layout = torch.jagged, device = device)
+
+        # embedding dropout
+
+        tokens = self.dropout(tokens)
+
+        # transformer
+
+        tokens = self.transformer(tokens)
+
+        # attention pooling
+        # will use a jagged tensor for queries, as SDPA requires all inputs to be jagged, or not
+
+        attn_pool_queries = [rearrange(self.attn_pool_queries, '... -> 1 ...')] * batch
+
+        attn_pool_queries = nested_tensor(attn_pool_queries, layout = torch.jagged)
+
+        pooled = self.attn_pool(attn_pool_queries, tokens)
+
+        # back to unjagged
+
+        logits = torch.stack(pooled.unbind())
+
+        logits = rearrange(logits, 'b 1 d -> b d')
+
+        logits = self.to_latent(logits)
+
+        return self.mlp_head(logits)
+
+# quick test
+
+if __name__ == '__main__':
+
+    v = NaViT(
+        image_size = 256,
+        patch_size = 32,
+        num_classes = 1000,
+        dim = 1024,
+        depth = 6,
+        heads = 16,
+        mlp_dim = 2048,
+        dropout = 0.,
+        emb_dropout = 0.,
+        token_dropout_prob = 0.1
+    )
+
+    # 5 images of different resolutions - List[Tensor]
+
+    images = [
+        torch.randn(3, 256, 256), torch.randn(3, 128, 128),
+        torch.randn(3, 128, 256), torch.randn(3, 256, 128),
+        torch.randn(3, 64, 256)
+    ]
+
+    assert v(images).shape == (5, 1000)

vit_pytorch/na_vit_nested_tensor_3d.py

@@ -0,0 +1,348 @@
+from __future__ import annotations
+
+from typing import List
+from functools import partial
+
+import torch
+import packaging.version as pkg_version
+
+if pkg_version.parse(torch.__version__) < pkg_version.parse('2.4'):
+    print('nested tensor NaViT was tested on pytorch 2.4')
+
+from torch import nn, Tensor
+import torch.nn.functional as F
+from torch.nn import Module, ModuleList
+from torch.nested import nested_tensor
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def divisible_by(numer, denom):
+    return (numer % denom) == 0
+
+# feedforward
+
+def FeedForward(dim, hidden_dim, dropout = 0.):
+    return nn.Sequential(
+        nn.LayerNorm(dim, bias = False),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Dropout(dropout),
+        nn.Linear(hidden_dim, dim),
+        nn.Dropout(dropout)
+    )
+
+class Attention(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim, bias = False)
+
+        dim_inner = heads * dim_head
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_queries = nn.Linear(dim, dim_inner, bias = False)
+        self.to_keys = nn.Linear(dim, dim_inner, bias = False)
+        self.to_values = nn.Linear(dim, dim_inner, bias = False)
+
+        # in the paper, they employ qk rmsnorm, a way to stabilize attention
+        # will use layernorm in place of rmsnorm, which has been shown to work in certain papers. requires l2norm on non-ragged dimension to be supported in nested tensors
+
+        self.query_norm = nn.LayerNorm(dim_head, bias = False)
+        self.key_norm = nn.LayerNorm(dim_head, bias = False)
+
+        self.dropout = dropout
+
+        self.to_out = nn.Linear(dim_inner, dim, bias = False)
+
+    def forward(
+        self, 
+        x,
+        context: Tensor | None = None
+    ):
+
+        x = self.norm(x)
+
+        # for attention pooling, one query pooling to entire sequence
+
+        context = default(context, x)
+
+        # queries, keys, values
+
+        query = self.to_queries(x)
+        key = self.to_keys(context)
+        value = self.to_values(context)
+
+        # split heads
+
+        def split_heads(t):
+            return t.unflatten(-1, (self.heads, self.dim_head)).transpose(1, 2).contiguous()
+
+        # queries, keys, values
+
+        query = self.to_queries(x)
+        key = self.to_keys(context)
+        value = self.to_values(context)
+
+        # split heads
+
+        def split_heads(t):
+            return t.unflatten(-1, (self.heads, self.dim_head))
+
+        def transpose_head_seq(t):
+            return t.transpose(1, 2)
+
+        query, key, value = map(split_heads, (query, key, value))
+
+        # qk norm for attention stability
+
+        query = self.query_norm(query)
+        key = self.key_norm(key)
+
+        query, key, value = map(transpose_head_seq, (query, key, value))
+
+        # attention
+
+        out = F.scaled_dot_product_attention(
+            query, key, value,
+            dropout_p = self.dropout if self.training else 0.
+        )
+
+        # merge heads
+
+        out = out.transpose(1, 2).flatten(-2)
+
+        return self.to_out(out)
+
+class Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = ModuleList([])
+
+        for _ in range(depth):
+            self.layers.append(ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
+                FeedForward(dim, mlp_dim, dropout = dropout)
+            ]))
+
+        self.norm = nn.LayerNorm(dim, bias = False)
+
+    def forward(self, x):
+
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+
+        return self.norm(x)
+
+class NaViT(Module):
+    def __init__(
+        self,
+        *,
+        image_size,
+        max_frames,
+        patch_size,
+        frame_patch_size,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        mlp_dim,
+        channels = 3,
+        dim_head = 64,
+        dropout = 0.,
+        emb_dropout = 0.,
+        token_dropout_prob: float | None = None
+    ):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+
+        # what percent of tokens to dropout
+        # if int or float given, then assume constant dropout prob
+        # otherwise accept a callback that in turn calculates dropout prob from height and width
+
+        self.token_dropout_prob = token_dropout_prob
+
+        # calculate patching related stuff
+
+        assert divisible_by(image_height, patch_size) and divisible_by(image_width, patch_size), 'Image dimensions must be divisible by the patch size.'
+        assert divisible_by(max_frames, frame_patch_size)
+
+        patch_frame_dim, patch_height_dim, patch_width_dim = (max_frames // frame_patch_size), (image_height // patch_size), (image_width // patch_size)
+
+        patch_dim = channels * (patch_size ** 2) * frame_patch_size
+
+        self.channels = channels
+        self.patch_size = patch_size
+        self.to_patches = Rearrange('c (f pf) (h p1) (w p2) -> f h w (c p1 p2 pf)', p1 = patch_size, p2 = patch_size, pf = frame_patch_size)
+
+        self.to_patch_embedding = nn.Sequential(
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.pos_embed_frame = nn.Parameter(torch.randn(patch_frame_dim, dim))
+        self.pos_embed_height = nn.Parameter(torch.randn(patch_height_dim, dim))
+        self.pos_embed_width = nn.Parameter(torch.randn(patch_width_dim, dim))
+
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        # final attention pooling queries
+
+        self.attn_pool_queries = nn.Parameter(torch.randn(dim))
+        self.attn_pool = Attention(dim = dim, dim_head = dim_head, heads = heads)
+
+        # output to logits
+
+        self.to_latent = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim, bias = False),
+            nn.Linear(dim, num_classes, bias = False)
+        )
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(
+        self,
+        volumes: List[Tensor], # different resolution images / CT scans
+    ):
+        batch, device = len(volumes), self.device
+        arange = partial(torch.arange, device = device)
+
+        assert all([volume.ndim == 4 and volume.shape[0] == self.channels for volume in volumes]), f'all volumes must have {self.channels} channels and number of dimensions of {self.channels} (channels, frame, height, width)'
+
+        all_patches = [self.to_patches(volume) for volume in volumes]
+
+        # prepare factorized positional embedding height width indices
+
+        positions = []
+
+        for patches in all_patches:
+            patch_frame, patch_height, patch_width = patches.shape[:3]
+            fhw_indices = torch.stack(torch.meshgrid((arange(patch_frame), arange(patch_height), arange(patch_width)), indexing = 'ij'), dim = -1)
+            fhw_indices = rearrange(fhw_indices, 'f h w c -> (f h w) c')
+
+            positions.append(fhw_indices)
+
+        # need the sizes to compute token dropout + positional embedding
+
+        tokens = [rearrange(patches, 'f h w d -> (f h w) d') for patches in all_patches]
+
+        # handle token dropout
+
+        seq_lens = torch.tensor([i.shape[0] for i in tokens], device = device)
+
+        if self.training and self.token_dropout_prob > 0:
+
+            keep_seq_lens = ((1. - self.token_dropout_prob) * seq_lens).int().clamp(min = 1)
+
+            kept_tokens = []
+            kept_positions = []
+
+            for one_image_tokens, one_image_positions, seq_len, num_keep in zip(tokens, positions, seq_lens, keep_seq_lens):
+                keep_indices = torch.randn((seq_len,), device = device).topk(num_keep, dim = -1).indices
+
+                one_image_kept_tokens = one_image_tokens[keep_indices]
+                one_image_kept_positions = one_image_positions[keep_indices]
+
+                kept_tokens.append(one_image_kept_tokens)
+                kept_positions.append(one_image_kept_positions)
+
+            tokens, positions, seq_lens = kept_tokens, kept_positions, keep_seq_lens
+
+        # add all height and width factorized positions
+
+
+        frame_indices, height_indices, width_indices = torch.cat(positions).unbind(dim = -1)
+        frame_embed, height_embed, width_embed = self.pos_embed_frame[frame_indices], self.pos_embed_height[height_indices], self.pos_embed_width[width_indices]
+
+        pos_embed = frame_embed + height_embed + width_embed
+
+        # use nested tensor for transformers and save on padding computation
+
+        tokens = torch.cat(tokens)
+
+        # linear projection to patch embeddings
+
+        tokens = self.to_patch_embedding(tokens)
+
+        # absolute positions
+
+        tokens = tokens + pos_embed
+
+        tokens = nested_tensor(tokens.split(seq_lens.tolist()), layout = torch.jagged, device = device)
+
+        # embedding dropout
+
+        tokens = self.dropout(tokens)
+
+        # transformer
+
+        tokens = self.transformer(tokens)
+
+        # attention pooling
+        # will use a jagged tensor for queries, as SDPA requires all inputs to be jagged, or not
+
+        attn_pool_queries = [rearrange(self.attn_pool_queries, '... -> 1 ...')] * batch
+
+        attn_pool_queries = nested_tensor(attn_pool_queries, layout = torch.jagged)
+
+        pooled = self.attn_pool(attn_pool_queries, tokens)
+
+        # back to unjagged
+
+        logits = torch.stack(pooled.unbind())
+
+        logits = rearrange(logits, 'b 1 d -> b d')
+
+        logits = self.to_latent(logits)
+
+        return self.mlp_head(logits)
+
+# quick test
+
+if __name__ == '__main__':
+
+    # works for torch 2.4
+
+    v = NaViT(
+        image_size = 256,
+        max_frames = 8,
+        patch_size = 32,
+        frame_patch_size = 2,
+        num_classes = 1000,
+        dim = 1024,
+        depth = 6,
+        heads = 16,
+        mlp_dim = 2048,
+        dropout = 0.,
+        emb_dropout = 0.,
+        token_dropout_prob = 0.1
+    )
+
+    # 5 volumetric data (videos or CT scans) of different resolutions - List[Tensor]
+
+    volumes = [
+        torch.randn(3, 2, 256, 256), torch.randn(3, 8, 128, 128),
+        torch.randn(3, 4, 128, 256), torch.randn(3, 2, 256, 128),
+        torch.randn(3, 4, 64, 256)
+    ]
+
+    assert v(volumes).shape == (5, 1000)

vit_pytorch/simple_uvit.py

@@ -0,0 +1,176 @@
+import torch
+from torch import nn
+from torch.nn import Module, ModuleList
+
+from einops import rearrange, repeat, pack, unpack
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def exists(v):
+    return v is not None
+
+def divisible_by(num, den):
+    return (num % den) == 0
+
+def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
+    y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
+    assert divisible_by(dim, 4), "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = temperature ** -omega
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
+    return pe.type(dtype)
+
+# classes
+
+def FeedForward(dim, hidden_dim):
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Linear(hidden_dim, dim),
+    )    
+
+class Attention(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+    def forward(self, x):
+        x = self.norm(x)
+
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        attn = self.attend(dots)
+
+        out = torch.matmul(attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim):
+        super().__init__()
+        self.depth = depth
+        self.norm = nn.LayerNorm(dim)
+        self.layers = ModuleList([])
+
+        for layer in range(1, depth + 1):
+            latter_half = layer >= (depth / 2 + 1)
+
+            self.layers.append(nn.ModuleList([
+                nn.Linear(dim * 2, dim) if latter_half else None,
+                Attention(dim, heads = heads, dim_head = dim_head),
+                FeedForward(dim, mlp_dim)
+            ]))
+
+    def forward(self, x):
+
+        skips = []
+
+        for ind, (combine_skip, attn, ff) in enumerate(self.layers):
+            layer = ind + 1
+            first_half = layer <= (self.depth / 2)
+
+            if first_half:
+                skips.append(x)
+
+            if exists(combine_skip):
+                skip = skips.pop()
+                skip_and_x = torch.cat((skip, x), dim = -1)
+                x = combine_skip(skip_and_x)
+
+            x = attn(x) + x
+            x = ff(x) + x
+
+        assert len(skips) == 0
+
+        return self.norm(x)
+
+class SimpleUViT(Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, num_register_tokens = 4, channels = 3, dim_head = 64):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert divisible_by(image_height, patch_height) and divisible_by(image_width, patch_width), 'Image dimensions must be divisible by the patch size.'
+
+        patch_dim = channels * patch_height * patch_width
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange("b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        pos_embedding = posemb_sincos_2d(
+            h = image_height // patch_height,
+            w = image_width // patch_width,
+            dim = dim
+        )
+
+        self.register_buffer('pos_embedding', pos_embedding, persistent = False)
+
+        self.register_tokens = nn.Parameter(torch.randn(num_register_tokens, dim))
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
+
+        self.pool = "mean"
+        self.to_latent = nn.Identity()
+
+        self.linear_head = nn.Linear(dim, num_classes)
+
+    def forward(self, img):
+        batch, device = img.shape[0], img.device
+
+        x = self.to_patch_embedding(img)
+        x = x + self.pos_embedding.type(x.dtype)
+
+        r = repeat(self.register_tokens, 'n d -> b n d', b = batch)
+
+        x, ps = pack([x, r], 'b * d')
+
+        x = self.transformer(x)
+
+        x, _ = unpack(x, ps, 'b * d')
+
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)
+
+# quick test on odd number of layers
+
+if __name__ == '__main__':
+
+    v = SimpleUViT(
+        image_size = 256,
+        patch_size = 32,
+        num_classes = 1000,
+        dim = 1024,
+        depth = 7,
+        heads = 16,
+        mlp_dim = 2048
+    ).cuda()
+
+    img = torch.randn(2, 3, 256, 256).cuda()
+
+    preds = v(img)
+    assert preds.shape == (2, 1000)