Challenge in replacing SelfAttention with ImageLinearAttention in Vision Transformer
When I am replacing ImageLinearAttention with SelfAttention in Vision Transformer, with the code as follows, I get a RuntimeError. The code for ImageLinearAttention is from https://github.com/lucidrains/linear-attention-transformer/blob/master/linear_attention_transformer/images.py except I removed number of channels as you see in commented code.
class ImageLinearAttention(nn.Module):
    def __init__(self, chan, chan_out = None, kernel_size = 1, padding = 0, stride = 1, key_dim = 64, value_dim = 64, heads = 8, norm_queries = True):
        super().__init__()
        self.chan = chan
        chan_out = chan if chan_out is None else chan_out

        self.key_dim = key_dim
        self.value_dim = value_dim
        self.heads = heads

        self.norm_queries = norm_queries

        conv_kwargs = {'padding': padding, 'stride': stride}
        self.to_q = nn.Conv2d(chan, key_dim * heads, kernel_size, **conv_kwargs)
        self.to_k = nn.Conv2d(chan, key_dim * heads, kernel_size, **conv_kwargs)
        self.to_v = nn.Conv2d(chan, value_dim * heads, kernel_size, **conv_kwargs)
        print('value dim: ', value_dim)
        print('chan out: ', chan_out)
        print('kernel_size: ', kernel_size)
        out_conv_kwargs = {'padding': padding}
        print('out_conv_kwargs: ', out_conv_kwargs)
        print('in_chan: ', value_dim * heads)
        self.to_out = nn.Conv2d(value_dim * heads, chan_out, kernel_size, **out_conv_kwargs)

    def forward(self, x, context = None):
        print('x.shape: ', x.shape)
        print('*x.shape is: ', *x.shape)
        print('heads: ', self.heads)
        #b, c, h, w, k_dim, heads = *x.shape, self.key_dim, self.heads
        b, h, w, k_dim, heads = *x.shape, self.key_dim, self.heads
        q, k, v = (self.to_q(x), self.to_k(x), self.to_v(x))
        q, k, v = map(lambda t: t.reshape(b, heads, -1, h * w), (q, k, v))
        q, k = map(lambda x: x * (self.key_dim ** -0.25), (q, k))
        
        if context is not None:
            #context = context.reshape(b, c, 1, -1)
            context = context.reshape(b, 1, -1)
            ck, cv = self.to_k(context), self.to_v(context)
            ck, cv = map(lambda t: t.reshape(b, heads, k_dim, -1), (ck, cv))
            k = torch.cat((k, ck), dim=3)
            v = torch.cat((v, cv), dim=3)

        k = k.softmax(dim=-1)

        if self.norm_queries:
            q = q.softmax(dim=-2)

        context = torch.einsum('bhdn,bhen-&gt;bhde', k, v)
        out = torch.einsum('bhdn,bhde-&gt;bhen', q, context)
        out = out.reshape(b, -1, h, w)
        out = self.to_out(out)
        return out

Error is:
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [384, 512, 1, 1], but got 3-dimensional input of size [1, 1984, 512] instead
Also, my data fed to transformer is of size torch.Size([1983, 512]) and my batch size is 1.
Full log is:
$ bash scripts/train.sh 
train: True test: False cam: False
preparing datasets and dataloaders......
total_train_num:  176
creating models......
n_class:  2
in_dim:  512
value dim:  64
chan out:  512
kernel_size:  1
out_conv_kwargs:  {'padding': 0}
in_chan:  768
in_dim:  512
value dim:  64
chan out:  512
kernel_size:  1
out_conv_kwargs:  {'padding': 0}
in_chan:  768

=&gt;Epoches 1, learning rate = 0.0010000, previous best = 0.0000
torch.Size([1983, 512])
features size:  torch.Size([1983, 512])
/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:129: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  warnings.warn(&quot;Detected call of `lr_scheduler.step()` before `optimizer.step()`. &quot;
/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
max_feature_num:  1983
batch feature size:  torch.Size([1, 1983, 512])
x.shape:  torch.Size([1, 1984, 512])
*x.shape is:  1 1984 512
heads:  12
Traceback (most recent call last):
  File &quot;main.py&quot;, line 148, in &lt;module&gt;
    preds,labels,loss = trainer.train(sample_batched, model)
  File &quot;/SeaExp/mona/research/code/cc/helper.py&quot;, line 71, in train
    pred,labels,loss = model.forward(feats, labels, masks)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py&quot;, line 166, in forward
    return self.module(*inputs[0], **kwargs[0])
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/research/code/cc/models/Transformer.py&quot;, line 31, in forward
    out = self.transformer(X)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/research/code/cc/models/linear_att_ViT.py&quot;, line 262, in forward
    feat = self.transformer(emb)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/research/code/cc/models/linear_att_ViT.py&quot;, line 206, in forward
    out = layer(out)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/research/code/cc/models/linear_att_ViT.py&quot;, line 174, in forward
    out = self.attn(out)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/research/code/cc/models/linear_att_ViT.py&quot;, line 92, in forward
    q, k, v = (self.to_q(x), self.to_k(x), self.to_v(x))
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py&quot;, line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/conv.py&quot;, line 443, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File &quot;/SeaExp/mona/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/conv.py&quot;, line 439, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [384, 512, 1, 1], but got 3-dimensional input of size [1, 1984, 512] instead

The original SelfAttention code is:
class SelfAttention(nn.Module):
    def __init__(self, in_dim, heads=8, dropout_rate=0.1):
        super(SelfAttention, self).__init__()
        self.heads = heads
        self.head_dim = in_dim // heads
        self.scale = self.head_dim ** 0.5
        
        self.query = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.key = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.value = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.out = LinearGeneral((self.heads, self.head_dim), (in_dim,))

        if dropout_rate &gt; 0:
            self.dropout = nn.Dropout(dropout_rate)
        else:
            self.dropout = None

    def forward(self, x):
        b, n, _ = x.shape

        q = self.query(x, dims=([2], [0]))
        k = self.key(x, dims=([2], [0]))
        v = self.value(x, dims=([2], [0]))

        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / self.scale
        attn_weights = F.softmax(attn_weights, dim=-1)
        out = torch.matmul(attn_weights, v)
        out = out.permute(0, 2, 1, 3)

        out = self.out(out, dims=([2, 3], [0, 1]))

        return out

How can I fix this error? I am calling the ImageSelfAttention as following in the Encoder block of the Vision Transformer:
class EncoderBlock(nn.Module):
    def __init__(self, in_dim, mlp_dim, num_heads, dropout_rate=0.1, attn_dropout_rate=0.1):
        super(EncoderBlock, self).__init__()

        self.norm1 = nn.LayerNorm(in_dim)
        #self.attn = SelfAttention(in_dim, heads=num_heads, dropout_rate=attn_dropout_rate)
        ## note Mona: not sure if I am correctly passing the params
        # what about attn_dropout_rate=0.1
        ## I don't know 
        print('in_dim: ', in_dim) 
        self.attn = ImageLinearAttention(chan=in_dim, heads=num_heads, key_dim=32)
        if dropout_rate &gt; 0:
            self.dropout = nn.Dropout(dropout_rate)
        else:
            self.dropout = None
        self.norm2 = nn.LayerNorm(in_dim)
        self.mlp = MlpBlock(in_dim, mlp_dim, in_dim, dropout_rate)

    def forward(self, x):
        residual = x
        out = self.norm1(x)
        out = self.attn(out)
        if self.dropout:
            out = self.dropout(out)
        out += residual
        residual = out

        out = self.norm2(out)
        out = self.mlp(out)
        out += residual
        return out

The code for SelfAttention and how to use it in encoder is mostly from https://github.com/asyml/vision-transformer-pytorch/blob/main/src/model.py

Looks like image self attention works on 4 dimensional inputs of shape (batch, dim, height, width) suited for images and self attention works on 3 dimensional inputs of shape (batch, sequence length, dim) suited for NLP tasks. Maybe the input has to be reshaped before feeding to self attention.
