release 1 conformer block

README.md

@@ -28,6 +28,27 @@ x = torch.randn(1, 1024, 512)
 x = layer(x) + x
 ```
 
+1 Conformer Block
+
+```python
+import torch
+from conformer import ConformerBlock
+
+block = ConformerBlock(
+    dim = 512,
+    dim_head = 64,
+    heads = 8,
+    ff_mult = 4,
+    conv_expansion_factor = 2,
+    conv_kernel_size = 31,
+    attn_dropout = 0.,
+    ff_dropout = 0.,
+    conv_dropout = 0.
+)
+
+x = torch.randn(1, 1024, 512)
+block(x) # (1, 1024, 512)
+```
 ## Citations
 
 ```bibtex

conformer/__init__.py

@@ -1 +1 @@
-from conformer.conformer import ConformerConvModule
+from conformer.conformer import ConformerConvModule, ConformerBlock

conformer/conformer.py

@@ -1,9 +1,17 @@
 import torch
-from torch import nn
+from torch import nn, einsum
 import torch.nn.functional as F
 
+from einops import rearrange
+
 # helper functions
 
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
 def calc_same_padding(kernel_size):
     pad = kernel_size // 2
     return (pad, pad - (kernel_size + 1) % 2)
@@ -42,7 +50,86 @@ def forward(self, x):
         x = F.pad(x, self.padding)
         return self.conv(x)
 
-# main class
+# attention, feedforward, and conv module
+
+class Scale(nn.Module):
+    def __init__(self, scale, fn):
+        super().__init__()
+        self.fn = fn
+        self.scale = scale
+
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) * self.scale
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.fn = fn
+        self.norm = nn.LayerNorm(dim)
+
+    def forward(self, x, **kwargs):
+        x = self.norm(x)
+        return self.fn(x, **kwargs)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads = 8,
+        dim_head = 64,
+        dropout = 0.
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads= heads
+        self.scale = dim_head ** -0.5
+        self.to_q = nn.Linear(dim, inner_dim, bias = False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, context = None, mask = None, context_mask = None):
+        device, h, has_context = x.device, self.heads, exists(context)
+        context = default(context, x)
+
+        q, k, v = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        if exists(mask) or exists(context_mask):
+            mask = default(mask, lambda: torch.ones(*x.shape[:2], device = device))
+            context_mask = default(context_mask, mask) if not has_context else default(context_mask, lambda: torch.ones(*context.shape[:2], device = device))
+            mask_value = -torch.finfo(dots.dtype).max
+            mask = mask[:, None, :, None] * context_mask[:, None, None, :]
+            dots.masked_fill_(~mask, mask_value)
+
+        attn = dots.softmax(dim = -1)
+        attn = self.dropout(attn)
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        mult = 4,
+        dropout = 0.
+    ):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, dim * mult),
+            Swish(),
+            nn.Dropout(dropout),
+            nn.Linear(dim * mult, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
 
 class ConformerConvModule(nn.Module):
     def __init__(
@@ -72,3 +159,39 @@ def __init__(
 
     def forward(self, x):
         return self.net(x)
+
+# Conformer Block
+
+class ConformerBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_head = 64,
+        heads = 8,
+        ff_mult = 4,
+        conv_expansion_factor = 2,
+        conv_kernel_size = 31,
+        attn_dropout = 0.,
+        ff_dropout = 0.,
+        conv_dropout = 0.
+    ):
+        super().__init__()
+        self.ff1 = FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
+        self.attn = Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = attn_dropout)
+        self.conv = ConformerConvModule(dim = dim, causal = False, expansion_factor = conv_expansion_factor, kernel_size = conv_kernel_size, dropout = conv_dropout)
+        self.ff2 = FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
+
+        self.attn = PreNorm(dim, self.attn)
+        self.ff1 = Scale(0.5, PreNorm(dim, self.ff1))
+        self.ff2 = Scale(0.5, PreNorm(dim, self.ff2))
+
+        self.post_norm = nn.LayerNorm(dim)
+
+    def forward(self, x, mask = None):
+        x = self.ff1(x) + x
+        x = self.attn(x, mask = mask) + x
+        x = self.conv(x) + x
+        x = self.ff2(x) + x
+        x = self.post_norm(x)
+        return x

setup.py

@@ -3,14 +3,15 @@
 setup(
   name = 'conformer',
   packages = find_packages(),
-  version = '0.1.0',
+  version = '0.2.0',
   license='MIT',
   description = 'The convolutional module from the Conformer paper',
   author = 'Phil Wang',
   author_email = '[email protected]',
   url = 'https://github.com/lucidrains/conformer',
   keywords = ['transformers', 'artificial intelligence', 'transformer'],
   install_requires=[
+      'einops',
       'torch'
   ],
   classifiers=[