From 548f52b35062e258622ea638ed1b132d6759a07a Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 9 May 2021 00:36:55 +0200
Subject: Attention layer soon done

---
 .../networks/transformer/attention_layers.py       | 19 ------
 text_recognizer/networks/transformer/layers.py     | 77 ++++++++++++++++++++++
 text_recognizer/networks/transformer/norm.py       | 11 ----
 .../networks/transformer/nystromer/__init__.py     |  2 +
 text_recognizer/networks/transformer/residual.py   |  8 +++
 5 files changed, 87 insertions(+), 30 deletions(-)
 delete mode 100644 text_recognizer/networks/transformer/attention_layers.py
 create mode 100644 text_recognizer/networks/transformer/layers.py
 create mode 100644 text_recognizer/networks/transformer/residual.py

(limited to 'text_recognizer/networks')

diff --git a/text_recognizer/networks/transformer/attention_layers.py b/text_recognizer/networks/transformer/attention_layers.py
deleted file mode 100644
index 721fa27..0000000
--- a/text_recognizer/networks/transformer/attention_layers.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""Generates the attention layer architecture."""
-from typing import Type
-
-import torch
-from torch import nn, Tensor
-
-
-class AttentionLayers(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        depth: int,
-        num_heads: int,
-        norm_layer: Type[nn.Module],
-        causal: bool = False,
-        cross_attend: bool = False,
-        only_cross: bool = False,
-    ) -> None:
-        pass
diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
new file mode 100644
index 0000000..1c951ae
--- /dev/null
+++ b/text_recognizer/networks/transformer/layers.py
@@ -0,0 +1,77 @@
+"""Generates the attention layer architecture."""
+from functools import partial
+from typing import Dict, Optional, Type
+
+from click.types import Tuple
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention
+from .mlp import FeedForward
+from .residual import Residual
+
+
+class AttentionLayers(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        num_heads: int,
+        ff_kwargs: Dict,
+        attn_kwargs: Dict,
+        attn_fn: Type[nn.Module] = Attention,
+        norm_fn: Type[nn.Module] = nn.LayerNorm,
+        ff_fn: Type[nn.Module] = FeedForward,
+        residual_fn: Type[nn.Module] = Residual,
+        causal: bool = False,
+        cross_attend: bool = False,
+    ) -> None:
+        super().__init__()
+        attn_fn = partial(attn_fn, dim=dim, num_heads=num_heads, **attn_kwargs)
+        norm_fn = partial(norm_fn, dim=dim)
+        ff_fn = partial(ff_fn, dim=dim, **ff_kwargs)
+        layer_types = self._get_layer_types(cross_attend) * depth
+        self.layers = self._build_network(
+            layer_types, causal, attn_fn, norm_fn, ff_fn, residual_fn
+        )
+
+    @staticmethod
+    def _get_layer_types(cross_attend: bool) -> Tuple:
+        """Get layer specification."""
+        if cross_attend:
+            return "a", "c", "f"
+        return "a", "f"
+
+    @staticmethod
+    def _build_network(
+        layer_types: Tuple,
+        causal: bool,
+        attn_fn: partial,
+        norm_fn: partial,
+        ff_fn: partial,
+        residual_fn: Type[nn.Module],
+    ) -> nn.ModuleList:
+        """Configures transformer layers."""
+        layers = nn.ModuleList([])
+        for layer_type in layer_types:
+            if layer_type == "a":
+                layer = attn_fn(causal=causal)
+            elif layer_type == "c":
+                layer = attn_fn()
+            elif layer_type == "f":
+                layer = ff_fn()
+
+            residual_fn = residual_fn()
+
+            layers.append(nn.ModuleList([norm_fn(), layer, residual_fn]))
+        return layers
+
+    def forward(
+        self,
+        x: Tensor,
+        context: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        context_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        pass
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
index 58c8770..8bc3221 100644
--- a/text_recognizer/networks/transformer/norm.py
+++ b/text_recognizer/networks/transformer/norm.py
@@ -11,17 +11,6 @@ from torch import nn
 from torch import Tensor
 
 
-class Rezero(nn.Module):
-    def __init__(self, fn: Callable) -> None:
-        super().__init__()
-        self.fn = fn
-        self.g = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x: Tensor, **kwargs: Dict) -> Tensor:
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.g, *rest)
-
-
 class ScaleNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1.0e-5) -> None:
         super().__init__()
diff --git a/text_recognizer/networks/transformer/nystromer/__init__.py b/text_recognizer/networks/transformer/nystromer/__init__.py
index e69de29..ea2c6fc 100644
--- a/text_recognizer/networks/transformer/nystromer/__init__.py
+++ b/text_recognizer/networks/transformer/nystromer/__init__.py
@@ -0,0 +1,2 @@
+"""Nyströmer module."""
+from .nystromer import Nystromer
diff --git a/text_recognizer/networks/transformer/residual.py b/text_recognizer/networks/transformer/residual.py
new file mode 100644
index 0000000..1547df6
--- /dev/null
+++ b/text_recognizer/networks/transformer/residual.py
@@ -0,0 +1,8 @@
+"""Residual function."""
+from torch import nn, Tensor
+
+
+class Residual(nn.Module):
+    def forward(self, x: Tensor, residual: Tensor) -> Tensor:
+        """Applies the residual function."""
+        return x + residual
-- 
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