Add SuperTransformerEncoder

lib/xlayers/super_norm.py

@@ -0,0 +1,82 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import math
+from typing import Optional, Callable
+
+import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+
+
+class SuperLayerNorm1D(SuperModule):
+    """Super Layer Norm."""
+
+    def __init__(
+        self, dim: IntSpaceType, eps: float = 1e-5, elementwise_affine: bool = True
+    ) -> None:
+        super(SuperLayerNorm1D, self).__init__()
+        self._in_dim = dim
+        self._eps = eps
+        self._elementwise_affine = elementwise_affine
+        if self._elementwise_affine:
+            self.weight = nn.Parameter(torch.Tensor(self.in_dim))
+            self.bias = nn.Parameter(torch.Tensor(self.in_dim))
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+        self.reset_parameters()
+
+    @property
+    def in_dim(self):
+        return spaces.get_max(self._in_dim)
+
+    @property
+    def eps(self):
+        return self._eps
+
+    def reset_parameters(self) -> None:
+        if self._elementwise_affine:
+            nn.init.ones_(self.weight)
+            nn.init.zeros_(self.bias)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._in_dim):
+            root_node.append("_in_dim", self._in_dim.abstract(reuse_last=True))
+        return root_node
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        if not spaces.is_determined(self._in_dim):
+            expected_input_dim = self.abstract_child["_in_dim"].value
+        else:
+            expected_input_dim = spaces.get_determined_value(self._in_dim)
+        if input.size(-1) != expected_input_dim:
+            raise ValueError(
+                "Expect the input dim of {:} instead of {:}".format(
+                    expected_input_dim, input.size(-1)
+                )
+            )
+        if self._elementwise_affine:
+            weight = self.weight[:expected_input_dim]
+            bias = self.bias[:expected_input_dim]
+        else:
+            weight, bias = None, None
+        return F.layer_norm(input, (expected_input_dim,), weight, bias, self.eps)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
+
+    def extra_repr(self) -> str:
+        return "{in_dim}, eps={eps}, " "elementwise_affine={elementwise_affine}".format(
+            in_dim=self._in_dim,
+            eps=self._eps,
+            elementwise_affine=self._elementwise_affine,
+        )