Add the SuperMLP class

lib/xlayers/super_core.py

@@ -3,4 +3,5 @@
 #####################################################
 from .super_module import SuperRunMode
 from .super_module import SuperModule
-from .super_mlp import SuperLinear
+from .super_linear import SuperLinear
+from .super_linear import SuperMLP

lib/xlayers/super_linear.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 import math
-from typing import Optional, Union
+from typing import Optional, Union, Callable
 
 import spaces
 from .super_module import SuperModule
@@ -57,11 +57,15 @@ class SuperLinear(SuperModule):
     def abstract_search_space(self):
         root_node = spaces.VirtualNode(id(self))
         if not spaces.is_determined(self._in_features):
-            root_node.append("_in_features", self._in_features.abstract())
+            root_node.append(
+                "_in_features", self._in_features.abstract(reuse_last=True)
+            )
         if not spaces.is_determined(self._out_features):
-            root_node.append("_out_features", self._out_features.abstract())
+            root_node.append(
+                "_out_features", self._out_features.abstract(reuse_last=True)
+            )
         if not spaces.is_determined(self._bias):
-            root_node.append("_bias", self._bias.abstract())
+            root_node.append("_bias", self._bias.abstract(reuse_last=True))
         return root_node
 
     def reset_parameters(self) -> None:
@@ -116,24 +120,51 @@ class SuperMLP(SuperModule):
 
     def __init__(
         self,
-        in_features,
-        hidden_features: Optional[int] = None,
-        out_features: Optional[int] = None,
-        act_layer=nn.GELU,
+        in_features: IntSpaceType,
+        hidden_features: IntSpaceType,
+        out_features: IntSpaceType,
+        act_layer: Callable[[], nn.Module] = nn.GELU,
         drop: Optional[float] = None,
     ):
         super(SuperMLP, self).__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
+        self._in_features = in_features
+        self._hidden_features = hidden_features
+        self._out_features = out_features
+        self._drop_rate = drop
+        self.fc1 = SuperLinear(in_features, hidden_features)
         self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.fc2 = SuperLinear(hidden_features, out_features)
         self.drop = nn.Dropout(drop or 0.0)
 
-    def forward(self, x):
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        space_fc1 = self.fc1.abstract_search_space
+        space_fc2 = self.fc2.abstract_search_space
+        if not spaces.is_determined(space_fc1):
+            root_node.append("fc1", space_fc1)
+        if not spaces.is_determined(space_fc2):
+            root_node.append("fc2", space_fc2)
+        return root_node
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        return self._unified_forward(x)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return self._unified_forward(x)
+
+    def _unified_forward(self, x):
         x = self.fc1(x)
         x = self.act(x)
         x = self.drop(x)
         x = self.fc2(x)
         x = self.drop(x)
         return x
+
+    def extra_repr(self) -> str:
+        return "in_features={:}, hidden_features={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
+            self._in_features,
+            self._hidden_features,
+            self._out_features,
+            self._drop_rate,
+        )