Add SuperTransformer

lib/xlayers/super_positional_embedding.py

@@ -0,0 +1,68 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
+#####################################################
+import torch
+import torch.nn as nn
+import math
+
+import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+
+
+class SuperPositionalEncoder(SuperModule):
+    """Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
+    https://github.com/pytorch/examples/blob/master/word_language_model/model.py#L65
+    """
+
+    def __init__(self, d_model: IntSpaceType, max_seq_len: int, dropout: float = 0.1):
+        super(SuperPositionalEncoder, self).__init__()
+        self._d_model = d_model
+        # create constant 'pe' matrix with values dependant on
+        # pos and i
+        self.dropout = nn.Dropout(p=dropout)
+        self.register_buffer("pe", self.create_pos_embed(max_seq_len, self.d_model))
+
+    @property
+    def d_model(self):
+        return spaces.get_max(self._d_model)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._d_model):
+            root_node.append("_d_model", self._d_model.abstract(reuse_last=True))
+        return root_node
+
+    def create_pos_embed(self, max_seq_len, d_model):
+        pe = torch.zeros(max_seq_len, d_model)
+        for pos in range(max_seq_len):
+            for i in range(0, d_model):
+                div = 10000 ** ((i // 2) * 2 / d_model)
+                value = pos / div
+                if i % 2 == 0:
+                    pe[pos, i] = math.sin(value)
+                else:
+                    pe[pos, i] = math.cos(value)
+        return pe.unsqueeze(0)
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        batch, seq, fdim = input.shape[:3]
+        embeddings = self.pe[:, :seq]
+        if not spaces.is_determined(self._d_model):
+            expected_d_model = self.abstract_child["_d_model"].value
+        else:
+            expected_d_model = spaces.get_determined_value(self._d_model)
+        assert fdim == expected_d_model, "{:} vs {:}".format(fdim, expected_d_model)
+
+        embeddings = torch.nn.functional.interpolate(
+            embeddings, size=(expected_d_model), mode="linear", align_corners=True
+        )
+        outs = self.dropout(input + embeddings)
+        return outs
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        batch, seq, fdim = input.shape[:3]
+        embeddings = self.pe[:, :seq]
+        outs = self.dropout(input + embeddings)
+        return outs