Update LFNA

xautodl/xlayers/super_attention_v2.py

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+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+from __future__ import division
+from __future__ import print_function
+
+import math
+from functools import partial
+from typing import Optional, Text
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+from xautodl import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+from .super_linear import SuperLinear
+
+
+class SuperQKVAttentionV2(SuperModule):
+    """The super model for attention layer."""
+
+    def __init__(
+        self,
+        qk_att_dim: int,
+        in_v_dim: int,
+        hidden_dim: int,
+        num_heads: int,
+        proj_dim: int,
+        qkv_bias: bool = False,
+        attn_drop: Optional[float] = None,
+        proj_drop: Optional[float] = None,
+    ):
+        super(SuperQKVAttentionV2, self).__init__()
+        self._in_v_dim = in_v_dim
+        self._qk_att_dim = qk_att_dim
+        self._proj_dim = proj_dim
+        self._hidden_dim = hidden_dim
+        self._num_heads = num_heads
+        self._qkv_bias = qkv_bias
+
+        self.qk_fc = SuperLinear(qk_att_dim, num_heads, bias=qkv_bias)
+        self.v_fc = SuperLinear(in_v_dim, hidden_dim * num_heads, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop or 0.0)
+        self.proj = SuperLinear(hidden_dim * num_heads, proj_dim)
+        self.proj_drop = nn.Dropout(proj_drop or 0.0)
+        self._infinity = 1e9
+
+    @property
+    def num_heads(self):
+        return spaces.get_max(self._num_heads)
+
+    @property
+    def in_v_dim(self):
+        return spaces.get_max(self._in_v_dim)
+
+    @property
+    def qk_att_dim(self):
+        return spaces.get_max(self._qk_att_dim)
+
+    @property
+    def hidden_dim(self):
+        return spaces.get_max(self._hidden_dim)
+
+    @property
+    def proj_dim(self):
+        return spaces.get_max(self._proj_dim)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        raise NotImplementedError
+
+    def apply_candidate(self, abstract_child: spaces.VirtualNode):
+        super(SuperQKVAttentionV2, self).apply_candidate(abstract_child)
+        raise NotImplementedError
+
+    def forward_qkv(
+        self, qk_att_tensor, v_tensor, num_head: int, mask=None
+    ) -> torch.Tensor:
+        qk_att = self.qk_fc(qk_att_tensor)
+        B, N, S, _ = qk_att.shape
+        assert _ == num_head
+        attn_v1 = qk_att.permute(0, 3, 1, 2)
+        if mask is not None:
+            mask = torch.unsqueeze(mask, dim=1)
+            attn_v1 = attn_v1.masked_fill(mask, -self._infinity)
+        attn_v1 = attn_v1.softmax(dim=-1)  # B * #head * N * S
+        attn_v1 = self.attn_drop(attn_v1)
+
+        v = self.v_fc(v_tensor)
+        B0, _, _ = v.shape
+        v_v1 = v.reshape(B0, S, num_head, -1).permute(0, 2, 1, 3)
+        feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
+        return feats_v1
+
+    def forward_candidate(self, qk_att_tensor, v_tensor, mask=None) -> torch.Tensor:
+        return self.forward_raw(qk_att_tensor, v_tensor, mask)
+
+    def forward_raw(self, qk_att_tensor, v_tensor, mask=None) -> torch.Tensor:
+        feats = self.forward_qkv(qk_att_tensor, v_tensor, self.num_heads, mask)
+        outs = self.proj(feats)
+        outs = self.proj_drop(outs)
+        return outs
+
+    def extra_repr(self) -> str:
+        return "input_dim={:}, hidden_dim={:}, proj_dim={:}, num_heads={:}, infinity={:}".format(
+            (self.qk_att_dim, self.in_v_dim),
+            self._hidden_dim,
+            self._proj_dim,
+            self._num_heads,
+            self._infinity,
+        )