Add SuperAttention

lib/xlayers/super_attention.py

@@ -0,0 +1,155 @@
+#####################################################
+# 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
+
+
+import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+from .super_linear import SuperLinear
+
+
+class SuperAttention(SuperModule):
+    """The super model for attention layer."""
+
+    def __init__(
+        self,
+        input_dim: IntSpaceType,
+        proj_dim: IntSpaceType,
+        num_heads: IntSpaceType,
+        qkv_bias: BoolSpaceType = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ):
+        super(SuperAttention, self).__init__()
+        self._input_dim = input_dim
+        self._proj_dim = proj_dim
+        self._num_heads = num_heads
+        self._qkv_bias = qkv_bias
+        # head_dim = dim // num_heads
+        # self.scale = qk_scale or math.sqrt(head_dim)
+
+        # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
+        self.k_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
+        self.v_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = SuperLinear(input_dim, proj_dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    @property
+    def num_heads(self):
+        return spaces.get_max(self._num_heads)
+
+    @property
+    def input_dim(self):
+        return spaces.get_max(self._input_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))
+        space_q = self.q_fc.abstract_search_space
+        space_k = self.k_fc.abstract_search_space
+        space_v = self.v_fc.abstract_search_space
+        space_proj = self.proj.abstract_search_space
+        if not spaces.is_determined(self._num_heads):
+            root_node.append("_num_heads", self._num_heads.abstract(reuse_last=True))
+        if not spaces.is_determined(space_q):
+            root_node.append("q_fc", space_q)
+        if not spaces.is_determined(space_k):
+            root_node.append("k_fc", space_k)
+        if not spaces.is_determined(space_v):
+            root_node.append("v_fc", space_v)
+        if not spaces.is_determined(space_proj):
+            root_node.append("proj", space_proj)
+        return root_node
+
+    def apply_candidate(self, abstract_child: spaces.VirtualNode):
+        super(SuperAttention, self).apply_candidate(abstract_child)
+        if "q_fc" in abstract_child:
+            self.q_fc.apply_candidate(abstract_child["q_fc"])
+        if "k_fc" in abstract_child:
+            self.k_fc.apply_candidate(abstract_child["k_fc"])
+        if "v_fc" in abstract_child:
+            self.v_fc.apply_candidate(abstract_child["v_fc"])
+        if "proj" in abstract_child:
+            self.proj.apply_candidate(abstract_child["proj"])
+
+    def forward_qkv(self, input: torch.Tensor, num_head: int) -> torch.Tensor:
+        B, N, C = input.shape
+        q = self.q_fc(input)
+        k = self.k_fc(input)
+        v = self.v_fc(input)
+        if num_head > C:
+            raise ValueError("Invalid num_head [{:}] vs C [{:}]".format(num_head, C))
+        head_dim = C // num_head
+        # process the first [num_head * head_dim] part
+        q_v1 = (
+            q[:, :, : num_head * head_dim]
+            .reshape(B, N, num_head, head_dim)
+            .permute(0, 2, 1, 3)
+        )
+        k_v1 = (
+            k[:, :, : num_head * head_dim]
+            .reshape(B, N, num_head, head_dim)
+            .permute(0, 2, 1, 3)
+        )
+        v_v1 = (
+            v[:, :, : num_head * head_dim]
+            .reshape(B, N, num_head, head_dim)
+            .permute(0, 2, 1, 3)
+        )
+        attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim)
+        attn_v1 = attn_v1.softmax(dim=-1)
+        attn_v1 = self.attn_drop(attn_v1)
+        feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
+        if C == head_dim * num_head:
+            feats = feats_v1
+        else:  # The channels can not be divided by num_head, the remainder forms an additional head
+            q_v2 = q[:, :, num_head * head_dim :]
+            k_v2 = k[:, :, num_head * head_dim :]
+            v_v2 = v[:, :, num_head * head_dim :]
+            attn_v2 = (q_v2 @ k_v2.transpose(-2, -1)) * math.sqrt(q_v2.shape[-1])
+            attn_v2 = attn_v2.softmax(dim=-1)
+            attn_v2 = self.attn_drop(attn_v2)
+            feats_v2 = attn_v2 @ v_v2
+            feats = torch.cat([feats_v1, feats_v2], dim=-1)
+        return feats
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check the num_heads:
+        if not spaces.is_determined(self._num_heads):
+            num_heads = self.abstract_child["_num_heads"].value
+        else:
+            num_heads = spaces.get_determined_value(self._num_heads)
+        feats = self.forward_qkv(input, num_heads)
+        outs = self.proj(feats)
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        feats = self.forward_qkv(input, self.num_heads)
+        outs = self.proj(feats)
+        outs = self.proj_drop(outs)
+        return outs
+
+    def extra_repr(self) -> str:
+        return "input_dim={:}, proj_dim={:}, num_heads={:}".format(
+            self._input_dim, self._proj_dim, self._num_heads
+        )