Fix bugs in xlayers

exps/LFNA/lfna.py

@@ -10,7 +10,8 @@ from tqdm import tqdm
 from copy import deepcopy
 from pathlib import Path
 
-lib_dir = (Path(__file__).parent / "..").resolve()
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+print("LIB-DIR: {:}".format(lib_dir))
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 

exps/LFNA/lfna_meta_model.py

@@ -20,7 +20,7 @@ class LFNA_Meta(super_core.SuperModule):
         layer_embedding,
         time_embedding,
         meta_timestamps,
-        mha_depth: int = 2,
+        mha_depth: int = 1,
         dropout: float = 0.1,
     ):
         super(LFNA_Meta, self).__init__()
@@ -44,8 +44,21 @@ class LFNA_Meta(super_core.SuperModule):
         self._append_meta_embed = dict(fixed=None, learnt=None)
         self._append_meta_timestamps = dict(fixed=None, learnt=None)
 
-        self._time_prob_drop = super_core.SuperDrop(dropout, (-1, 1), recover=False)
+        self._tscalar_embed = super_core.SuperDynamicPositionE(
+            time_embedding, scale=100
+        )
+
         # build transformer
+        self._trans_att = super_core.SuperQKVAttention(
+            time_embedding,
+            time_embedding,
+            time_embedding,
+            time_embedding,
+            4,
+            True,
+            attn_drop=None,
+            proj_drop=dropout,
+        )
         layers = []
         for ilayer in range(mha_depth):
             layers.append(
@@ -74,15 +87,9 @@ class LFNA_Meta(super_core.SuperModule):
         self._generator = get_model(**model_kwargs)
         # print("generator: {:}".format(self._generator))
 
-        # unknown token
-        self.register_parameter(
-            "_unknown_token",
-            torch.nn.Parameter(torch.Tensor(1, time_embedding)),
-        )
-
         # initialization
         trunc_normal_(
-            [self._super_layer_embed, self._super_meta_embed, self._unknown_token],
+            [self._super_layer_embed, self._super_meta_embed],
             std=0.02,
         )
 
@@ -136,28 +143,21 @@ class LFNA_Meta(super_core.SuperModule):
                     (self._append_meta_embed["fixed"], meta_embed), dim=0
                 )
 
-    def forward_raw(self, timestamps):
+    def _obtain_time_embed(self, timestamps):
         # timestamps is a batch of sequence of timestamps
         batch, seq = timestamps.shape
-        timestamps = timestamps.unsqueeze(dim=-1)
+        timestamp_q_embed = self._tscalar_embed(timestamps)
-        meta_timestamps = self.meta_timestamps.view(1, 1, -1)
+        timestamp_k_embed = self._tscalar_embed(self.meta_timestamps.view(1, -1))
-        time_diffs = timestamps - meta_timestamps
+        timestamp_v_embed = self.super_meta_embed.unsqueeze(dim=0)
-        time_match_v, time_match_i = torch.min(torch.abs(time_diffs), dim=-1)
+        timestamp_embeds = self._trans_att(
-        # select corresponding meta-knowledge
+            timestamp_q_embed, timestamp_k_embed, timestamp_v_embed
-        meta_match = torch.index_select(
-            self.super_meta_embed, dim=0, index=time_match_i.view(-1)
         )
-        meta_match = meta_match.view(batch, seq, -1)
+        corrected_embeds = self.meta_corrector(timestamp_embeds)
-        # create the probability
+        return corrected_embeds
-        time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
 
-        x_time_probs = self._time_prob_drop(time_probs)
+    def forward_raw(self, timestamps):
-        # if self.training:
+        batch, seq = timestamps.shape
-        #    time_probs[:, -1, :] = 0
+        meta_embed = self._obtain_time_embed(timestamps)
-        unknown_token = self._unknown_token.view(1, 1, -1)
-        raw_meta_embed = x_time_probs * meta_match + (1 - x_time_probs) * unknown_token
-
-        meta_embed = self.meta_corrector(raw_meta_embed)
         # create joint embed
         num_layer, _ = self._super_layer_embed.shape
         meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)

setup.py

@@ -16,7 +16,7 @@
 #
 # TODO(xuanyidong): upload it to conda
 #
-# [2021.05.18] v1.0
+# [2021.05.21] v0.9.9
 import os
 from setuptools import setup, find_packages
 

xautodl/xlayers/super_attention.py

@@ -20,7 +20,7 @@ from .super_module import BoolSpaceType
 from .super_linear import SuperLinear
 
 
-class SuperAttention(SuperModule):
+class SuperSelfAttention(SuperModule):
     """The super model for attention layer."""
 
     def __init__(
@@ -32,7 +32,7 @@ class SuperAttention(SuperModule):
         attn_drop: Optional[float] = None,
         proj_drop: Optional[float] = None,
     ):
-        super(SuperAttention, self).__init__()
+        super(SuperSelfAttention, self).__init__()
         self._input_dim = input_dim
         self._proj_dim = proj_dim
         self._num_heads = num_heads
@@ -150,3 +150,157 @@ class SuperAttention(SuperModule):
         return "input_dim={:}, proj_dim={:}, num_heads={:}".format(
             self._input_dim, self._proj_dim, self._num_heads
         )
+
+
+class SuperQKVAttention(SuperModule):
+    """The super model for attention layer."""
+
+    def __init__(
+        self,
+        in_q_dim: IntSpaceType,
+        in_k_dim: IntSpaceType,
+        in_v_dim: IntSpaceType,
+        proj_dim: IntSpaceType,
+        num_heads: IntSpaceType,
+        qkv_bias: BoolSpaceType = False,
+        attn_drop: Optional[float] = None,
+        proj_drop: Optional[float] = None,
+    ):
+        super(SuperQKVAttention, self).__init__()
+        self._in_v_dim = in_v_dim
+        self._in_q_dim = in_q_dim
+        self._in_k_dim = in_k_dim
+        self._proj_dim = proj_dim
+        self._num_heads = num_heads
+        self._qkv_bias = qkv_bias
+
+        self.q_fc = SuperLinear(in_q_dim, proj_dim, bias=qkv_bias)
+        self.k_fc = SuperLinear(in_k_dim, proj_dim, bias=qkv_bias)
+        self.v_fc = SuperLinear(in_v_dim, proj_dim, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop or 0.0)
+        self.proj = SuperLinear(proj_dim, proj_dim)
+        self.proj_drop = nn.Dropout(proj_drop or 0.0)
+
+    @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 in_q_dim(self):
+        return spaces.get_max(self._in_q_dim)
+
+    @property
+    def in_k_dim(self):
+        return spaces.get_max(self._in_k_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, q_tensor, k_tensor, v_tensor, num_head: int) -> torch.Tensor:
+        q = self.q_fc(q_tensor)
+        B, N, C = q.shape
+
+        k = self.k_fc(k_tensor)
+        B0, S, _ = k.shape
+
+        v = self.v_fc(v_tensor)
+        assert B0 == v.shape[0] and S == v.shape[1]
+
+        head_dim = C // num_head
+        if num_head > C:
+            raise ValueError("Invalid num_head [{:}] vs C [{:}]".format(num_head, C))
+        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(B0, S, num_head, head_dim)
+            .permute(0, 2, 1, 3)
+        )
+        # compute the attention map
+        attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim)
+        attn_v1 = attn_v1.softmax(dim=-1)  # B * #head * N * S
+        attn_v1 = self.attn_drop(attn_v1)
+
+        v_v1 = (
+            v[:, :, : num_head * head_dim]
+            .reshape(B0, S, num_head, head_dim)
+            .permute(0, 2, 1, 3)
+        )
+        feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
+        # process the first [num_head * head_dim] part
+        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
+            # [might have bugs, did not check yet]
+            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, q_tensor, k_tensor, v_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(q_tensor, k_tensor, v_tensor, num_heads)
+        outs = self.proj(feats)
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward_raw(self, q_tensor, k_tensor, v_tensor) -> torch.Tensor:
+        feats = self.forward_qkv(q_tensor, k_tensor, v_tensor, 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.in_q_dim, self.in_k_dim, self.in_v_dim),
+            self._proj_dim,
+            self._num_heads,
+        )

xautodl/xlayers/super_core.py

@@ -24,7 +24,8 @@ super_name2norm = {
     "identity": SuperIdentity,
 }
 
-from .super_attention import SuperAttention
+from .super_attention import SuperSelfAttention
+from .super_attention import SuperQKVAttention
 from .super_transformer import SuperTransformerEncoderLayer
 
 from .super_activations import SuperReLU

xautodl/xlayers/super_positional_embedding.py

@@ -35,11 +35,13 @@ class SuperDynamicPositionE(SuperModule):
         return self.forward_raw(input)
 
     def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
-        import pdb
+        positions = torch.unsqueeze(input * self._scale, dim=-1)
-
+        divisions = torch.reshape(
-        pdb.set_trace()
+            self._div_term, [1] * input.ndim + [self._div_term.numel()]
-        print("---")
+        )
-        return F.linear(input, self._super_weight, self._super_bias)
+        values = positions / divisions
+        embeds = torch.cat((torch.sin(values), torch.cos(values)), dim=-1)
+        return embeds
 
     def extra_repr(self) -> str:
         return "scale={:}, dim={:}".format(self._scale, self._dimension)

xautodl/xlayers/super_transformer.py

@@ -19,7 +19,7 @@ from .super_module import LayerOrder
 from .super_module import SuperModule
 from .super_linear import SuperMLPv2
 from .super_norm import SuperLayerNorm1D
-from .super_attention import SuperAttention
+from .super_attention import SuperSelfAttention
 
 
 class SuperTransformerEncoderLayer(SuperModule):
@@ -47,7 +47,7 @@ class SuperTransformerEncoderLayer(SuperModule):
         order: LayerOrder = LayerOrder.PreNorm,
     ):
         super(SuperTransformerEncoderLayer, self).__init__()
-        mha = SuperAttention(
+        mha = SuperSelfAttention(
             d_model,
             d_model,
             num_heads=num_heads,