Rerange experimental

xautodl/xmodels/__init__.py

@@ -0,0 +1,5 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
+#####################################################
+# The models in this folder is written with xlayers #
+#####################################################

xautodl/xmodels/transformers.py

@@ -0,0 +1,197 @@
+opyright (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, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from xautodl import spaces
+from xautodl.xlayers import trunc_normal_
+from xautodl.xlayers import super_core
+
+
+__all__ = ["DefaultSearchSpace", "DEFAULT_NET_CONFIG", "get_transformer"]
+
+
+def _get_mul_specs(candidates, num):
+    results = []
+    for i in range(num):
+        results.append(spaces.Categorical(*candidates))
+    return results
+
+
+def _get_list_mul(num, multipler):
+    results = []
+    for i in range(1, num + 1):
+        results.append(i * multipler)
+    return results
+
+
+def _assert_types(x, expected_types):
+    if not isinstance(x, expected_types):
+        raise TypeError(
+            "The type [{:}] is expected to be {:}.".format(type(x), expected_types)
+        )
+
+
+DEFAULT_NET_CONFIG = None
+_default_max_depth = 5
+DefaultSearchSpace = dict(
+    d_feat=6,
+    embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
+    num_heads=_get_mul_specs((1, 2, 4, 8), _default_max_depth),
+    mlp_hidden_multipliers=_get_mul_specs((0.5, 1, 2, 4, 8), _default_max_depth),
+    qkv_bias=True,
+    pos_drop=0.0,
+    other_drop=0.0,
+)
+
+
+class SuperTransformer(super_core.SuperModule):
+    """The super model for transformer."""
+
+    def __init__(
+        self,
+        d_feat: int = 6,
+        embed_dim: List[super_core.IntSpaceType] = DefaultSearchSpace["embed_dim"],
+        num_heads: List[super_core.IntSpaceType] = DefaultSearchSpace["num_heads"],
+        mlp_hidden_multipliers: List[super_core.IntSpaceType] = DefaultSearchSpace[
+            "mlp_hidden_multipliers"
+        ],
+        qkv_bias: bool = DefaultSearchSpace["qkv_bias"],
+        pos_drop: float = DefaultSearchSpace["pos_drop"],
+        other_drop: float = DefaultSearchSpace["other_drop"],
+        max_seq_len: int = 65,
+    ):
+        super(SuperTransformer, self).__init__()
+        self._embed_dim = embed_dim
+        self._num_heads = num_heads
+        self._mlp_hidden_multipliers = mlp_hidden_multipliers
+
+        # the stem part
+        self.input_embed = super_core.SuperAlphaEBDv1(d_feat, embed_dim)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
+        self.pos_embed = super_core.SuperPositionalEncoder(
+            d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop
+        )
+        # build the transformer encode layers -->> check params
+        _assert_types(num_heads, (tuple, list))
+        _assert_types(mlp_hidden_multipliers, (tuple, list))
+        assert len(num_heads) == len(mlp_hidden_multipliers), "{:} vs {:}".format(
+            len(num_heads), len(mlp_hidden_multipliers)
+        )
+        # build the transformer encode layers -->> backbone
+        layers = []
+        for num_head, mlp_hidden_multiplier in zip(num_heads, mlp_hidden_multipliers):
+            layer = super_core.SuperTransformerEncoderLayer(
+                embed_dim,
+                num_head,
+                qkv_bias,
+                mlp_hidden_multiplier,
+                other_drop,
+            )
+            layers.append(layer)
+        self.backbone = super_core.SuperSequential(*layers)
+
+        # the regression head
+        self.head = super_core.SuperSequential(
+            super_core.SuperLayerNorm1D(embed_dim), super_core.SuperLinear(embed_dim, 1)
+        )
+        trunc_normal_(self.cls_token, std=0.02)
+        self.apply(self._init_weights)
+
+    @property
+    def embed_dim(self):
+        return spaces.get_max(self._embed_dim)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._embed_dim):
+            root_node.append("_embed_dim", self._embed_dim.abstract(reuse_last=True))
+        xdict = dict(
+            input_embed=self.input_embed.abstract_search_space,
+            pos_embed=self.pos_embed.abstract_search_space,
+            backbone=self.backbone.abstract_search_space,
+            head=self.head.abstract_search_space,
+        )
+        for key, space in xdict.items():
+            if not spaces.is_determined(space):
+                root_node.append(key, space)
+        return root_node
+
+    def apply_candidate(self, abstract_child: spaces.VirtualNode):
+        super(SuperTransformer, self).apply_candidate(abstract_child)
+        xkeys = ("input_embed", "pos_embed", "backbone", "head")
+        for key in xkeys:
+            if key in abstract_child:
+                getattr(self, key).apply_candidate(abstract_child[key])
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, super_core.SuperLinear):
+            trunc_normal_(m._super_weight, std=0.02)
+            if m._super_bias is not None:
+                nn.init.constant_(m._super_bias, 0)
+        elif isinstance(m, super_core.SuperLayerNorm1D):
+            nn.init.constant_(m.weight, 1.0)
+            nn.init.constant_(m.bias, 0)
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        batch, flatten_size = input.shape
+        feats = self.input_embed(input)  # batch * 60 * 64
+        if not spaces.is_determined(self._embed_dim):
+            embed_dim = self.abstract_child["_embed_dim"].value
+        else:
+            embed_dim = spaces.get_determined_value(self._embed_dim)
+        cls_tokens = self.cls_token.expand(batch, -1, -1)
+        cls_tokens = F.interpolate(
+            cls_tokens, size=(embed_dim), mode="linear", align_corners=True
+        )
+        feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
+        feats_w_tp = self.pos_embed(feats_w_ct)
+        xfeats = self.backbone(feats_w_tp)
+        xfeats = xfeats[:, 0, :]  # use the feature for the first token
+        predicts = self.head(xfeats).squeeze(-1)
+        return predicts
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        batch, flatten_size = input.shape
+        feats = self.input_embed(input)  # batch * 60 * 64
+        cls_tokens = self.cls_token.expand(batch, -1, -1)
+        feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
+        feats_w_tp = self.pos_embed(feats_w_ct)
+        xfeats = self.backbone(feats_w_tp)
+        xfeats = xfeats[:, 0, :]  # use the feature for the first token
+        predicts = self.head(xfeats).squeeze(-1)
+        return predicts
+
+
+def get_transformer(config):
+    if config is None:
+        return SuperTransformer(6)
+    if not isinstance(config, dict):
+        raise ValueError("Invalid Configuration: {:}".format(config))
+    name = config.get("name", "basic")
+    if name == "basic":
+        model = SuperTransformer(
+            d_feat=config.get("d_feat"),
+            embed_dim=config.get("embed_dim"),
+            num_heads=config.get("num_heads"),
+            mlp_hidden_multipliers=config.get("mlp_hidden_multipliers"),
+            qkv_bias=config.get("qkv_bias"),
+            pos_drop=config.get("pos_drop"),
+            other_drop=config.get("other_drop"),
+        )
+    else:
+        raise ValueError("Unknown model name: {:}".format(name))
+    return model