Rerange experimental

xautodl/xlayers/__init__.py

@@ -4,8 +4,5 @@
 # This file is expected to be self-contained, expect
 # for importing from spaces to include search space.
 #####################################################
-from .drop import DropBlock2d, DropPath
-from .mlp import MLP
 from .weight_init import trunc_normal_
-
+from .super_core import *
-from .positional_embedding import PositionalEncoder

xautodl/xlayers/drop.py

@@ -1,229 +0,0 @@
-""" Borrowed from https://github.com/rwightman/pytorch-image-models
-DropBlock, DropPath
-
-PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
-
-Papers:
-DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
-
-Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
-
-Code:
-DropBlock impl inspired by two Tensorflow impl that I liked:
- - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def drop_block_2d(
-    x,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-    batchwise: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
-    runs with success, but needs further validation and possibly optimization for lower runtime impact.
-    """
-    B, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size ** 2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    # Forces the block to be inside the feature map.
-    w_i, h_i = torch.meshgrid(
-        torch.arange(W).to(x.device), torch.arange(H).to(x.device)
-    )
-    valid_block = (
-        (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)
-    ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
-    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
-
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
-    else:
-        uniform_noise = torch.rand_like(x)
-    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
-    block_mask = -F.max_pool2d(
-        -block_mask,
-        kernel_size=clipped_block_size,  # block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = (
-            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
-            if batchwise
-            else torch.randn_like(x)
-        )
-        if inplace:
-            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
-        else:
-            x = x * block_mask + normal_noise * (1 - block_mask)
-    else:
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
-        ).to(x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-def drop_block_fast_2d(
-    x: torch.Tensor,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-    batchwise: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
-    block mask at edges.
-    """
-    B, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size ** 2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
-    else:
-        # mask per batch element
-        block_mask = torch.rand_like(x) < gamma
-    block_mask = F.max_pool2d(
-        block_mask.to(x.dtype),
-        kernel_size=clipped_block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = (
-            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
-            if batchwise
-            else torch.randn_like(x)
-        )
-        if inplace:
-            x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)
-        else:
-            x = x * (1.0 - block_mask) + normal_noise * block_mask
-    else:
-        block_mask = 1 - block_mask
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
-        ).to(dtype=x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-class DropBlock2d(nn.Module):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf"""
-
-    def __init__(
-        self,
-        drop_prob=0.1,
-        block_size=7,
-        gamma_scale=1.0,
-        with_noise=False,
-        inplace=False,
-        batchwise=False,
-        fast=True,
-    ):
-        super(DropBlock2d, self).__init__()
-        self.drop_prob = drop_prob
-        self.gamma_scale = gamma_scale
-        self.block_size = block_size
-        self.with_noise = with_noise
-        self.inplace = inplace
-        self.batchwise = batchwise
-        self.fast = fast  # FIXME finish comparisons of fast vs not
-
-    def forward(self, x):
-        if not self.training or not self.drop_prob:
-            return x
-        if self.fast:
-            return drop_block_fast_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-                self.batchwise,
-            )
-        else:
-            return drop_block_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-                self.batchwise,
-            )
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)

xautodl/xlayers/mlp.py

@@ -1,29 +0,0 @@
-import torch.nn as nn
-from typing import Optional
-
-
-class MLP(nn.Module):
-    # MLP: FC -> Activation -> Drop -> FC -> Drop
-    def __init__(
-        self,
-        in_features,
-        hidden_features: Optional[int] = None,
-        out_features: Optional[int] = None,
-        act_layer=nn.GELU,
-        drop: Optional[float] = None,
-    ):
-        super(MLP, self).__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop or 0)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x

xautodl/xlayers/positional_embedding.py

@@ -1,35 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
-#####################################################
-import torch
-import torch.nn as nn
-import math
-
-
-class PositionalEncoder(nn.Module):
-    # 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, max_seq_len, dropout=0.1):
-        super(PositionalEncoder, self).__init__()
-        self.d_model = d_model
-        # create constant 'pe' matrix with values dependant on
-        # pos and i
-        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)
-        pe = pe.unsqueeze(0)
-        self.dropout = nn.Dropout(p=dropout)
-        self.register_buffer("pe", pe)
-
-    def forward(self, x):
-        batch, seq, fdim = x.shape[:3]
-        embeddings = self.pe[:, :seq, :fdim]
-        outs = self.dropout(x + embeddings)
-        return outs

xautodl/xlayers/super_attention.py

@@ -1,11 +1,7 @@
 #####################################################
 # 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

xautodl/xlayers/super_attention_v2.py

@@ -1,11 +1,7 @@
 #####################################################
 # 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

xautodl/xlayers/super_rearrange.py

@@ -0,0 +1,44 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#############################################################
+# Borrow the idea of https://github.com/arogozhnikov/einops #
+#############################################################
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import math
+from typing import Optional, Callable
+
+from xautodl import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+
+
+class SuperRearrange(SuperModule):
+    """Applies the rearrange operation."""
+
+    def __init__(self, pattern, **axes_lengths):
+        super(SuperRearrange, self).__init__()
+
+        self._pattern = pattern
+        self._axes_lengths = axes_lengths
+        self.reset_parameters()
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        return root_node
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    def extra_repr(self) -> str:
+        params = repr(self._pattern)
+        for axis, length in self._axes_lengths.items():
+            params += ", {}={}".format(axis, length)
+        return "{}({})".format(self.__class__.__name__, params)

xautodl/xlayers/super_trade_stem.py

@@ -1,12 +1,7 @@
 #####################################################
 # 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

xautodl/xlayers/super_transformer.py

@@ -1,11 +1,7 @@
 #####################################################
 # 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, Callable
 
 import torch

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