add autodl

AutoDL-Projects/xautodl/xlayers/super_norm.py

@@ -0,0 +1,224 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+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 SuperLayerNorm1D(SuperModule):
+    """Super Layer Norm."""
+
+    def __init__(
+        self, dim: IntSpaceType, eps: float = 1e-6, elementwise_affine: bool = True
+    ) -> None:
+        super(SuperLayerNorm1D, self).__init__()
+        self._in_dim = dim
+        self._eps = eps
+        self._elementwise_affine = elementwise_affine
+        if self._elementwise_affine:
+            self.register_parameter("weight", nn.Parameter(torch.Tensor(self.in_dim)))
+            self.register_parameter("bias", nn.Parameter(torch.Tensor(self.in_dim)))
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+        self.reset_parameters()
+
+    @property
+    def in_dim(self):
+        return spaces.get_max(self._in_dim)
+
+    @property
+    def eps(self):
+        return self._eps
+
+    def reset_parameters(self) -> None:
+        if self._elementwise_affine:
+            nn.init.ones_(self.weight)
+            nn.init.zeros_(self.bias)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._in_dim):
+            root_node.append("_in_dim", self._in_dim.abstract(reuse_last=True))
+        return root_node
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        if not spaces.is_determined(self._in_dim):
+            expected_input_dim = self.abstract_child["_in_dim"].value
+        else:
+            expected_input_dim = spaces.get_determined_value(self._in_dim)
+        if input.size(-1) != expected_input_dim:
+            raise ValueError(
+                "Expect the input dim of {:} instead of {:}".format(
+                    expected_input_dim, input.size(-1)
+                )
+            )
+        if self._elementwise_affine:
+            weight = self.weight[:expected_input_dim]
+            bias = self.bias[:expected_input_dim]
+        else:
+            weight, bias = None, None
+        return F.layer_norm(input, (expected_input_dim,), weight, bias, self.eps)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
+
+    def forward_with_container(self, input, container, prefix=[]):
+        super_weight_name = ".".join(prefix + ["weight"])
+        if container.has(super_weight_name):
+            weight = container.query(super_weight_name)
+        else:
+            weight = None
+        super_bias_name = ".".join(prefix + ["bias"])
+        if container.has(super_bias_name):
+            bias = container.query(super_bias_name)
+        else:
+            bias = None
+        return F.layer_norm(input, (self.in_dim,), weight, bias, self.eps)
+
+    def extra_repr(self) -> str:
+        return (
+            "shape={in_dim}, eps={eps}, elementwise_affine={elementwise_affine}".format(
+                in_dim=self._in_dim,
+                eps=self._eps,
+                elementwise_affine=self._elementwise_affine,
+            )
+        )
+
+
+class SuperSimpleNorm(SuperModule):
+    """Super simple normalization."""
+
+    def __init__(self, mean, std, inplace=False) -> None:
+        super(SuperSimpleNorm, self).__init__()
+        self.register_buffer("_mean", torch.tensor(mean, dtype=torch.float))
+        self.register_buffer("_std", torch.tensor(std, dtype=torch.float))
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        if not self._inplace:
+            tensor = input.clone()
+        else:
+            tensor = input
+        mean = torch.as_tensor(self._mean, dtype=tensor.dtype, device=tensor.device)
+        std = torch.as_tensor(self._std, dtype=tensor.dtype, device=tensor.device)
+        if (std == 0).any():
+            raise ValueError(
+                "std evaluated to zero after conversion to {}, leading to division by zero.".format(
+                    tensor.dtype
+                )
+            )
+        while mean.ndim < tensor.ndim:
+            mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
+        return tensor.sub_(mean).div_(std)
+
+    def extra_repr(self) -> str:
+        return "mean={mean}, std={std}, inplace={inplace}".format(
+            mean=self._mean.item(), std=self._std.item(), inplace=self._inplace
+        )
+
+
+class SuperSimpleLearnableNorm(SuperModule):
+    """Super simple normalization."""
+
+    def __init__(self, mean=0, std=1, eps=1e-6, inplace=False) -> None:
+        super(SuperSimpleLearnableNorm, self).__init__()
+        self.register_parameter(
+            "_mean", nn.Parameter(torch.tensor(mean, dtype=torch.float))
+        )
+        self.register_parameter(
+            "_std", nn.Parameter(torch.tensor(std, dtype=torch.float))
+        )
+        self._eps = eps
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        if not self._inplace:
+            tensor = input.clone()
+        else:
+            tensor = input
+        mean, std = (
+            self._mean.to(tensor.device),
+            torch.abs(self._std.to(tensor.device)) + self._eps,
+        )
+        if (std == 0).any():
+            raise ValueError("std leads to division by zero.")
+        while mean.ndim < tensor.ndim:
+            mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
+        return tensor.sub_(mean).div_(std)
+
+    def forward_with_container(self, input, container, prefix=[]):
+        if not self._inplace:
+            tensor = input.clone()
+        else:
+            tensor = input
+        mean_name = ".".join(prefix + ["_mean"])
+        std_name = ".".join(prefix + ["_std"])
+        mean, std = (
+            container.query(mean_name).to(tensor.device),
+            torch.abs(container.query(std_name).to(tensor.device)) + self._eps,
+        )
+        while mean.ndim < tensor.ndim:
+            mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
+        return tensor.sub_(mean).div_(std)
+
+    def extra_repr(self) -> str:
+        return "mean={mean}, std={std}, inplace={inplace}".format(
+            mean=self._mean.item(), std=self._std.item(), inplace=self._inplace
+        )
+
+
+class SuperIdentity(SuperModule):
+    """Super identity mapping layer."""
+
+    def __init__(self, inplace=False, **kwargs) -> None:
+        super(SuperIdentity, self).__init__()
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        if not self._inplace:
+            tensor = input.clone()
+        else:
+            tensor = input
+        return tensor
+
+    def extra_repr(self) -> str:
+        return "inplace={inplace}".format(inplace=self._inplace)
+
+    def forward_with_container(self, input, container, prefix=[]):
+        return self.forward_raw(input)