ColossalAI/colossalai/nn/layer/vanilla/layers.py

227 lines
9.1 KiB
Python
Raw Normal View History

import math
from typing import Callable
import torch
import torch.nn.functional as F
from colossalai.nn import init as init
from colossalai.registry import LAYERS
from colossalai.utils import get_current_device
from torch import Tensor, dtype
from torch import nn as nn
from ..utils import to_2tuple
2022-01-07 07:08:36 +00:00
from colossalai.context import seed
def drop_path(x, drop_prob: float = 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. 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).
Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
"""
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)
2022-01-07 07:08:36 +00:00
class WrappedDropout(nn.Module):
"""Same as torch.nn.Dropout. But it is wrapped with the context of seed manager.
"""
def __init__(self, p: float = 0.5, inplace: bool = False, mode=None):
super().__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
if mode is None:
self.func = self.nonefunc
else:
self.func = self.normalfunc
self.mode = mode
def nonefunc(self, inputs):
return F.dropout(inputs, self.p, self.training, self.inplace)
def normalfunc(self, inputs):
with seed(self.mode):
return F.dropout(inputs, self.p, self.training, self.inplace)
def forward(self, inputs):
return self.func(inputs)
class WrappedDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Here, it is wrapped with the context of seed manager.
"""
def __init__(self, p: float = 0., mode=None):
super().__init__()
self.p = p
self.mode = mode
if self.mode is None:
self.func = self.nonefunc
else:
self.func = self.normalfunc
self.mode = mode
def nonefunc(self, inputs):
return drop_path(inputs, self.p, self.training)
def normalfunc(self, inputs):
with seed(self.mode):
return drop_path(inputs, self.p, self.training)
def forward(self, inputs):
return self.func(inputs)
@LAYERS.register_module
class VanillaPatchEmbedding(nn.Module):
"""
2D Image to Patch Embedding
:param img_size: image size
:type img_size: int
:param patch_size: patch size
:type patch_size: int
:param in_chans: number of channels of input image
:type in_chans: int
:param embed_size: size of embedding
:type embed_size: int
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param flatten: whether to flatten output tensor, defaults to True
:type flatten: bool, optional
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
:param position_embed_initializer: The intializer of position embedding, defaults to zero
:type position_embed_initializer: typing.Callable, optional
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
dtype: dtype = None,
flatten: bool = True,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.weight = nn.Parameter(
torch.empty((embed_size, in_chans, *self.patch_size), device=get_current_device(), dtype=dtype))
self.bias = nn.Parameter(torch.empty(embed_size, device=get_current_device(), dtype=dtype))
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_size))
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_size))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(self.weight)
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
def forward(self, input_: Tensor) -> Tensor:
B, C, H, W = input_.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = self.cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + self.pos_embed
return output
@LAYERS.register_module
class VanillaClassifier(nn.Module):
"""
2022-01-21 02:44:30 +00:00
Classifier for ViT
:param in_features: size of each input sample
:type in_features: int
:param num_classes: number of classes
:type num_classes: int
:param weight: weight of the classifier, defaults to True
:type weight: torch.nn.Parameter, optional
:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to True
:type bias: bool, optional
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: nn.Parameter = None,
bias: bool = True,
dtype: dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = nn.Parameter(
torch.empty(self.num_classes, self.in_features, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = nn.Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
def reset_parameters(self, weight_initializer, bias_initializer):
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def forward(self, input_: Tensor) -> Tensor:
return F.linear(input_, self.weight, self.bias)