Layer integration (#83)

* integrated parallel layers for ease of building models

* integrated 2.5d layers

* cleaned codes and unit tests

* added log metric by step hook; updated imagenet benchmark; fixed some bugs

* reworked initialization; cleaned codes

Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>

colossalai/nn/layer/vanilla/__init__.py

@@ -0,0 +1,3 @@
+from .layers import DropPath, VanillaClassifier, VanillaPatchEmbedding
+
+__all__ = ['VanillaPatchEmbedding', 'VanillaClassifier', 'DropPath']

colossalai/nn/layer/vanilla/layers.py

@@ -0,0 +1,134 @@
+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 .._common_utils import to_2tuple
+
+
+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)
+
+
+@LAYERS.register_module
+class VanillaPatchEmbedding(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    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):
+    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)