Source code for libai.models.swin_transformer
# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import oneflow as flow
import oneflow.nn as nn
from flowvision.layers import trunc_normal_
from flowvision.models import to_2tuple
from libai.config.config import configurable
from libai.layers import MLP, DropPath, LayerNorm, Linear
from libai.utils import distributed as dist
def window_partition(x, window_size):
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
"""Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query,key,value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(
self,
dim,
window_size,
num_heads,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
fused_bias_add_dropout=False,
layer_idx=0,
):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
flow.zeros(
(2 * window_size[0] - 1) * (2 * window_size[1] - 1),
num_heads,
placement=dist.get_layer_placement(layer_idx),
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
)
) # 2*Wh-1 * 2*Ww-1, nH
trunc_normal_(self.relative_position_bias_table, std=0.02)
# get pair-wise relative position index for each token inside the window
coords_h = flow.arange(self.window_size[0])
coords_w = flow.arange(self.window_size[1])
coords = flow.stack(flow.meshgrid(*[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = flow.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] = (
relative_coords[:, :, 0] + self.window_size[0] - 1
) # shift to start from 0
relative_coords[:, :, 1] = relative_coords[:, :, 1] + self.window_size[1] - 1
relative_coords[:, :, 0] = relative_coords[:, :, 0] * (2 * self.window_size[1] - 1)
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer(
"relative_position_index",
relative_position_index.to_global(
placement=dist.get_layer_placement(layer_idx),
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
),
)
self.qkv = Linear(dim, dim * 3, bias=qkv_bias, layer_idx=layer_idx)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = Linear(dim, dim, layer_idx=layer_idx)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(dim=-1)
self.fused_bias_add_dropout = fused_bias_add_dropout
self.p = proj_drop
def forward(self, x, mask):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B_, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
# attn = flow.matmul(q, k.transpose(-2, -1))
attn = flow.matmul(q, k, transpose_b=True)
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)
].view(
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1],
-1,
) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1
).contiguous() # nH, Wh*Ww, Wh*Ww
unsqueeze_relative_position_bias = relative_position_bias.unsqueeze(0)
attn = attn + unsqueeze_relative_position_bias
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = flow.matmul(attn, v).transpose(1, 2).reshape(B_, N, C)
if self.fused_bias_add_dropout:
x = flow._C.matmul(x, self.proj.weight, transpose_a=False, transpose_b=True)
x = flow._C.fused_bias_add_dropout(x, self.proj.bias, p=self.p, axis=2)
else:
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerBlock(nn.Module):
"""Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: libai.layers.LayerNorm
"""
def __init__(
self,
dim,
input_resolution,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=LayerNorm,
layer_idx=0,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
self.layer_idx = layer_idx
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim, layer_idx=layer_idx)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
fused_bias_add_dropout=True,
layer_idx=layer_idx,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim, layer_idx=layer_idx)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(
hidden_size=dim,
ffn_hidden_size=mlp_hidden_dim,
output_dropout_prob=drop,
bias_gelu_fusion=True,
bias_dropout_fusion=True,
layer_idx=layer_idx,
)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = flow.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
w_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt = cnt + 1
mask_windows = window_partition(
img_mask, self.window_size
) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
attn_mask == 0, float(0.0)
)
attn_mask = attn_mask.to_global(
placement=dist.get_layer_placement(layer_idx),
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
)
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = flow.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(
shifted_x, self.window_size
) # nW*B, window_size, window_size, C
x_windows = x_windows.view(
-1, self.window_size * self.window_size, C
) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows, self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = flow.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchMerging(nn.Module):
"""Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: libai.layers.LayerNorm
"""
def __init__(self, input_resolution, dim, norm_layer=LayerNorm, layer_idx=0):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = Linear(4 * dim, 2 * dim, bias=False, layer_idx=layer_idx)
self.norm = norm_layer(4 * dim, layer_idx=layer_idx)
self.layer_idx = layer_idx
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = flow.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
class PatchEmbed(nn.Module):
"""Image to Patch Embedding
Args:
img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(
self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, layer_idx=0
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0],
img_size[1] // patch_size[1],
]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
).to_global(
placement=dist.get_layer_placement(layer_idx),
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
)
if norm_layer is not None:
self.norm = norm_layer(embed_dim, layer_idx=layer_idx)
else:
self.norm = None
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
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]})."
x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
class BasicLayer(nn.Module):
"""A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: libai.layers.LayerNorm
downsample (nn.Module | None, optional): Downsample at the end of the layer. Default: None
"""
def __init__(
self,
dim,
input_resolution,
depth,
num_heads,
window_size,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
norm_layer=LayerNorm,
downsample=None,
layer_id_offset=0,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.layer_id_offset = layer_id_offset
# build blocks
self.blocks = nn.ModuleList(
[
SwinTransformerBlock(
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
layer_idx=layer_id_offset + i,
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution,
dim=dim,
norm_layer=norm_layer,
layer_idx=layer_id_offset + depth - 1,
)
else:
self.downsample = None
def forward(self, x):
layer_idx = self.layer_id_offset
for i in range(len(self.blocks)):
x = x.to_global(placement=dist.get_layer_placement(layer_idx))
x = self.blocks[i](x)
layer_idx += 1
if self.downsample is not None:
x = self.downsample(x)
return x
[docs]class SwinTransformer(nn.Module):
"""Swin Transformer in LiBai.
LiBai implement of:
`Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
<https://arxiv.org/pdf/2103.14030>`_
Args:
img_size (int, tuple(int)): Input image size. Default 224
patch_size (int, tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: libai.layers.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
loss_func (callable, optional): Loss function for computing the total loss
between logits and labels
"""
@configurable
def __init__(
self,
img_size=224,
patch_size=4,
in_chans=3,
num_classes=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=LayerNorm,
ape=False,
patch_norm=True,
loss_func=None,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None,
layer_idx=0,
)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(
flow.zeros(1, num_patches, embed_dim),
placement=dist.get_layer_placement(0),
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
)
trunc_normal_(self.absolute_pos_embed, std=0.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in flow.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
layer_id_offset = 0
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2 ** i_layer),
input_resolution=(
patches_resolution[0] // (2 ** i_layer),
patches_resolution[1] // (2 ** i_layer),
),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
layer_id_offset=layer_id_offset,
)
layer_id_offset += depths[i_layer]
self.layers.append(layer)
self.norm = norm_layer(self.num_features, layer_idx=-1)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.head = (
Linear(self.num_features, num_classes, layer_idx=-1)
if num_classes > 0
else nn.Identity()
)
# Loss func
self.loss_func = nn.CrossEntropyLoss() if loss_func is None else loss_func
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@classmethod
def from_config(cls, cfg):
return {
"img_size": cfg.img_size,
"patch_size": cfg.patch_size,
"in_chans": cfg.in_chans,
"num_classes": cfg.num_classes,
"embed_dim": cfg.embed_dim,
"depths": cfg.depths,
"num_heads": cfg.num_heads,
"window_size": cfg.window_size,
"mlp_ratio": cfg.mlp_ratio,
"qkv_bias": cfg.qkv_bias,
"qk_scale": cfg.qk_scale,
"drop_rate": cfg.drop_rate,
"drop_path_rate": cfg.drop_path_rate,
"ape": cfg.ape,
"patch_norm": cfg.patch_norm,
"loss_func": cfg.loss_func,
}
def forward_features(self, x):
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = flow.flatten(x, 1)
return x
[docs] def forward(self, images, labels=None):
"""
Args:
images (flow.Tensor): training samples.
labels (flow.LongTensor, optional): training targets
Returns:
dict:
A dict containing :code:`loss_value` or :code:`logits`
depending on training or evaluation mode.
:code:`{"losses": loss_value}` when training,
:code:`{"prediction_scores": logits}` when evaluating.
"""
x = self.forward_features(images)
x = self.head(x)
if labels is not None and self.training:
losses = self.loss_func(x, labels)
return {"losses": losses}
else:
return {"prediction_scores": x}
@staticmethod
def set_pipeline_stage_id(model):
dist_utils = dist.get_dist_util()
# Set pipeline parallelism stage_id
if hasattr(model.patch_embed, "config"):
# Old API in OneFlow 0.8
model.patch_embed.config.set_stage(
dist_utils.get_layer_stage_id(0), dist.get_layer_placement(0)
)
model.pos_drop.config.set_stage(
dist_utils.get_layer_stage_id(0), dist.get_layer_placement(0)
)
for module_block in model.modules():
if isinstance(module_block.origin, SwinTransformerBlock):
module_block.config.set_stage(
dist_utils.get_layer_stage_id(module_block.layer_idx),
dist.get_layer_placement(module_block.layer_idx),
)
elif isinstance(module_block.origin, PatchMerging):
module_block.config.set_stage(
dist_utils.get_layer_stage_id(module_block.layer_idx),
dist.get_layer_placement(module_block.layer_idx),
)
model.norm.config.set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.head.config.set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.avgpool.config.set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.loss_func.config.set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
else:
model.patch_embed.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(0), dist.get_layer_placement(0)
)
model.pos_drop.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(0), dist.get_layer_placement(0)
)
for module_block in model.modules():
if isinstance(module_block.to(nn.Module), SwinTransformerBlock):
module_block.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(module_block.layer_idx),
dist.get_layer_placement(module_block.layer_idx),
)
elif isinstance(module_block.to(nn.Module), PatchMerging):
module_block.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(module_block.layer_idx),
dist.get_layer_placement(module_block.layer_idx),
)
model.norm.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.head.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.avgpool.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
model.loss_func.to(flow.nn.graph.GraphModule).set_stage(
dist_utils.get_layer_stage_id(-1), dist.get_layer_placement(-1)
)
@staticmethod
def set_activation_checkpoint(model):
for module_block in model.modules():
if hasattr(module_block, "origin"):
# Old API in OneFlow 0.8
if isinstance(module_block.origin, SwinTransformerBlock):
module_block.config.activation_checkpointing = True
else:
if isinstance(module_block.to(nn.Module), SwinTransformerBlock):
module_block.to(flow.nn.graph.GraphModule).activation_checkpointing = True