362 lines
9.8 KiB
Python
362 lines
9.8 KiB
Python
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import torch
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import torch.nn as nn
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from timm.models.vision_transformer import Mlp
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from .act import build_act, get_act_name
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from .norms import build_norm, get_norm_name
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from .utils import get_same_padding, val2tuple
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class ConvLayer(nn.Module):
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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kernel_size=3,
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stride=1,
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dilation=1,
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groups=1,
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padding: int or None = None,
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use_bias=False,
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dropout=0.0,
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norm="bn2d",
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act="relu",
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):
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super().__init__()
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if padding is None:
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padding = get_same_padding(kernel_size)
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padding *= dilation
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.kernel_size = kernel_size
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self.stride = stride
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self.dilation = dilation
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self.groups = groups
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self.padding = padding
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self.use_bias = use_bias
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self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
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self.conv = nn.Conv2d(
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in_dim,
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out_dim,
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kernel_size=(kernel_size, kernel_size),
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stride=(stride, stride),
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padding=padding,
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dilation=(dilation, dilation),
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groups=groups,
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bias=use_bias,
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)
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self.norm = build_norm(norm, num_features=out_dim)
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self.act = build_act(act)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.dropout is not None:
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x = self.dropout(x)
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x = self.conv(x)
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if self.norm:
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x = self.norm(x)
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if self.act:
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x = self.act(x)
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return x
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class GLUMBConv(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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out_feature=None,
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kernel_size=3,
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stride=1,
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padding: int or None = None,
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use_bias=False,
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norm=(None, None, None),
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act=("silu", "silu", None),
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dilation=1,
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):
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out_feature = out_feature or in_features
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super().__init__()
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use_bias = val2tuple(use_bias, 3)
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norm = val2tuple(norm, 3)
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act = val2tuple(act, 3)
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self.glu_act = build_act(act[1], inplace=False)
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self.inverted_conv = ConvLayer(
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in_features,
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hidden_features * 2,
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1,
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use_bias=use_bias[0],
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norm=norm[0],
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act=act[0],
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)
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self.depth_conv = ConvLayer(
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hidden_features * 2,
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hidden_features * 2,
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kernel_size,
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stride=stride,
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groups=hidden_features * 2,
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padding=padding,
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use_bias=use_bias[1],
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norm=norm[1],
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act=None,
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dilation=dilation,
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)
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self.point_conv = ConvLayer(
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hidden_features,
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out_feature,
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1,
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use_bias=use_bias[2],
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norm=norm[2],
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act=act[2],
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)
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# from IPython import embed; embed(header='debug dilate conv')
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def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
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B, N, C = x.shape
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if HW is None:
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H = W = int(N**0.5)
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else:
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H, W = HW
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x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
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x = self.inverted_conv(x)
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x = self.depth_conv(x)
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x, gate = torch.chunk(x, 2, dim=1)
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gate = self.glu_act(gate)
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x = x * gate
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x = self.point_conv(x)
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x = x.reshape(B, C, N).permute(0, 2, 1)
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return x
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class SlimGLUMBConv(GLUMBConv):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# 移除 self.inverted_conv 层
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del self.inverted_conv
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self.out_dim = self.point_conv.out_dim
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def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
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B, N, C = x.shape
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if HW is None:
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H = W = int(N**0.5)
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else:
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H, W = HW
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# 直接使用 x，跳过 self.inverted_conv 层的调用
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x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
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# x = self.inverted_conv(x)
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x = self.depth_conv(x)
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x, gate = torch.chunk(x, 2, dim=1)
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gate = self.glu_act(gate)
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x = x * gate
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x = self.point_conv(x)
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x = x.reshape(B, self.out_dim, N).permute(0, 2, 1)
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return x
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class MBConvPreGLU(nn.Module):
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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kernel_size=3,
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stride=1,
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mid_dim=None,
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expand=6,
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padding: int or None = None,
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use_bias=False,
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norm=(None, None, "ln2d"),
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act=("silu", "silu", None),
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):
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super().__init__()
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use_bias = val2tuple(use_bias, 3)
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norm = val2tuple(norm, 3)
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act = val2tuple(act, 3)
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mid_dim = mid_dim or round(in_dim * expand)
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self.inverted_conv = ConvLayer(
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in_dim,
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mid_dim * 2,
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1,
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use_bias=use_bias[0],
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norm=norm[0],
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act=None,
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)
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self.glu_act = build_act(act[0], inplace=False)
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self.depth_conv = ConvLayer(
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mid_dim,
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mid_dim,
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kernel_size,
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stride=stride,
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groups=mid_dim,
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padding=padding,
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use_bias=use_bias[1],
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norm=norm[1],
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act=act[1],
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)
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self.point_conv = ConvLayer(
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mid_dim,
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out_dim,
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1,
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use_bias=use_bias[2],
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norm=norm[2],
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act=act[2],
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)
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def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
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B, N, C = x.shape
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if HW is None:
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H = W = int(N**0.5)
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else:
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H, W = HW
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x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
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x = self.inverted_conv(x)
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x, gate = torch.chunk(x, 2, dim=1)
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gate = self.glu_act(gate)
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x = x * gate
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x = self.depth_conv(x)
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x = self.point_conv(x)
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x = x.reshape(B, C, N).permute(0, 2, 1)
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return x
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@property
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def module_str(self) -> str:
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_str = f"{self.depth_conv.kernel_size}{type(self).__name__}("
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_str += f"in={self.inverted_conv.in_dim},mid={self.depth_conv.in_dim},out={self.point_conv.out_dim},s={self.depth_conv.stride}"
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_str += (
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f",norm={get_norm_name(self.inverted_conv.norm)}"
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f"+{get_norm_name(self.depth_conv.norm)}"
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f"+{get_norm_name(self.point_conv.norm)}"
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)
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_str += (
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f",act={get_act_name(self.inverted_conv.act)}"
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f"+{get_act_name(self.depth_conv.act)}"
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f"+{get_act_name(self.point_conv.act)}"
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)
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_str += f",glu_act={get_act_name(self.glu_act)})"
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return _str
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class DWMlp(Mlp):
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"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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bias=True,
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drop=0.0,
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kernel_size=3,
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stride=1,
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dilation=1,
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padding=None,
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):
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super().__init__(
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in_features=in_features,
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hidden_features=hidden_features,
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out_features=out_features,
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act_layer=act_layer,
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bias=bias,
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drop=drop,
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)
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hidden_features = hidden_features or in_features
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self.hidden_features = hidden_features
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if padding is None:
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padding = get_same_padding(kernel_size)
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padding *= dilation
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self.conv = nn.Conv2d(
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hidden_features,
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hidden_features,
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kernel_size=(kernel_size, kernel_size),
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stride=(stride, stride),
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padding=padding,
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dilation=(dilation, dilation),
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groups=hidden_features,
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bias=bias,
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)
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def forward(self, x, HW=None):
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B, N, C = x.shape
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if HW is None:
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H = W = int(N**0.5)
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else:
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H, W = HW
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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x = x.reshape(B, H, W, self.hidden_features).permute(0, 3, 1, 2)
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x = self.conv(x)
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x = x.reshape(B, self.hidden_features, N).permute(0, 2, 1)
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x = self.fc2(x)
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x = self.drop2(x)
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return x
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class Mlp(Mlp):
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"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0):
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super().__init__(
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in_features=in_features,
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hidden_features=hidden_features,
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out_features=out_features,
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act_layer=act_layer,
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bias=bias,
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drop=drop,
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)
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def forward(self, x, HW=None):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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x = self.fc2(x)
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x = self.drop2(x)
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return x
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if __name__ == "__main__":
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model = GLUMBConv(
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1152,
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1152 * 4,
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1152,
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use_bias=(True, True, False),
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norm=(None, None, None),
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act=("silu", "silu", None),
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).cuda()
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input = torch.randn(4, 256, 1152).cuda()
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output = model(input)
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