File size: 6,619 Bytes
9c1d2b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
# Copyright 2024 Rhymes AI. All rights reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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 torch
import torch.nn as nn
from torch.nn.init import trunc_normal_
from transformers.activations import ACT2FN
class FFN(nn.Module):
"""
Feed-Forward Network module.
Args:
embed_dim (int): Input embedding dimension.
ff_dim (int): Hidden dimension of the feed-forward network.
output_dim (int): Output dimension.
"""
def __init__(self, embed_dim, ff_dim, output_dim):
super().__init__()
self.linear_in = nn.Linear(embed_dim, ff_dim, bias=False)
self.linear_out = nn.Linear(ff_dim, output_dim, bias=False)
self.act = ACT2FN["gelu_new"]
def forward(self, hidden_states):
hidden_states = self.act(self.linear_in(hidden_states))
hidden_states = self.linear_out(hidden_states)
return hidden_states
class CrossAttention(nn.Module):
"""
Cross-Attention module.
Args:
kv_dim (int): Dimension of key and value.
embed_dim (int): Embedding dimension.
num_heads (int): Number of attention heads.
drop_out_rate (float): Dropout rate. Default is 0.
"""
def __init__(self, kv_dim, embed_dim, num_heads, drop_out_rate=0):
super().__init__()
self.num_heads = num_heads
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.k_proj = nn.Linear(kv_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(kv_dim, embed_dim, bias=False)
self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
self.linear = nn.Linear(embed_dim, embed_dim)
self.dropout = nn.Dropout(drop_out_rate)
self.layer_norm = nn.LayerNorm(embed_dim)
self.ln_kv = nn.LayerNorm(kv_dim)
def forward(self, x, hidden_states, attn_mask=None, add_residual=False):
"""
Forward pass of the CrossAttention module.
Args:
x (torch.Tensor): Input tensor for key and value.
hidden_states (torch.Tensor): Input tensor for query.
attn_mask (torch.Tensor, optional): Attention mask. Default is None.
add_residual (bool): Whether to add residual connection. Default is False.
Returns:
torch.Tensor: Output tensor after cross-attention.
"""
normed_hidden_states = self.layer_norm(hidden_states)
query = self.q_proj(normed_hidden_states).permute(1, 0, 2)
x = self.ln_kv(x)
key = self.k_proj(x).permute(1, 0, 2)
value = self.v_proj(x).permute(1, 0, 2)
attn_output, _ = self.multihead_attn(query, key, value, attn_mask=attn_mask)
attn_output = attn_output.permute(1, 0, 2)
if add_residual:
attn_output = hidden_states + self.dropout(self.linear(attn_output))
else:
attn_output = self.dropout(self.linear(attn_output))
return attn_output
class AriaProjector(nn.Module):
"""
A projection module with one cross attention layer and one FFN layer, which projects ViT's outputs into MoE's inputs.
Args:
patch_to_query_dict (dict): Maps patch numbers to their corresponding query numbers,
e.g., {1225: 128, 4900: 256}. This allows for different query sizes based on image resolution.
embed_dim (int): Embedding dimension.
num_heads (int): Number of attention heads.
kv_dim (int): Dimension of key and value.
ff_dim (int): Hidden dimension of the feed-forward network.
output_dim (int): Output dimension.
norm_layer (nn.Module): Normalization layer. Default is nn.LayerNorm.
Outputs:
A tensor with the shape of (batch_size, query_number, output_dim)
"""
def __init__(
self,
patch_to_query_dict,
embed_dim,
num_heads,
kv_dim,
ff_dim,
output_dim,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.patch_to_query_dict = patch_to_query_dict
self.embed_dim = embed_dim
self.num_heads = num_heads
self.query = nn.Parameter(
torch.zeros(max(patch_to_query_dict.values()), self.embed_dim)
)
trunc_normal_(self.query, std=0.02)
self.cross_attn = CrossAttention(kv_dim, embed_dim, num_heads)
self.ln_ffn = norm_layer(embed_dim)
self.ffn = FFN(embed_dim, ff_dim, output_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, attn_mask=None):
"""
Forward pass of the Projector module.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_patches, kv_dim).
attn_mask (torch.Tensor, optional): Attention mask. Default is None.
Returns:
torch.Tensor: Output tensor of shape (batch_size, query_number, output_dim).
"""
bs = x.shape[0]
queries = self.query.unsqueeze(0).repeat(bs, 1, 1)
query_num = self.patch_to_query_dict.get(x.shape[1], None)
assert (
query_num is not None
), f"Query number for {x.shape[1]} patches is not provided"
queries = queries[:, :query_num, :]
if attn_mask is not None:
attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
attention_out = self.cross_attn(x, queries, attn_mask=attn_mask)
out = self.ffn(self.ln_ffn(attention_out))
return out
|