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|
| 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 |
|
|
