| |
| from typing import Optional, Tuple, Union |
|
|
| import torch |
| from .configuration_aimv2 import AIMv2Config |
| from torch import nn |
| from torch.nn import functional as F |
| from transformers.modeling_outputs import BaseModelOutputWithNoAttention |
| from transformers.modeling_utils import PreTrainedModel |
|
|
| __all__ = ["AIMv2Model"] |
|
|
|
|
| class RMSNorm(nn.Module): |
| def __init__(self, dim: int, eps: float = 1e-6): |
| super().__init__() |
| self.weight = nn.Parameter(torch.ones(dim)) |
| self.eps = eps |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| output = self._norm(x.float()).type_as(x) |
| return output * self.weight |
|
|
| def extra_repr(self) -> str: |
| return f"{tuple(self.weight.shape)}, eps={self.eps}" |
|
|
| def _norm(self, x: torch.Tensor) -> torch.Tensor: |
| return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) |
|
|
|
|
| class AIMv2SwiGLUFFN(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| hidden_features = config.intermediate_size |
| in_features = config.hidden_size |
| bias = config.use_bias |
|
|
| self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) |
| self.fc2 = nn.Linear(hidden_features, in_features, bias=bias) |
| self.fc3 = nn.Linear(in_features, hidden_features, bias=bias) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| x = F.silu(self.fc1(x)) * self.fc3(x) |
| x = self.fc2(x) |
| return x |
|
|
|
|
| class AIMv2PatchEmbed(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| self.proj = nn.Conv2d( |
| config.num_channels, |
| config.hidden_size, |
| kernel_size=(config.patch_size, config.patch_size), |
| stride=(config.patch_size, config.patch_size), |
| ) |
| self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| x = self.proj(x).flatten(2).transpose(1, 2) |
| x = self.norm(x) |
| return x |
|
|
|
|
| class AIMv2ViTPreprocessor(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| num_patches = (config.image_size // config.patch_size) ** 2 |
|
|
| self.patchifier = AIMv2PatchEmbed(config) |
| self.pos_embed = nn.Parameter(torch.zeros((1, num_patches, config.hidden_size))) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| tokens = self.patchifier(x) |
| _, N, _ = tokens.shape |
| pos_embed = self.pos_embed.to(tokens.device) |
| tokens = tokens + pos_embed[:, :N] |
| return tokens |
|
|
|
|
| class AIMv2Attention(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| dim = config.hidden_size |
|
|
| self.num_heads = config.num_attention_heads |
| self.qkv = nn.Linear(dim, dim * 3, bias=config.qkv_bias) |
| self.attn_drop = nn.Dropout(config.attention_dropout) |
| self.proj = nn.Linear(dim, dim, bias=config.use_bias) |
| self.proj_drop = nn.Dropout(config.projection_dropout) |
|
|
| def forward( |
| self, x: torch.Tensor, mask: Optional[torch.Tensor] = None |
| ) -> torch.Tensor: |
| 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.unbind(0) |
|
|
| x = F.scaled_dot_product_attention(q, k, v, attn_mask=mask) |
| x = x.transpose(1, 2).contiguous().reshape(B, N, C) |
| x = self.proj(x) |
| x = self.proj_drop(x) |
| return x |
|
|
|
|
| class AIMv2Block(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| self.attn = AIMv2Attention(config) |
| self.norm_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| self.mlp = AIMv2SwiGLUFFN(config) |
| self.norm_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
|
| def forward( |
| self, x: torch.Tensor, mask: Optional[torch.Tensor] = None |
| ) -> torch.Tensor: |
| x = x + self.attn(self.norm_1(x), mask) |
| x = x + self.mlp(self.norm_2(x)) |
| return x |
|
|
|
|
| class AIMv2Transformer(nn.Module): |
| def __init__(self, config: AIMv2Config): |
| super().__init__() |
| self.blocks = nn.ModuleList( |
| [AIMv2Block(config) for _ in range(config.num_hidden_layers)] |
| ) |
| self.post_trunk_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| self.gradient_checkpointing = False |
|
|
| def forward( |
| self, |
| tokens: torch.Tensor, |
| mask: Optional[torch.Tensor] = None, |
| output_hidden_states: bool = False, |
| ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, ...]]]: |
| hidden_states = () if output_hidden_states else None |
| for block in self.blocks: |
| if self.gradient_checkpointing and self.training: |
| tokens = self._gradient_checkpointing_func(block.__call__, tokens, mask) |
| else: |
| tokens = block(tokens, mask) |
| if output_hidden_states: |
| hidden_states += (tokens,) |
| tokens = self.post_trunk_norm(tokens) |
| return tokens, hidden_states |
|
|
|
|
| class AIMv2PretrainedModel(PreTrainedModel): |
| config_class = AIMv2Config |
| base_model_prefix = "aimv2" |
| supports_gradient_checkpointing = True |
| main_input_name = "pixel_values" |
| _no_split_modules = ["AIMv2ViTPreprocessor", "AIMv2Block"] |
| _supports_sdpa = True |
|
|
|
|
| class AIMv2Model(AIMv2PretrainedModel): |
| def __init__(self, config: AIMv2Config): |
| super().__init__(config) |
| self.preprocessor = AIMv2ViTPreprocessor(config) |
| self.trunk = AIMv2Transformer(config) |
|
|
| def forward( |
| self, |
| pixel_values: torch.Tensor, |
| mask: Optional[torch.Tensor] = None, |
| output_hidden_states: Optional[bool] = None, |
| return_dict: Optional[bool] = None, |
| ) -> Union[ |
| Tuple[torch.Tensor], |
| Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], |
| BaseModelOutputWithNoAttention, |
| ]: |
| if output_hidden_states is None: |
| output_hidden_states = self.config.output_hidden_states |
| if return_dict is None: |
| return_dict = self.config.use_return_dict |
|
|
| x = self.preprocessor(pixel_values) |
| x, hidden_states = self.trunk( |
| x, mask, output_hidden_states=output_hidden_states |
| ) |
|
|
| if not return_dict: |
| res = (x,) |
| res += (hidden_states,) if output_hidden_states else () |
| return res |
|
|
| return BaseModelOutputWithNoAttention( |
| last_hidden_state=x, |
| hidden_states=hidden_states, |
| ) |
|
|
|
|
