File size: 6,302 Bytes
c627d45 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
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)
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:
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"
main_input_name = "pixel_values"
_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,
)
|