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from typing import Optional, Tuple, Union
import torch
import dataclasses
import math
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.utils import ModelOutput
from .configuration_aimv2 import AIMv2Config, AIMv2VisionConfig, AIMv2TextConfig
from torch import nn
from torch.nn import functional as F
from transformers.modeling_outputs import BaseModelOutputWithNoAttention
from transformers.modeling_utils import PreTrainedModel
__all__ = ["AIMv2VisionModel", "AIMv2TextModel", "AIMv2Model"]
AIMv2VisionOrTextConfig = Union[AIMv2VisionConfig, AIMv2TextConfig]
@dataclasses.dataclass
class AIMv2Output(ModelOutput):
logits_per_image: torch.Tensor
logits_per_text: Optional[torch.Tensor] = None
image_features: Optional[torch.Tensor] = None
text_features: Optional[torch.Tensor] = None
vision_output: Optional[BaseModelOutputWithNoAttention] = None
text_output: Optional[BaseModelOutputWithNoAttention] = None
class AIMv2TextPreprocessor(nn.Module):
def __init__(self, config: AIMv2TextConfig):
super().__init__()
self.max_context_length = config.max_context_length
self.eos_token_id = config.eos_token_id
self.text_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.positional_embedding = nn.Parameter(
torch.zeros(self.max_context_length, config.hidden_size)
)
def forward(self, input_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
_, N = input_ids.shape
max_len = min(N, self.max_context_length)
eos_token_mask = input_ids == self.eos_token_id
tokens = self.text_embedding(input_ids)
tokens = tokens[:, :max_len] + self.positional_embedding[:max_len].unsqueeze(0)
return tokens, eos_token_mask
class AIMv2ExtractEOS(nn.Module):
def forward(
self, tokens: torch.Tensor, eos_token_mask: torch.Tensor
) -> torch.Tensor:
B, _, D = tokens.shape
eos_token_mask = torch.argmax(eos_token_mask.float(), dim=-1)
assert eos_token_mask.shape == (B,)
eos_token_mask = eos_token_mask.reshape(B, 1, 1).expand(B, 1, D)
eos_token = torch.gather(tokens, 1, eos_token_mask)
eos_token = eos_token.squeeze(1)
return eos_token
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: AIMv2VisionOrTextConfig):
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: AIMv2VisionOrTextConfig):
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: AIMv2VisionConfig):
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: AIMv2VisionOrTextConfig):
super().__init__()
dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.is_causal = config.is_causal
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)
if mask is None:
x = F.scaled_dot_product_attention(q, k, v, is_causal=self.is_causal)
else:
mask_converter = AttentionMaskConverter(self.is_causal)
mask = mask_converter.to_4d(
mask, key_value_length=N, query_length=N, dtype=q.dtype
)
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: AIMv2VisionOrTextConfig):
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 AIMv2AttentionPoolingHead(nn.Module):
def __init__(self, config: AIMv2VisionConfig):
super().__init__()
dim = config.hidden_size
qkv_bias = config.qkv_bias
self.num_heads = config.num_attention_heads
self.num_queries = config.num_queries
self.k = nn.Linear(dim, dim, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.cls_token = nn.Parameter(torch.randn(1, self.num_queries, dim) * 0.02)
self.linear = nn.Linear(dim, dim, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
cls_token = self.cls_token.expand(B, -1, -1)
q = cls_token.reshape(
B, self.num_queries, self.num_heads, C // self.num_heads
).permute(0, 2, 1, 3)
k = (
self.k(x)
.reshape(B, N, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
v = (
self.v(x)
.reshape(B, N, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
x_cls = F.scaled_dot_product_attention(q, k, v)
x_cls = x_cls.transpose(1, 2).reshape(B, self.num_queries, C)
x_cls = x_cls.mean(dim=1)
out = self.linear(x_cls)
return out
class AIMv2Transformer(nn.Module):
def __init__(self, config: AIMv2VisionOrTextConfig):
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):
base_model_prefix = "aimv2"
_supports_sdpa = True
class AIMv2VisionModel(AIMv2PretrainedModel):
config_class = AIMv2VisionConfig
main_input_name = "pixel_values"
_no_split_modules = ["AIMv2ViTPreprocessor", "AIMv2Block"]
def __init__(self, config: AIMv2VisionConfig):
super().__init__(config)
self.preprocessor = AIMv2ViTPreprocessor(config)
self.trunk = AIMv2Transformer(config)
self.head = AIMv2AttentionPoolingHead(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
)
x = self.head(x)
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,
)
class AIMv2TextModel(AIMv2PretrainedModel):
config_class = AIMv2TextConfig
main_input_name = "input_ids"
_no_split_modules = ["AIMv2TextPreprocessor", "AIMv2Block"]
def __init__(self, config: AIMv2TextConfig):
super().__init__(config)
self.preprocessor = AIMv2TextPreprocessor(config)
self.trunk = AIMv2Transformer(config)
self.head = AIMv2ExtractEOS()
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, eos_token_mask = self.preprocessor(pixel_values)
x, hidden_states = self.trunk(
x, mask, output_hidden_states=output_hidden_states
)
x = self.head(x, eos_token_mask)
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,
)
class AIMv2Model(AIMv2PretrainedModel):
config_class = AIMv2Config
main_input_name = ["input_ids", "pixel_values"]
_no_split_modules = ["AIMv2ViTPreprocessor", "AIMv2TextPreprocessor", "AIMv2Block"]
def __init__(self, config: AIMv2Config):
super().__init__(config)
self.image_encoder = AIMv2VisionModel(config.vision_config)
self.text_encoder = AIMv2TextModel(config.text_config)
self.image_projector = nn.Linear(
config.vision_config.hidden_size, config.projection_dim, bias=False
)
self.text_projector = nn.Linear(
config.text_config.hidden_size, config.projection_dim, bias=False
)
self.log_logit_scale = nn.Parameter(
torch.full([], fill_value=math.log(1.0 / config.init_temperature))
)
self.max_log_logit_scale = math.log(config.max_logit_scale)
def forward(
self,
input_ids: torch.Tensor,
pixel_values: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[
Tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
Union[Tuple[torch.Tensor, ...], BaseModelOutputWithNoAttention],
Union[Tuple[torch.Tensor, ...], BaseModelOutputWithNoAttention],
],
AIMv2Output,
]:
if return_dict is None:
return_dict = self.config.use_return_dict
image_out = self.image_encoder(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_features = image_out.last_hidden_state if return_dict else image_out[0]
image_features = self.image_projector(image_features)
image_features = F.normalize(image_features, p=2, dim=-1)
text_out = self.text_encoder(
input_ids,
mask=attention_mask,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
text_features = text_out.last_hidden_state if return_dict else text_out[0]
text_features = self.text_projector(text_features)
text_features = F.normalize(text_features, p=2, dim=-1)
logit_scale = self.log_logit_scale.clamp(0.0, self.max_log_logit_scale).exp()
logits_per_text = (logit_scale * text_features) @ image_features.t()
logits_per_image = logits_per_text.t()
if not return_dict:
return (
logits_per_image,
logits_per_text,
image_features,
text_features,
image_out,
text_out,
)
return AIMv2Output(
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
image_features=image_features,
text_features=text_features,
vision_output=image_out,
text_output=text_out,
)
def get_image_features(
self,
input_pixels: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
out = self.image_encoder(input_pixels, mask=attention_mask, return_dict=True)
image_features = self.image_projector(out.last_hidden_state)
return image_features
def get_text_features(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
out = self.text_encoder(input_ids, mask=attention_mask, return_dict=True)
text_features = self.text_projector(out.last_hidden_state)
return text_features
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