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import torch
import torch.nn as nn
from typing import Optional, Tuple
from .siglip_config import SigLipConfig
class SiglipTransformer(nn.Module):
def __init__(self, config: SigLipConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = SigLipEmbeddings(config)
self.encoder = SiglipEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
hidden_states = self.embeddings(pixel_values)
last_hidden_state = self.encoder(inputs_embeds=hidden_states)
last_hidden_state = self.post_layernorm(last_hidden_state)
return last_hidden_state
class SiglipEncoder(nn.Module):
def __init__(self, config: SigLipConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[SigLipEncoderLayer(config) for _ in range(config.num_hidden_layers)]
)
def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
hidden_states = inputs_embeds
for encoder_layer in self.layers:
hidden_states = encoder_layer(hidden_states)
return hidden_states
class SigLipEncoderLayer(nn.Module):
def __init__(self, config: SigLipConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = SigLipAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = SigLipMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, _ = self.self_attn(hidden_states=hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
class SigLipMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = nn.functional.gelu(hidden_states, approximate="tanh")
hidden_states = self.fc2(hidden_states)
return hidden_states
class SigLipAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
batch_size, seq_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
attn_weights = (torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale)
if attn_weights.size() != (batch_size, self.num_heads, seq_len, seq_len):
raise ValueError(
f"Attention weights should be of size {(batch_size, self.num_heads, seq_len, seq_len)}, but is"
f" {attn_weights.size()}"
)
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (batch_size, self.num_heads, seq_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(batch_size, self.num_heads, seq_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class SigLipEmbeddings(nn.Module):
def __init__(self, config: SigLipConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid"
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer(
"position_ids",
torch.arange(self.num_positions).expand((1, -1)),
persistent=False,
)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
patch_embeds = self.patch_embedding(pixel_values)
embeddings = patch_embeds.flatten(2).transpose(1, 2)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings |