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import torch
import xformers.ops as xops
from torch import nn
from transformers.activations import ACT2FN
class PatchEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.proj = nn.Conv2d(
config.in_channels,
config.hidden_size,
kernel_size=config.patch_size,
stride=config.patch_size,
)
self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
self.position_embedding = nn.Embedding(config.num_positions, config.hidden_size)
def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
x = self.proj(images)
x = x.flatten(2).transpose(1, 2)
cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x += self.position_embedding.weight.unsqueeze(0)
return x
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_heads = config.num_heads
head_dim = config.hidden_size // config.num_heads
self.scale = head_dim**-0.5
self.query_key_value = nn.Linear(config.hidden_size, config.hidden_size * 3)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.output_dropout = torch.nn.Dropout(config.dropout_prob)
def forward(self, x: "tensor(B, L, D)") -> "tensor(B, L, D)":
B, L, _ = x.shape
qkv = self.query_key_value(x)
qkv = qkv.reshape(B, L, 3, self.num_heads, -1).permute(
2, 0, 1, 3, 4
) # 3, B, L, H, D
q, k, v = qkv[0], qkv[1], qkv[2]
out = xops.memory_efficient_attention(
q,
k,
v,
scale=self.scale,
)
output = self.dense(out.view(B, L, -1))
output = self.output_dropout(output)
return output
def attention(self, q, k, v):
attn_weights = torch.matmul(q * self.scale, k.transpose(-2, -1))
attn_weights = attn_weights.softmax(dim=-1)
output = torch.matmul(attn_weights, v)
return output
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x)
x = self.activation_fn(x)
x = self.fc2(x)
return x
class TransformerLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.input_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
self.attention = Attention(config)
self.mlp = MLP(config)
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
def forward(self, hidden_states):
attention_input = hidden_states
attention_output = self.input_layernorm(self.attention(attention_input))
hidden_states = attention_input + attention_output
mlp_input = hidden_states
mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
output = mlp_input + mlp_output
return output
class Transformer(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList(
[TransformerLayer(config) for _ in range(config.num_hidden_layers)]
)
def forward(self, hidden_states):
for layer_module in self.layers:
hidden_states = layer_module(hidden_states)
return hidden_states
class GLU(nn.Module):
def __init__(self, config, in_features):
super().__init__()
self.linear_proj = nn.Linear(in_features, config.hidden_size, bias=False)
self.norm1 = nn.LayerNorm(config.hidden_size)
self.act1 = nn.GELU()
self.act2 = nn.functional.silu
self.dense_h_to_4h = nn.Linear(
config.hidden_size, config.intermediate_size, bias=False
)
self.gate_proj = nn.Linear(
config.hidden_size, config.intermediate_size, bias=False
)
self.dense_4h_to_h = nn.Linear(
config.intermediate_size, config.hidden_size, bias=False
)
def forward(self, x):
x = self.linear_proj(x)
x = self.act1(self.norm1(x))
x = self.act2(self.gate_proj(x)) * self.dense_h_to_4h(x)
x = self.dense_4h_to_h(x)
return x
class EVA2CLIPModel(nn.Module):
def __init__(self, config):
super().__init__()
vision_config = Namespace(**config.vision_config)
self.patch_embedding = PatchEmbedding(vision_config)
self.transformer = Transformer(vision_config)
self.linear_proj = GLU(config, in_features=vision_config.hidden_size)
self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.pos_embed = nn.Parameter(
torch.zeros(
(vision_config.image_size // vision_config.patch_size) ** 2,
vision_config.hidden_size,
)
)
def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
x = self.patch_embedding(images)
x = self.transformer(x)
x = x[:, 1:]
x = self.linear_proj(x + self.pos_embed.to(x.device).unsqueeze(0))
boi = self.boi.to(x.device).expand(x.shape[0], -1, -1)
eoi = self.eoi.to(x.device).expand(x.shape[0], -1, -1)
x = torch.cat((boi, x, eoi), dim=1)
return x
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