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import torch
from torch import nn
from argparse import Namespace
import torch.nn.functional as F
from transformers.activations import ACT2FN
import math
def standard_attention(query_layer, key_layer, value_layer, scaling_attention_score=True):
if scaling_attention_score:
query_layer = query_layer / math.sqrt(query_layer.shape[-1])
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_probs = F.softmax(attention_scores, dim=-1)
context_layer = torch.matmul(attention_probs, value_layer)
return context_layer
def attention_fn_default(query_layer, key_layer, value_layer, scaling_attention_score=True):
# expand head dim to query dim, if necessary
# only useful for multi-query attention
batch_size, num_query_heads = query_layer.shape[:2] # [b, np, s, hn]
num_kv_heads = key_layer.shape[1] # [b, np, s, hn]
key_layer = key_layer.unsqueeze(2).expand(-1, -1, num_query_heads//num_kv_heads, -1, -1).contiguous().view(batch_size, num_query_heads, *key_layer.shape[2:])
value_layer = value_layer.unsqueeze(2).expand(-1, -1, num_query_heads//num_kv_heads, -1, -1).contiguous().view(batch_size, num_query_heads, *value_layer.shape[2:])
if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score:
# Pytorch 2.0 attention uses very much memory if attention_mask is float, and has NaN bug if attention_mask is None.
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_layer, key_layer, value_layer,
attn_mask=None,
dropout_p=0.,
is_causal=False
)
return attn_output
else:
return standard_attention(
query_layer, key_layer, value_layer, scaling_attention_score=scaling_attention_score
)
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, 3, 1, 4) # 3, B, H, L, D
q, k, v = qkv[0], qkv[1], qkv[2]
out = attention_fn_default(
q, k, v
) # 24 x 3 x
out = out.transpose(2, 1)
# breakpoint()
# output = self.dense(out.reshape(B, L, -1))
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.conv = nn.Conv2d(in_channels=vision_config.hidden_size, out_channels=vision_config.hidden_size, kernel_size=2, stride=2)
self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.eoi = nn.Parameter(torch.zeros(1, 1, 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:]
b, s, h = x.shape
grid_size = int(s**0.5)
x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)
x = self.conv(x)
x = x.flatten(2).transpose(1, 2)
x = self.linear_proj(x)
boi = self.boi.expand(x.shape[0], -1, -1)
eoi = self.eoi.expand(x.shape[0], -1, -1)
x = torch.cat((boi, x, eoi), dim=1)
return x
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