import torch from torch import nn from argparse import Namespace import torch.nn.functional as F from transformers.activations import ACT2FN import math from torch.nn import LayerNorm 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): 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 ) output = self.dense(out.transpose(1, 2).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 = LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = Attention(config) self.mlp = MLP(config) self.post_attention_layernorm = 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.ffn_hidden_size, bias=False) self.gate_proj = nn.Linear(config.hidden_size, config.ffn_hidden_size, bias=False) self.dense_4h_to_h = nn.Linear(config.ffn_hidden_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=config.hidden_size) self.conv = nn.Conv2d(in_channels=vision_config.hidden_size, out_channels=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)) self.scaling_factor = vision_config.scaling_factor 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) x = x / self.scaling_factor return x