models/basic_var.py

import math
from typing import Optional, List, Union, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F

from models.helpers import DropPath, drop_path

from utils.model_args import ModelArgs
from transformers import AutoImageProcessor, AutoModel

# this file only provides the 3 blocks used in VAR transformer
__all__ = ['FFN', 'AdaLNSelfAttn', 'AdaLNBeforeHead']


# automatically import fused operators
dropout_add_layer_norm = fused_mlp_func = memory_efficient_attention = flash_attn_func = None
try:
    from flash_attn.ops.layer_norm import dropout_add_layer_norm
    from flash_attn.ops.fused_dense import fused_mlp_func
except ImportError: pass
# automatically import faster attention implementations
try: from xformers.ops import memory_efficient_attention
except ImportError: pass
try: from flash_attn import flash_attn_func              # qkv: BLHc, ret: BLHcq
except ImportError: pass
try: from torch.nn.functional import scaled_dot_product_attention as slow_attn    # q, k, v: BHLc
except ImportError:
    def slow_attn(query, key, value, scale: float, attn_mask=None, dropout_p=0.0):
        attn = query.mul(scale) @ key.transpose(-2, -1) # BHLc @ BHcL => BHLL
        if attn_mask is not None: attn.add_(attn_mask)
        return (F.dropout(attn.softmax(dim=-1), p=dropout_p, inplace=True) if dropout_p > 0 else attn.softmax(dim=-1)) @ value




class ConditionEmbedder(nn.Module):
    """
    Embeds Condition into vector representations. Also handles label dropout for classifier-free guidance.
    """
    def __init__(self, in_channels, hidden_size, uncond_prob, token_num=120, vocab_size=16384):
        super().__init__()
        self.cap_proj = MLP(in_features=hidden_size, hidden_features=hidden_size, out_features=hidden_size)
        self.register_buffer("uncond_embedding", torch.zeros(token_num, hidden_size) / hidden_size ** 0.5)
        self.uncond_prob = uncond_prob

    def token_drop(self, caption, force_drop_ids=None, drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            if drop_ids is None:
                drop_ids = torch.rand(caption.shape[0], device=caption.device) < self.uncond_prob
        else:
            drop_ids = force_drop_ids == 1
        if self.uncond_embedding.shape[0] < caption.shape[1]:
            # 动态扩展
            repeat_factor = int(caption.shape[1] / self.uncond_embedding.shape[0]) + 1
            extended = self.uncond_embedding.repeat(repeat_factor, 1)[:caption.shape[1]]
        else:
            extended = self.uncond_embedding[:caption.shape[1]]

        caption = torch.where(drop_ids[:, None, None], extended, caption)

        # caption = torch.where(drop_ids[:, None, None], self.uncond_embedding[:caption.shape[1]], caption)
        return caption

    def forward(self, caption, train, force_drop_ids=None, drop_ids=None):
        use_dropout = self.uncond_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            caption = self.token_drop(caption, force_drop_ids, drop_ids)
        embeddings = self.cap_proj(caption)
        return embeddings

class MLP(nn.Module):
    def __init__(self, in_features, hidden_features, out_features):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features, bias=False)
        self.act = nn.GELU(approximate='tanh')
        self.fc2 = nn.Linear(hidden_features, out_features, bias=False)
        
        nn.init.zeros_(self.fc1.weight)
        nn.init.zeros_(self.fc2.weight)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.fc2(x)
        return x


class Dinov2_Adapter(nn.Module):
    def __init__(self, input_dim=1, output_dim=768, attention=False, pool=False, nheads=8, dropout=0.1, adapter_size='small', condition_type='seg'):
        super(Dinov2_Adapter, self).__init__()
        # print(f"Choose adapter size: {adapter_size}")
        # print(f"condition type: {condition_type}")

        from transformers import logging
        logging.set_verbosity_error()



        self.model = AutoModel.from_pretrained(f'./dinov2_small',local_files_only=True, use_safetensors=False)
        self.condition_type = condition_type
    
    def to_patch14(self, input):
        H, W = input.shape[2:]
        new_H = (H // 16) * 14
        new_W = (W // 16) * 14
        if self.condition_type in ['canny', 'seg']:
            output = torch.nn.functional.interpolate(input, size=(new_H, new_W), mode='nearest')#, align_corners=True)  canny, seg
        else:
            output = torch.nn.functional.interpolate(input, size=(new_H, new_W), mode='bicubic', align_corners=True) # depth, lineart, hed
        return output
        
    def forward(self, x):
        x = self.to_patch14(x)
        x = self.model(x)
        return x.last_hidden_state[:, 1:]


#################################################################################
#                         Cross-Attention    Injection                          #
#################################################################################
class CrossAttentionInjection(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.query_proj = nn.Linear(embed_dim, embed_dim)
        self.key_proj = nn.Linear(embed_dim, embed_dim)
        self.value_proj = nn.Linear(embed_dim, embed_dim)
        self.out_proj = nn.Linear(embed_dim, embed_dim)
        self.num_heads = num_heads
        self.scale = (embed_dim // num_heads) ** -0.5

    def forward(self, x, cond_feat):
        """
        x: [B, L, C]，主特征序列
        cond_feat: [B, L_cond, C]，来自分割图的条件token序列
        """
        B, L, C = x.shape
        H = self.num_heads
        Q = self.query_proj(x).reshape(B, L, H, C // H).transpose(1, 2)  # [B, H, L, d]
        K = self.key_proj(cond_feat).reshape(B, -1, H, C // H).transpose(1, 2)  # [B, H, Lc, d]
        V = self.value_proj(cond_feat).reshape(B, -1, H, C // H).transpose(1, 2)  # [B, H, Lc, d]

        attn = (Q @ K.transpose(-2, -1)) * self.scale  # [B, H, L, Lc]
        attn = attn.softmax(dim=-1)
        out = (attn @ V).transpose(1, 2).reshape(B, L, C)  # [B, L, C]
        return self.out_proj(out)



class FFN(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, drop=0., fused_if_available=True):
        super().__init__()
        self.fused_mlp_func = fused_mlp_func if fused_if_available else None
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = nn.GELU(approximate='tanh')
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop, inplace=True) if drop > 0 else nn.Identity()
    
    def forward(self, x):
        if self.fused_mlp_func is not None:
            return self.drop(self.fused_mlp_func(
                x=x, weight1=self.fc1.weight, weight2=self.fc2.weight, bias1=self.fc1.bias, bias2=self.fc2.bias,
                activation='gelu_approx', save_pre_act=self.training, return_residual=False, checkpoint_lvl=0,
                heuristic=0, process_group=None,
            ))
        else:
            return self.drop(self.fc2( self.act(self.fc1(x)) ))
    
    def extra_repr(self) -> str:
        return f'fused_mlp_func={self.fused_mlp_func is not None}'

 
class SelfAttention(nn.Module):
    def __init__(
        self, block_idx, embed_dim=768, num_heads=12,
        attn_drop=0., proj_drop=0., attn_l2_norm=False, flash_if_available=False,
    ):
        super().__init__()
        assert embed_dim % num_heads == 0
        self.block_idx, self.num_heads, self.head_dim = block_idx, num_heads, embed_dim // num_heads  # =64
        self.attn_l2_norm = attn_l2_norm
        if self.attn_l2_norm:
            self.scale = 1
            self.scale_mul_1H11 = nn.Parameter(torch.full(size=(1, self.num_heads, 1, 1), fill_value=4.0).log(), requires_grad=True)
            self.max_scale_mul = torch.log(torch.tensor(100)).item()
        else:
            self.scale = 0.25 / math.sqrt(self.head_dim)
        
        self.mat_qkv = nn.Linear(embed_dim, embed_dim * 3, bias=False)
        self.q_bias, self.v_bias = nn.Parameter(torch.zeros(embed_dim)), nn.Parameter(torch.zeros(embed_dim))
        self.register_buffer('zero_k_bias', torch.zeros(embed_dim))
        
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = nn.Dropout(proj_drop, inplace=True) if proj_drop > 0 else nn.Identity()
        self.attn_drop: float = attn_drop
        self.using_flash = flash_if_available and flash_attn_func is not None
        # self.using_xform = flash_if_available and memory_efficient_attention is not None
        self.using_xform = False
        # only used during inference
        self.caching, self.cached_k, self.cached_v = False, None, None
    
    def kv_caching(self, enable: bool): self.caching, self.cached_k, self.cached_v = enable, None, None
    
    # NOTE: attn_bias is None during inference because kv cache is enabled
    def forward(self, x, attn_bias):
        B, L, C = x.shape
        
        qkv = F.linear(input=x, weight=self.mat_qkv.weight, bias=torch.cat((self.q_bias, self.zero_k_bias, self.v_bias))).view(B, L, 3, self.num_heads, self.head_dim)
        main_type = qkv.dtype
        # qkv: BL3Hc
        
        using_flash = self.using_flash and attn_bias is None and qkv.dtype != torch.float32
        if using_flash or self.using_xform: q, k, v = qkv.unbind(dim=2); dim_cat = 1   # q or k or v: BLHc
        else: q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0); dim_cat = 2               # q or k or v: BHLc
        
        if self.attn_l2_norm:
            scale_mul = self.scale_mul_1H11.clamp_max(self.max_scale_mul).exp()
            if using_flash or self.using_xform: scale_mul = scale_mul.transpose(1, 2)  # 1H11 to 11H1
            q = F.normalize(q, dim=-1).mul(scale_mul)
            k = F.normalize(k, dim=-1)
        
        if self.caching:
            if self.cached_k is None: self.cached_k = k; self.cached_v = v
            else: k = self.cached_k = torch.cat((self.cached_k, k), dim=dim_cat); v = self.cached_v = torch.cat((self.cached_v, v), dim=dim_cat)
        
        dropout_p = self.attn_drop if self.training else 0.0
        if using_flash:
            oup = flash_attn_func(q.to(dtype=main_type), k.to(dtype=main_type), v.to(dtype=main_type), dropout_p=dropout_p, softmax_scale=self.scale).view(B, L, C)
        elif self.using_xform:
            oup = memory_efficient_attention(q.to(dtype=main_type), k.to(dtype=main_type), v.to(dtype=main_type), attn_bias=None if attn_bias is None else attn_bias.to(dtype=main_type).expand(B, self.num_heads, -1, -1), p=dropout_p, scale=self.scale).view(B, L, C)
        else:
            oup = slow_attn(query=q, key=k, value=v, scale=self.scale, attn_mask=attn_bias, dropout_p=dropout_p).transpose(1, 2).reshape(B, L, C)
        
        return self.proj_drop(self.proj(oup))
        # attn = (q @ k.transpose(-2, -1)).add_(attn_bias + self.local_rpb())  # BHLc @ BHcL => BHLL
        # attn = self.attn_drop(attn.softmax(dim=-1))
        # oup = (attn @ v).transpose_(1, 2).reshape(B, L, -1)     # BHLL @ BHLc = BHLc => BLHc => BLC
    
    def extra_repr(self) -> str:
        return f'using_flash={self.using_flash}, using_xform={self.using_xform}, attn_l2_norm={self.attn_l2_norm}'

config = ModelArgs()
class AdaLNSelfAttn(nn.Module):
    def __init__(
        self, block_idx, last_drop_p, embed_dim, cond_dim, shared_aln: bool, norm_layer,
        num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0., attn_l2_norm=False,
        flash_if_available=False, fused_if_available=True,depth=16,
    ):
        super(AdaLNSelfAttn, self).__init__()
        self.block_idx, self.last_drop_p, self.C = block_idx, last_drop_p, embed_dim
        self.C, self.D = embed_dim, cond_dim
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.attn = SelfAttention(block_idx=block_idx, embed_dim=embed_dim, num_heads=num_heads, attn_drop=attn_drop, proj_drop=drop, attn_l2_norm=attn_l2_norm, flash_if_available=flash_if_available)
        self.ffn = FFN(in_features=embed_dim, hidden_features=round(embed_dim * mlp_ratio), drop=drop, fused_if_available=fused_if_available)
        
        self.ln_wo_grad = norm_layer(embed_dim, elementwise_affine=False)
        self.shared_aln = shared_aln
        if self.shared_aln:
            self.ada_gss = nn.Parameter(torch.randn(1, 1, 6, embed_dim) / embed_dim**0.5)
        else:
            lin = nn.Linear(cond_dim, 6*embed_dim)
            self.ada_lin = nn.Sequential(nn.SiLU(inplace=False), lin)
        
        self.fused_add_norm_fn = None

        self.adapter = Dinov2_Adapter(adapter_size=config.adapter_size, condition_type=config.condition_type)
        #  冻结 Dinov2 主干权重
        for p in self.adapter.model.parameters():
            p.requires_grad = False
        
        # self.adapter = EVA_Adapter()
        if config.adapter_size == "small":
            self.adapter_mlp = MLP(384, config.dim, config.dim)
        elif config.adapter_size == 'base':
            self.adapter_mlp = MLP(768, config.dim, config.dim)

        self.condition_embeddings = nn.Embedding(config.vocab_size, config.dim)
        self.condition_mlp = ConditionEmbedder(config.block_size, config.dim, config.class_dropout_prob, config.block_size, config.vocab_size)
        # conditon 注入层
        self.condition_layers = torch.nn.ModuleList()
        for layer_id in range(3):
            self.condition_layers.append(MLP(config.dim,config.dim,config.dim))
        
        self.layer_internal = depth=16 // 2
        self.control_strength = 1  

        #CrossAttention注入方式
        self.cross_attn_inject = CrossAttentionInjection(embed_dim=config.dim, num_heads=num_heads)


    # NOTE: attn_bias is None during inference because kv cache is enabled
    def forward(self, x, cond_BD, condition, attn_bias, current_step: int, total_steps:int):   # C: embed_dim, D: cond_dim
        if self.shared_aln:
            gamma1, gamma2, scale1, scale2, shift1, shift2 = (self.ada_gss + cond_BD).unbind(2) # 116C + B16C =unbind(2)=> 6 B1C
        else:
            gamma1, gamma2, scale1, scale2, shift1, shift2 = self.ada_lin(cond_BD).view(-1, 1, 6, self.C).unbind(2)
    
        # --------- 注入策略一：条件注入机制（例如每隔 N 层注入） ---------
        # if condition is not None:
        #     condition_embeddings = self.adapter(condition)
        #     condition_embeddings = self.adapter_mlp(condition_embeddings)
        #     self.condition_token = self.condition_mlp(condition_embeddings,train=self.training)

        # # self.block_idx 是当前block编号
        # if self.block_idx % self.layer_internal == 0:
        #     cond_feat = self.condition_layers[self.block_idx // self.layer_internal](self.condition_token)  # [B, 1, C]
        #     # cond_feat: [B, Lc, C] → [B, Lx, C]
        #     cond_feat = cond_feat.mean(dim=1, keepdim=True).expand(-1, x.shape[1], -1)
        #     x = x + self.control_strength * cond_feat

        # # --------- 注入策略二：在某些层激活 cross-attention 注入（例如每隔 N 层注入） ---------
        # if condition is not None:
        #     condition_embeddings = self.adapter(condition)
        #     condition_embeddings = self.adapter_mlp(condition_embeddings)
        #     self.condition_token = self.condition_mlp(condition_embeddings,train=self.training)
            
        # if self.block_idx % self.layer_internal == 0:
        #     cond_feat = self.condition_layers[self.block_idx // self.layer_internal](self.condition_token)
        #     cond_feat = cond_feat.mean(dim=1, keepdim=True).expand(-1, x.shape[1], -1)
        #     x = x + self.control_strength * cond_feat

        #     # cross-attention: x attends to condition token
        #     # query: x, key/value: condition_token
        #     cross_attn_out = self.cross_attn_inject(query=x, key=self.condition_token, value=self.condition_token)
        #     x = x + self.control_strength * cross_attn_out


        # --------- 注入策略三：注入强度控制机制 ---------
        # 使用训练步调度：
        if condition is not None:
            condition_embeddings = self.adapter(condition)
            condition_embeddings = self.adapter_mlp(condition_embeddings)
            self.condition_token = self.condition_mlp(condition_embeddings,train=self.training)
            
            cond_feat = self.condition_layers[self.block_idx // self.layer_internal](self.condition_token)
            cond_feat = cond_feat.mean(dim=1, keepdim=True).expand(-1, x.shape[1], -1)
           
            # cross_attn_out = self.cross_attn_inject(x, key=self.condition_token, value=self.condition_token)
            cross_attn_out = self.cross_attn_inject(x, self.condition_token)

        if current_step is not None:
            progress = min(current_step / total_steps, 1.0)
            alpha = 0.5 * (1 + math.cos(math.pi * progress))
        else:
            alpha = 1.0
            
        x = x + alpha * cross_attn_out

        # --------- 注入策略四：区域掩码控制机制 ---------
        # 在生成的早期阶段，边缘信息可能会有更大的权重来帮助模型生成图像的轮廓
        # 在生成的后期，分割图的控制可能更重要，用于确保图像区域的语义一致性 
        # progress = current_step / total_steps

        # alpha_edge = 1.0 - progress     # 边缘控制随时间递减
        # alpha_seg  = progress           # 分割控制随时间增强

        # edge_feat = self.edge_adapter(edge_map) # 这两个部分需要处理
        # seg_feat  = self.seg_adapter(seg_map)   # 需要边缘提取图，以及处理模型：ViT?????

        # # 将两个特征注入模型
        # x = x + alpha_edge * self.cross_attn_edge(x, edge_feat)
        # x = x + alpha_seg  * self.cross_attn_seg(x, seg_feat)


        # --------- 注入策略五：阶段性控制机制 ---------
        
        # 根据训练轮次逐步增强/弱化注入作用：warm-up、余弦下降、分阶段注入策略

        # if self.training:
        #     if current_epoch < 10:
        #         alpha = 0.1
        #     elif current_epoch < 30:
        #         alpha = 0.5
        #     else:
        #         alpha = 1.0
        # x = x + alpha * cross_attn_out


        # --------- 注意力 + FFN  ---------
        x = x + self.drop_path(self.attn( self.ln_wo_grad(x).mul(scale1.add(1)).add_(shift1), attn_bias=attn_bias ).mul_(gamma1))
        x = x + self.drop_path(self.ffn( self.ln_wo_grad(x).mul(scale2.add(1)).add_(shift2) ).mul(gamma2)) # this mul(gamma2) cannot be in-placed when FusedMLP is used
        return x


    def extra_repr(self) -> str:
        return f'shared_aln={self.shared_aln}'


class AdaLNBeforeHead(nn.Module):
    def __init__(self, C, D, norm_layer):   # C: embed_dim, D: cond_dim
        super().__init__()
        self.C, self.D = C, D
        self.ln_wo_grad = norm_layer(C, elementwise_affine=False)
        self.ada_lin = nn.Sequential(nn.SiLU(inplace=False), nn.Linear(D, 2*C))
    
    def forward(self, x_BLC: torch.Tensor, cond_BD: torch.Tensor):
        scale, shift = self.ada_lin(cond_BD).view(-1, 1, 2, self.C).unbind(2)
        return self.ln_wo_grad(x_BLC).mul(scale.add(1)).add_(shift)