model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Optional, Tuple
import math
from components import RMSNorm
from transformer import OptimizedTransformerBlock
from multimodel_fusion import MultiModalFusionModule
from encoders import (
    ImprovedVisionTransformer,
    ImprovedAudioEncoder,
    ImprovedVideoEncoder
)

class MultiModalDenseTransformer(nn.Module):
    def __init__(
        self,
        model_dim: int = 2048,
        vocab_size: int = 30000,
        n_layers: int = 48,
        n_heads: int = 32,
        n_kv_heads: Optional[int] = None,
        head_dim: Optional[int] = None,
        max_seq_len: int = 8192,
        dropout: float = 0.0,
        attn_dropout: float = 0.0,
        
        # MoE配置
        use_moe: bool = False,
        num_experts: int = 8,
        moe_top_k: int = 2,
        moe_layers: Optional[List[int]] = None,
        
        # PEFT配置
        use_adapter: bool = False,
        adapter_dim: int = 64,
        use_lora: bool = False,
        lora_rank: int = 8,
        
        # 训练配置
        use_gradient_checkpointing: bool = False,
        use_parallel_residual: bool = False,
        
        # 位置编码
        rope_scaling_factor: float = 1.0,
        rope_scaling_type: str = "yarn",
        sliding_window: Optional[int] = None,
        
        # 规范化
        norm_eps: float = 1e-6,
        initializer_range: float = 0.02,
        ffn_dim_multiplier: Optional[float] = None,
        tie_word_embeddings: bool = True,
        
        # 多模态配置
        use_multimodal_fusion: bool = True,
        fusion_layers: int = 4,
        use_contrastive: bool = True,
        vision_depth: int = 24,
        audio_depth: int = 12,
        video_spatial_depth: int = 12,
        video_temporal_depth: int = 4
    ):
        super().__init__()
        
        self.model_dim = model_dim
        self.vocab_size = vocab_size
        self.n_layers = n_layers
        self.max_seq_len = max_seq_len
        self.use_gradient_checkpointing = use_gradient_checkpointing
        self.tie_word_embeddings = tie_word_embeddings
        self.use_multimodal_fusion = use_multimodal_fusion
        
        # Token embedding
        self.token_embedding = nn.Embedding(vocab_size, model_dim)
        self.modality_embedding = nn.Embedding(4, model_dim)
        self.embed_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
        
        self.vision_encoder = ImprovedVisionTransformer(
            embed_dim=model_dim,
            depth=vision_depth,
            n_heads=n_heads,
            dropout=dropout,
            use_adapter=use_adapter,
            adapter_dim=adapter_dim
        )
        
        self.audio_encoder = ImprovedAudioEncoder(
            embed_dim=model_dim,
            depth=audio_depth,
            n_heads=n_heads,
            dropout=dropout,
            use_adapter=use_adapter,
            adapter_dim=adapter_dim
        )
        
        self.video_encoder = ImprovedVideoEncoder(
            embed_dim=model_dim,
            spatial_depth=video_spatial_depth,
            temporal_depth=video_temporal_depth,
            n_heads=n_heads,
            dropout=dropout,
            use_adapter=use_adapter,
            adapter_dim=adapter_dim
        )
        
        # 多模态融合模块
        if use_multimodal_fusion:
            self.fusion_module = MultiModalFusionModule(
                dim=model_dim,
                num_fusion_layers=fusion_layers,
                n_heads=n_heads,
                dropout=dropout,
                use_contrastive=use_contrastive
            )
        
        if moe_layers is None and use_moe:
            moe_layers = list(range(n_layers // 2, n_layers))
        elif moe_layers is None:
            moe_layers = []
        
        self.layers = nn.ModuleList([
            OptimizedTransformerBlock(
                dim=model_dim,
                n_heads=n_heads,
                n_kv_heads=n_kv_heads,
                head_dim=head_dim,
                dropout=dropout,
                attn_dropout=attn_dropout,
                use_moe=(use_moe and i in moe_layers),
                num_experts=num_experts,
                moe_top_k=moe_top_k,
                use_adapter=use_adapter,
                adapter_dim=adapter_dim,
                use_lora=use_lora,
                lora_rank=lora_rank,
                use_parallel_residual=use_parallel_residual,
                norm_eps=norm_eps,
                sliding_window=sliding_window,
                ffn_dim_multiplier=ffn_dim_multiplier,
                layer_idx=i
            )
            for i in range(n_layers)
        ])
        
        self.norm = RMSNorm(model_dim, eps=norm_eps)
        self.lm_head = nn.Linear(model_dim, vocab_size, bias=False)
        
        if tie_word_embeddings:
            self.lm_head.weight = self.token_embedding.weight
        
        self.initializer_range = initializer_range
        self.apply(self._init_weights)
        
        if not tie_word_embeddings:
            self._init_lm_head()
        
        self.n_params = sum(p.numel() for p in self.parameters())
        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
        
        print(f"\n{'='*80}")
        print(f"Improved Model Configuration:")
        print(f"  Model Dimension: {model_dim}")
        print(f"  Vocab Size: {vocab_size}")
        print(f"  Layers: {n_layers}")
        print(f"  Attention Heads: {n_heads}")
        print(f"  KV Heads: {n_kv_heads if n_kv_heads else n_heads}")
        print(f"  Max Sequence Length: {max_seq_len}")
        print(f"  Multimodal Fusion: {use_multimodal_fusion}")
        print(f"  Contrastive Learning: {use_contrastive}")
        print(f"  MoE: {use_moe} (Experts: {num_experts}, Top-K: {moe_top_k})")
        print(f"  Total Parameters: {self.n_params / 1e9:.2f}B")
        print(f"  Trainable Parameters: {trainable_params / 1e9:.2f}B")
        print(f"{'='*80}\n")

    def _init_weights(self, module):
        """权重初始化"""
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=self.initializer_range)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=self.initializer_range)
            if hasattr(module, 'padding_idx') and module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _init_lm_head(self):
        """初始化LM head"""
        std = self.initializer_range / math.sqrt(2 * self.n_layers)
        torch.nn.init.normal_(self.lm_head.weight, mean=0.0, std=std)

    def _encode_modality(self, segment: Dict) -> torch.Tensor:
        """编码单个模态"""
        seg_type = segment['type']
        seg_data = segment['data']
        
        if seg_type == 'image':
            return self.vision_encoder(seg_data)
        elif seg_type == 'audio':
            return self.audio_encoder(seg_data)
        elif seg_type == 'video':
            return self.video_encoder(seg_data)
        elif seg_type == 'text':
            return self.token_embedding(seg_data)
        else:
            return seg_data

    def forward(
        self,
        input_data: Dict,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        return_hidden: bool = False,
        use_cache: bool = False,
        past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        compute_contrastive: bool = False
    ) -> Dict:
        """前向传播"""
        device = self.token_embedding.weight.device
        
        # 编码每个模态
        encoded_segments = []
        for segment in input_data.get('segments', []):
            encoded = self._encode_modality(segment)
            
            # 添加模态嵌入
            modality_id = segment.get('modality_id', 0)
            modality_embeds = self.modality_embedding(
                torch.tensor([modality_id], device=device)
            ).expand(encoded.shape[0], encoded.shape[1], -1)
            
            encoded_segments.append({
                'type': segment['type'],
                'data': encoded + modality_embeds,
                'modality_id': modality_id
            })
        
        # 多模态融合
        contrastive_losses = {}
        if self.use_multimodal_fusion and len(encoded_segments) > 1:
            fusion_output = self.fusion_module(
                encoded_segments,
                compute_contrastive=compute_contrastive
            )
            x = fusion_output['fused_features']
            contrastive_losses = fusion_output.get('contrastive_losses', {})
        else:
            # 简单拼接
            all_embeddings = [seg['data'] for seg in encoded_segments]
            x = torch.cat(all_embeddings, dim=1) if all_embeddings else torch.zeros(
                1, 1, self.model_dim, device=device
            )
        
        x = self.embed_dropout(x)
        if position_ids is None:
            if past_key_values is not None:
                # 缓存的长度 (KV cache 的 shape 是 [B, H, SeqLen, D])
                past_length = past_key_values[0][0].size(2)
                # 当前输入的长度
                seq_length = x.shape[1]
                # 生成正确的位置索引: [past_length, past_length + 1, ...]
                position_ids = torch.arange(
                    past_length, past_length + seq_length, dtype=torch.long, device=device
                ).unsqueeze(0).expand(x.shape[0], -1)
            else:
                # 如果没有缓存，从 0 开始
                seq_length = x.shape[1]
                position_ids = torch.arange(
                    0, seq_length, dtype=torch.long, device=device
                ).unsqueeze(0).expand(x.shape[0], -1)
        # Transformer层
        present_key_values = [] if use_cache else None
        all_hidden_states = [] if output_hidden_states else None
        all_attentions = [] if output_attentions else None
        moe_aux_loss = torch.tensor(0.0, device=device)
        
        for idx, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states.append(x)
            
            past_kv = past_key_values[idx] if past_key_values is not None else None
            
            if self.use_gradient_checkpointing and self.training:
                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(
                            inputs[0],
                            attention_mask=inputs[1],
                            position_ids=inputs[2],
                            use_cache=False,
                            past_kv=None,
                            output_attentions=False
                        )
                    return custom_forward
                
                import torch.utils.checkpoint as checkpoint
                layer_outputs = checkpoint.checkpoint(
                    create_custom_forward(layer),
                    x,
                    attention_mask,
                    position_ids,
                    use_reentrant=False
                )
                x = layer_outputs[0]
                present_kv = None
                attn_weights = None
            else:
                layer_outputs = layer(
                    x,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    use_cache=use_cache,
                    past_kv=past_kv,
                    output_attentions=output_attentions
                )
                x, present_kv, attn_weights = layer_outputs
            
            if use_cache:
                present_key_values.append(present_kv)
            
            if output_attentions:
                all_attentions.append(attn_weights)
            
            if hasattr(layer, 'moe_aux_loss'):
                moe_aux_loss += layer.moe_aux_loss
        
        hidden_states = self.norm(x)
        logits = self.lm_head(hidden_states)
        
        if output_hidden_states:
            all_hidden_states.append(hidden_states)
        
        # 组装输出
        outputs = {
            'logits': logits,
            'moe_aux_loss': moe_aux_loss,
            'contrastive_losses': contrastive_losses
        }
        
        if use_cache:
            outputs['past_key_values'] = present_key_values
        
        if output_hidden_states:
            outputs['hidden_states'] = all_hidden_states
        
        if output_attentions:
            outputs['attentions'] = all_attentions
        
        if return_hidden:
            outputs['last_hidden_state'] = hidden_states
        
        return outputs

    @torch.no_grad()
    def generate(
        self,
        input_data: Dict,
        max_new_tokens: int = 100,
        temperature: float = 1.0,
        top_k: int = 50,
        top_p: float = 0.9,
        eos_token_id: int = 2,
        pad_token_id: Optional[int] = None,
        use_cache: bool = True,
        repetition_penalty: float = 1.0,
        length_penalty: float = 1.0,
        min_length: int = 0,
        do_sample: bool = True,
        num_beams: int = 1
    ) -> torch.Tensor:
        """改进的生成方法"""
        self.eval()
        device = next(self.parameters()).device
        
        if pad_token_id is None:
            pad_token_id = eos_token_id
        
        initial_text_tokens = input_data['segments'][0]['data'].to(device)
        batch_size = initial_text_tokens.shape[0]
        
        if 'attention_mask' in input_data:
            attention_mask = input_data['attention_mask'].to(device)
        else:
            attention_mask = torch.ones_like(initial_text_tokens)
        initial_seq_len = initial_text_tokens.shape[1]
        position_ids = torch.zeros((batch_size,initial_seq_len),dtype=torch.long,device=device)
        
        for i in range(batch_size):
            non_pad_mask = attention_mask[i].bool()
            if non_pad_mask.any():
                positions = torch.cumsum(non_pad_mask.long(),dim=0) -1
                position_ids[i]=positions * non_pad_mask.long()
            


        generated_tokens = []
        past_key_values = None
        current_tokens = initial_text_tokens
        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=device)
        
        for step in range(max_new_tokens):
            current_input_data = {
                'segments': [{'type': 'text', 'data': current_tokens, 'modality_id': 0}]
            }
            
            if step > 0 and use_cache:
                # 添加当前 token 的 mask (1)
                new_mask = torch.ones(batch_size,1,dtype=torch.long,device=device)
                attention_mask = torch.cat([attention_mask, new_mask], dim=1)
                current_positions = (attention_mask.sum(dim=1 , keepdim=True) -1).clamp(min=0)
                current_positions_ids=current_positions
            else:
                current_positions_ids=position_ids
            outputs = self.forward(
                current_input_data,
                attention_mask=attention_mask, # <--- 传入 Mask
                position_ids=current_positions_ids,
                use_cache=use_cache,
                past_key_values=past_key_values
            )
            
            logits = outputs['logits']
            if use_cache:
                past_key_values = outputs['past_key_values']
            
            next_token_logits = logits[:, -1, :] / max(temperature, 1e-5)
            
            # Repetition penalty
            if repetition_penalty != 1.0 and len(generated_tokens) > 0:
                prev_generated = torch.cat(generated_tokens, dim=1)
                score = torch.gather(next_token_logits, 1, prev_generated)
                score = torch.where(
                    score < 0,
                    score * repetition_penalty,
                    score / repetition_penalty
                )
                next_token_logits.scatter_(1, prev_generated, score)
            
            # Min length constraint
            if step < min_length:
                next_token_logits[:, eos_token_id] = float('-inf')
            
            # Sampling
            if do_sample:
                if top_k > 0:
                    top_k_vals, _ = torch.topk(next_token_logits, top_k)
                    min_val_to_keep = top_k_vals[:, -1].unsqueeze(-1)
                    next_token_logits[next_token_logits < min_val_to_keep] = float('-inf')
                
                if top_p < 1.0:
                    sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
                    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
                    sorted_indices_to_remove = cumulative_probs > top_p
                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                    sorted_indices_to_remove[..., 0] = 0
                    indices_to_remove = torch.zeros_like(next_token_logits, dtype=torch.bool)
                    indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
                    next_token_logits[indices_to_remove] = float('-inf')
                
                probs = F.softmax(next_token_logits, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1)
            else:
                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
            
            # Apply unfinished mask
            next_token = next_token * unfinished_sequences[:, None] + pad_token_id * (1 - unfinished_sequences[:, None])
            
            generated_tokens.append(next_token)
            
            if not use_cache:
                initial_text_tokens = torch.cat([initial_text_tokens, next_token], dim=1)
                current_tokens = initial_text_tokens
            else:
                current_tokens = next_token
            
            # Update unfinished sequences
            unfinished_sequences = unfinished_sequences.mul(
                (next_token.squeeze(-1) != eos_token_id).long()
            )
            
            if unfinished_sequences.max() == 0:
                break
        
        if not generated_tokens:
            return torch.empty(batch_size, 0, dtype=torch.long, device=device)
        
        return torch.cat(generated_tokens, dim=1)