modeling_densebackward_olmoe0125_v1.py

# my_custom_olmoe/modeling_custom.py

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

# 导入官方实现（注意根据你的 transformers 版本调整导入路径）
from transformers.models.olmoe.modeling_olmoe import OlmoeForCausalLM, OlmoeSparseMoeBlock, OlmoeMLP
from .configuration_densebackward_olmoe0125_v1 import DenseBackwardOLMoEConfig


class DenseBackwardOlmoeSparseMoeBlock(OlmoeSparseMoeBlock):
    
    """
    继承自官方 OlmoeSparseMoeBlock，实现 dense backward 功能：
    前向输出依旧保持与官方相同（即稀疏计算结果），
    但在反向传播时，通过直通梯度让 dense 计算的梯度传递回来，
    dense 输出通过对每个专家在所有 token 上进行计算，并利用全 routing 权重加权获得。
    
    输入：
        hidden_states: Tensor, shape (batch_size, sequence_length, hidden_dim)
    输出：
        final_output: Tensor, shape (batch_size, sequence_length, hidden_dim)
        router_logits: Tensor, shape (batch_size * sequence_length, num_experts)
    """
    def forward(self, hidden_states: torch.Tensor):
        batch_size, seq_length, hidden_dim = hidden_states.shape
        # 记录输入张量的数据类型，确保所有计算保持一致
        dtype = hidden_states.dtype
        device = hidden_states.device
        
        flat_hidden = hidden_states.view(-1, hidden_dim)  # (B*seq_len, hidden_dim)
        N_tokens = flat_hidden.size(0)

        # 计算路由逻辑
        router_logits = self.gate(flat_hidden)  # (B*seq_len, num_experts)
        # 确保router_logits和flat_hidden数据类型一致
        router_logits = router_logits.to(dtype=dtype)
        routing_weights = F.softmax(router_logits, dim=1, dtype=dtype)  # (B*seq_len, num_experts)

        # 选择top-k专家
        routing_weights_topk, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
        if self.norm_topk_prob:
            routing_weights_topk = routing_weights_topk / routing_weights_topk.sum(dim=-1, keepdim=True)
            # 确保归一化后类型一致
            routing_weights_topk = routing_weights_topk.to(dtype=dtype)
        
        # ---------- 真实计算所有专家输出（密集计算）----------
        all_expert_outputs = torch.zeros((N_tokens, self.num_experts, hidden_dim),
                                         dtype=dtype, device=device)
        
        for expert_idx in range(self.num_experts):
            expert_layer = self.experts[expert_idx]
            # 对所有token都计算当前专家的输出
            expert_output = expert_layer(flat_hidden)  # (N_tokens, hidden_dim)
            # 确保专家输出与预期类型一致
            expert_output = expert_output.to(dtype=dtype)
            all_expert_outputs[:, expert_idx, :] = expert_output
        
        # ---------- 提取激活专家输出（稀疏前向）- 使用张量批处理 ----------
        # 创建索引张量，第一维是token索引，第二维是专家索引
        token_indices = torch.arange(N_tokens, device=device).unsqueeze(1).expand(-1, self.top_k)
        batch_indices = token_indices.reshape(-1)
        expert_indices = selected_experts.reshape(-1)
        
        # 批量提取激活专家的输出
        selected_outputs = all_expert_outputs[batch_indices, expert_indices].view(N_tokens, self.top_k, hidden_dim)
        
        # 扩展权重以便批量相乘
        expanded_weights = routing_weights_topk.unsqueeze(-1)  # (N_tokens, top_k, 1)
        expanded_weights = expanded_weights.to(dtype=dtype)
        
        # 权重乘以专家输出并求和
        sparse_output = (selected_outputs * expanded_weights).sum(dim=1)  # (N_tokens, hidden_dim)
        
        # ---------- 密集计算聚合（用于反向传播）----------
        # 使用所有专家的输出和路由权重计算密集输出
        routing_weights_expanded = routing_weights.unsqueeze(-1)  # (N_tokens, num_experts, 1)
        routing_weights_expanded = routing_weights_expanded.to(dtype=dtype)
        print(expanded_weights.shape)
        print("sparse",expanded_weights)
        print(routing_weights_expanded.shape)
        print("dense",routing_weights_expanded)
        dense_outputs = (all_expert_outputs * routing_weights_expanded).sum(dim=1)  # (N_tokens, hidden_dim)
        
        # ---------- 组合稀疏前向和密集反向 ----------
        # sparse_output.detach()保留稀疏前向计算图
        # (dense_outputs - dense_outputs.detach())只保留密集反向梯度
        final_flat = sparse_output.detach() + (dense_outputs - dense_outputs.detach())
        final_flat = final_flat.to(dtype=dtype)  # 确保最终输出类型一致
        final_output = final_flat.view(batch_size, seq_length, hidden_dim)
        
        return final_output, router_logits

class DenseBackwardOLMoEForCausalLM(OlmoeForCausalLM):
    """
    自定义的 Olmoe ForCausalLM 模型，使用新的 DenseBackwardOlmoeSparseMoeBlock 替换原版的 MoE 模块，
    以实现 dense backward 功能。
    
    配置类：DenseBackwardOLMoEConfig
    """
    config_class = DenseBackwardOLMoEConfig
    base_model_prefix = "olmoe"

    def __init__(self, config):
        # 首先调用父类初始化方法
        super().__init__(config)
        
        # 不要尝试重新赋值self，而是从预训练模型加载并更新当前模型
        pretrained_model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0125")
        
        # 复制预训练模型的状态到当前模型
        self.config = pretrained_model.config
        self.model = pretrained_model.model
        self.vocab_size = pretrained_model.vocab_size
        self.router_aux_loss_coef = pretrained_model.router_aux_loss_coef
        self.num_experts = pretrained_model.num_experts
        self.lm_head = pretrained_model.lm_head
        
        # 遍历模型中所有 decoder 层，替换每个 OlmoeSparseMoeBlock 为 DenseBackward 版本
        # 此处假设官方模型在 self.model.layers 中组织 decoder 层，
        # 且每层中 mlp 模块包含属性 sparse_moe_block。
        for layer in self.model.layers:
            if hasattr(layer.mlp, "gate"):
                print("111")
                orig_block = layer.mlp
                # 通过直接复制原版属性创建新的块
                new_block = DenseBackwardOlmoeSparseMoeBlock(config)  # 或其他适当参数
                # 然后手动复制需要共享的属性：
                new_block.gate = orig_block.gate
                new_block.experts = orig_block.experts
                new_block.num_experts = orig_block.num_experts
                new_block.top_k = orig_block.top_k
                new_block.norm_topk_prob = orig_block.norm_topk_prob
                layer.mlp = new_block
                print(type(layer.mlp))
        # 释放预训练模型内存
        del pretrained_model
        import gc
        gc.collect()
        torch.cuda.empty_cache()
        print("原始预训练模型已释放")

def main():
    config = DenseBackwardOLMoEConfig(        # 官方模型参数
    model_marker="DenseBackward_olmoe_marker",
)
# 创建自定义模型实例
    model = DenseBackwardOLMoEForCausalLM(config)
    print(type(model))
    print(type(model.model))
    print(type(model.model.layers[0]))
    print(type(model.model.layers[0].mlp))
    print(type(model.model.layers[0].mlp.experts))
if __name__ == "__main__":
    main()