# 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 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): # determine the shape of hidden_states batch_size, seq_length, hidden_dim = hidden_states.shape flat_hidden = hidden_states.view(-1, hidden_dim) # (B*seq_len, hidden_dim) total_tokens = flat_hidden.size(0) # 计算路由 logits 和全专家 routing 权重 router_logits = self.gate(flat_hidden) # (B*seq_len, num_experts) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) # (B*seq_len, num_experts) # Top-k 选择 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) routing_weights_topk = routing_weights_topk.to(flat_hidden.dtype) # ---------- 稀疏计算部分 ---------- # 初始化稀疏输出，shape: (B*seq_len, hidden_dim) sparse_output = torch.zeros((total_tokens, hidden_dim), dtype=flat_hidden.dtype, device=flat_hidden.device) # 创建一个张量存储激活专家的输出，避免使用Python字典 # shape: (B*seq_len, num_experts, hidden_dim) all_expert_outputs = torch.zeros((total_tokens, self.num_experts, hidden_dim), dtype=flat_hidden.dtype, device=flat_hidden.device) # 使用张量掩码跟踪哪些专家被激活 # shape: (B*seq_len, num_experts) expert_activated = torch.zeros((total_tokens, self.num_experts), dtype=torch.bool, device=flat_hidden.device) # one-hot 编码 top-k 专家，shape: (B*seq_len, top_k, num_experts) expert_mask = F.one_hot(selected_experts, num_classes=self.num_experts) # (B*seq_len, top_k, num_experts) expert_mask = expert_mask.permute(2, 1, 0) # (num_experts, top_k, B*seq_len) for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) if top_x.numel() > 0: current_state = flat_hidden[top_x] # (n, hidden_dim) current_output = expert_layer(current_state) # (n, hidden_dim) weight = routing_weights_topk[top_x, idx].unsqueeze(-1) # (n, 1) weighted_output = current_output * weight sparse_output.index_add_(0, top_x, weighted_output.to(flat_hidden.dtype)) # 直接为激活的token分配专家输出 for i in range(top_x.shape[0]): token_idx = top_x[i] all_expert_outputs[token_idx, expert_idx] = current_output[i] expert_activated[token_idx, expert_idx] = True # ---------- 稀疏计算结束 ---------- # ---------- Dense估计部分 ---------- # 从GPU获取必要信息，避免过多的tensor->list转换 selected_experts_gpu = selected_experts # 保持在GPU上 # 预分配结果张量，避免在循环中append dense_outputs = torch.zeros_like(sparse_output) # 使用向量化的estimate_dense_output函数 dense_outputs = self.estimate_dense_output_batch( total_tokens=total_tokens, selected_experts=selected_experts_gpu, routing_weights=routing_weights, expert_activated=expert_activated, all_expert_outputs=all_expert_outputs ) # ---------- Dense估计结束 ---------- # 使用直通梯度：前向输出用稀疏结果，但反向传播时梯度来源于 dense 估计 final_flat = sparse_output.detach() + (dense_outputs - dense_outputs.detach()) final_output = final_flat.view(batch_size, seq_length, hidden_dim) return final_output, router_logits def estimate_dense_output_batch(self, total_tokens, selected_experts, routing_weights, expert_activated, all_expert_outputs): """ 批量估计所有token的dense输出，优化版本。 参数： total_tokens: token总数 selected_experts: 每个token激活的专家索引，形状 (total_tokens, top_k) routing_weights: 路由权重，形状 (total_tokens, num_experts) expert_activated: 掩码张量，标记每个token激活了哪些专家，形状 (total_tokens, num_experts) all_expert_outputs: 专家输出，形状 (total_tokens, num_experts, hidden_dim) 返回： dense_outputs: 形状 (total_tokens, hidden_dim) """ hidden_dim = all_expert_outputs.size(-1) num_experts = routing_weights.size(1) device = all_expert_outputs.device # 预分配结果张量，注意是hidden_dim而不是num_experts dense_outputs = torch.zeros((total_tokens, hidden_dim), dtype=all_expert_outputs.dtype, device=device) # 对每个token单独处理（此处仍需循环，但后续可进一步优化） for token_idx in range(total_tokens): # 对于激活的专家，直接使用输出 activated_mask = expert_activated[token_idx] # (num_experts,) # 对于未激活的专家，找到估计值 for expert_idx in range(num_experts): if activated_mask[expert_idx]: # 直接使用激活专家的输出 expert_output = all_expert_outputs[token_idx, expert_idx] else: # 寻找可以用于估计的输出 # 找出其他激活了当前专家的token tokens_with_expert = expert_activated[:, expert_idx] # 找出同时激活了当前token的某些专家和当前专家的其他token # 首先获取当前token激活的专家 current_activated = selected_experts[token_idx] # 在其他token中寻找同时激活了current_activated中专家和expert_idx的token valid_tokens = torch.zeros(total_tokens, dtype=torch.bool, device=device) # 对于每个其他token，检查它是否同时激活了当前token的某个专家和当前专家 for other_token in range(total_tokens): if other_token == token_idx: continue # 检查其他token是否激活了当前专家 if expert_activated[other_token, expert_idx]: # 检查是否有共同激活的专家 other_experts = selected_experts[other_token] common = torch.any(torch.isin(other_experts, current_activated)) if common: valid_tokens[other_token] = True # 如果找到了有效token if valid_tokens.any(): # 获取有效token对当前专家的输出 valid_outputs = all_expert_outputs[valid_tokens, expert_idx] # 只计算非零值的平均值 mask = (valid_outputs.sum(dim=-1) != 0).to(valid_outputs.dtype).unsqueeze(-1) if mask.sum() > 0: expert_output = (valid_outputs * mask).sum(dim=0) / mask.sum() else: expert_output = torch.zeros(hidden_dim, dtype=all_expert_outputs.dtype, device=device) else: # 如果没有找到有效token，使用所有激活了当前专家的token的输出 if tokens_with_expert.any(): all_valid_outputs = all_expert_outputs[tokens_with_expert, expert_idx] mask = (all_valid_outputs.sum(dim=-1) != 0).to(all_valid_outputs.dtype).unsqueeze(-1) if mask.sum() > 0: expert_output = (all_valid_outputs * mask).sum(dim=0) / mask.sum() else: expert_output = torch.zeros(hidden_dim, dtype=all_expert_outputs.dtype, device=device) else: expert_output = torch.zeros(hidden_dim, dtype=all_expert_outputs.dtype, device=device) # 根据routing权重加权 dense_outputs[token_idx] += routing_weights[token_idx, expert_idx] * expert_output return dense_outputs def estimate_dense_output(self, token_idx, activated, gate_prob, activated_outputs, all_routing, all_expert_outputs): """ 对于当前 token，根据 mini-batch 中的信息估计 dense 输出。 参数： token_idx: 当前 token 的索引（标量） activated: 当前 token 激活的专家列表，例如 [1, 3] gate_prob: 当前 token 的 routing 权重，形状 (num_experts,) activated_outputs: dict，当前 token 对激活专家的实际输出，形状 (hidden_dim,) all_routing: list，每个 token 的激活专家列表（长度为 N，每个元素为 list） all_expert_outputs: Tensor, (N, num_experts, hidden_dim) 返回： estimated_dense: Tensor, (hidden_dim,) """ num_experts = gate_prob.size(0) dense_parts = {} # 对于激活的专家，直接使用其实际输出 for idx in activated: dense_parts[idx] = activated_outputs[idx] # 对于未激活的专家，使用 mini-batch 中其他 token 的输出估计 non_activated = [i for i in range(num_experts) if i not in activated] for i in non_activated: indices = [] for idx, r_dec in enumerate(all_routing): if (i in r_dec) and (len(set(r_dec) & set(activated)) > 0): indices.append(idx) if indices: selected_outputs = all_expert_outputs[indices, i, :] # (n, hidden_dim) # 只计算非零值的平均值 mask = (selected_outputs.sum(dim=-1) != 0).to(selected_outputs.dtype).unsqueeze(-1) if mask.sum() > 0: estimated = (selected_outputs * mask).sum(dim=0) / mask.sum() else: # 如果全是零，返回零向量 estimated = torch.zeros_like(selected_outputs[0]) else: all_outputs = all_expert_outputs[:, i, :] mask = (all_outputs.sum(dim=-1) != 0).to(all_outputs.dtype).unsqueeze(-1) if mask.sum() > 0: estimated = (all_outputs * mask).sum(dim=0) / mask.sum() else: # 如果全是零，返回零向量 estimated = torch.zeros_like(all_outputs[0]) dense_parts[i] = estimated # 按 gate_prob 加权求和各专家输出 estimated_dense = 0 for i in range(num_experts): estimated_dense += gate_prob[i] * dense_parts[i] return estimated_dense 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)) 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()