模型解读

关于数据的模式

Looking at the TrajectoryLoader class, yes - that's exactly how it handles different sequence lengths. Let me break down the sequence length handling:

  1. Each batch finds the longest sequence length in that batch:
max_length = max(src_length)
  1. Then it pads shorter sequences with a special padding token (self.pad):
# For source sequences
for item in src_batch:
    item += [self.pad] * (max_length - len(item))

# For target sequences (note +2 for BOS/EOS tokens)
for item in trg_batch:
    item += [self.pad] * (max_length + 2 - len(item))
  1. The process works like this:
  • Each trajectory gets BOS (beginning of sequence) token at start
  • Original trajectory nodes in the middle
  • EOS (end of sequence) token at end
  • PAD tokens fill the remaining positions up to max_length

For example, if we have trajectories of lengths 3 and 5 in a batch:

Original sequences:
seq1: [A, B, C]
seq2: [A, B, C, D, E]

After processing:
seq1: [BOS, A, B, C, EOS, PAD, PAD]  # Padded to match seq2
seq2: [BOS, A, B, C, D, E, EOS]      # No padding needed

The key details:

  • BOS token = label_num-3
  • EOS token = label_num-2
  • PAD token = label_num-1
  • Original sequence length is tracked in src_length_batchs/trg_length_batchs
  • This padding allows batch processing while keeping original length info

This padding strategy is common in sequence processing, as it allows:

  1. Batch processing of variable length sequences
  2. Attention masks can use the padding information to ignore padded positions
  3. Loss functions can use padding_idx to ignore loss on padded positions

The model then uses these lengths for proper loss calculation and masking of padded positions.

Yes, BOS, EOS, and PAD tokens also have their own embeddings. Here's why:

In the model, the node_embedding layer is created with size num_nodes:

self.node_embedding = nn.Embedding(num_nodes, d_model)

And num_nodes is set to label_num which includes all the special tokens:

label_num = len(self.road_network.nodes) + 3  # +3 for BOS, EOS, PAD

The token IDs are assigned as:

self.bos = self.label_num-3  # Beginning of sequence token
self.eos = self.label_num-2  # End of sequence token 
self.pad = self.label_num-1  # Padding token

So when any sequence goes through the node_embedding layer:

  1. Regular road nodes (0 to label_num-4) get their learned embeddings
  2. BOS gets its own embedding at index label_num-3
  3. EOS gets its own embedding at index label_num-2
  4. PAD gets its own embedding at index label_num-1

These special token embeddings are learned during training just like the regular node embeddings. However, a few special considerations:

  1. The PAD token embedding often gets ignored in loss computation:
self.loss_fn = nn.CrossEntropyLoss(ignore_index=label_num-1, reduction='none')
  1. The padding mask is used to ignore PAD positions in attention:
src_padding_mask = (src == self.label_num-1)
tgt_padding_mask = (trg == self.label_num-1)

This allows the model to:

  • Learn meaningful start and end sequence representations
  • Handle variable length sequences effectively
  • Ignore padding tokens during attention and loss computation

Looking at the code, the NLL loss calculation considers BOS, EOS, and PAD differently. Let's break it down:

  1. The NLL loss is calculated using:
nll_loss = self.nll(log_probs_flat, src_flat)

Where self.nll is defined as:

self.nll = nn.NLLLoss(ignore_index=self.num_nodes-1, reduction='none')

This means:

  • PAD tokens (num_nodes-1) are explicitly ignored in the loss calculation due to ignore_index
  • However, BOS (num_nodes-3) and EOS (num_nodes-2) tokens are included in the loss calculation
  1. Then when calculating mean loss per sequence:
mask = (src != self.num_nodes-1).float()  # Create mask for padding
nll_loss = (nll_loss * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)

This mask only excludes PAD tokens but still includes BOS and EOS.

To modify this to handle BOS and EOS differently, you could:

def compute_confidence(self, h, src):
    # ... existing code until masking ...
    
    # Create mask excluding BOS, EOS, and PAD
    mask = ((src != self.num_nodes-1) &  # not PAD
            (src != self.num_nodes-2) &  # not EOS
            (src != self.num_nodes-3)    # not BOS
           ).float()
    
    # Or alternatively, only include actual road nodes
    mask = (src < self.num_nodes-3).float()
    
    # Apply mask to losses
    nll_loss = (nll_loss * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)
    kl_div = kl_div.mean(dim=-1)  
    kl_div = (kl_div * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)
    
    total_loss = nll_loss + kl_div
    return total_loss

This way, the confidence score would only be based on actual road nodes in the trajectory, not the special tokens. Would you like me to explain more about how this would affect the model's behavior?

上采样

To add upsampling in the encode function, there are a few approaches we could take. Here are two common ways:

  1. Using interpolation to upsample the sequence length:
def encode(self, src, src_mask=None, upsample_factor=2):
    # Get original embeddings
    src = self.node_embedding(src) * math.sqrt(self.d_model)
    src = self.pos_encoder(src)
    
    # Upsample sequence length
    B, L, D = src.shape
    src = src.transpose(1, 2)  # [B, D, L]
    src = F.interpolate(src, size=L * upsample_factor, mode='linear')
    src = src.transpose(1, 2)  # [B, L*2, D]
    
    # Adjust position encoding for new length
    src = self.pos_encoder(src)
    
    memory = self.transformer.encoder(src, src_mask)
    return memory
  1. Using a learnable upsampling layer:
class TransformerTrajectoryModel(nn.Module):
    def __init__(self, num_nodes, d_model=128, ...):
        super().__init__()
        # ... other initialization code ...
        
        # Add upsampling layer
        self.upsample = nn.Sequential(
            nn.Linear(d_model, d_model * 2),
            nn.ReLU(),
            nn.Linear(d_model * 2, d_model)
        )
        
    def encode(self, src, src_mask=None, upsample_factor=2):
        # Get original embeddings
        src = self.node_embedding(src) * math.sqrt(self.d_model)
        src = self.pos_encoder(src)
        
        # Create upsampled sequence
        B, L, D = src.shape
        upsampled = []
        for i in range(L-1):
            current = src[:, i:i+1, :]
            next = src[:, i+1:i+2, :]
            
            # Get interpolated embedding
            interpolated = self.upsample(current + next)
            upsampled.extend([current, interpolated])
        upsampled.append(src[:, -1:, :])  # Add last token
        
        # Combine upsampled sequence
        src = torch.cat(upsampled, dim=1)
        
        # Final position encoding
        src = self.pos_encoder(src)
        
        memory = self.transformer.encoder(src, src_mask)
        return memory
  1. Or a simpler repeat-based approach:
def encode(self, src, src_mask=None, upsample_factor=2):
    # Get original embeddings  
    src = self.node_embedding(src) * math.sqrt(self.d_model)
    
    # Repeat each embedding
    B, L, D = src.shape
    src = src.unsqueeze(2)  # [B, L, 1, D]
    src = src.repeat(1, 1, upsample_factor, 1)  # [B, L, 2, D]
    src = src.view(B, L * upsample_factor, D)  # [B, L*2, D]
    
    # Position encoding for upsampled sequence
    src = self.pos_encoder(src)
    
    memory = self.transformer.encoder(src, src_mask)
    return memory

The key differences between these approaches:

  1. Interpolation-based:
  • Uses linear interpolation
  • Smooth transitions between tokens
  • No additional parameters
  1. Learnable upsampling:
  • More flexible/complex upsampling
  • Can learn better intermediate representations
  • Additional trainable parameters
  1. Repeat-based:
  • Simplest approach
  • Duplicates embeddings
  • No additional parameters

You'll need to:

  1. Adjust src_mask to match new sequence length
  2. Potentially modify decoder to handle longer sequences
  3. Consider impact on computational resources

I'd recommend starting with approach #3 for simplicity, then moving to #1 or #2 if you need more sophisticated upsampling.

Would you like me to explain any of these approaches in more detail?

关于node_embedding的冻结

这是一个好问题！确实，node_embedding 已经包含在了 component_names 中，而这段代码仍然单独处理了 node_embedding，其目的是 对 node_embedding 进行特殊处理。原因在于 node_embedding 的特性和它在整个系统中的角色与其他组件（例如 transformer 或 decoder）有所不同。

让我们详细分析为什么需要这样的额外处理：

1. node_embedding 的特殊性

  • 本地性：

    • node_embedding 是一个不可聚合的组件（在联邦学习中本地化），它的参数不会在客户端之间共享。
    • 与其他组件不同，node_embedding 的更新只影响本地客户端的模型。

  • 与数据直接相关：

    • 它的参数直接嵌入图节点的特征，因此它对数据的依赖更为强烈。
    • 这与其他组件（如 transformer 或 decoder）不同，这些组件是通用的子模块，可能在多个客户端中共享。

由于其本地化和数据相关的特性，node_embedding 在激活或冻结时可能需要单独处理。

2. 为什么需要单独设置？

虽然 node_embedding 被包含在 component_names 中，理论上已经能够参与通用逻辑，但以下情况使得单独处理是必要的：

(1) 它是一个特殊的组件，不能参与联邦聚合

  • 在 average_models 中，node_embedding 被排除在联邦聚合之外：
    if any(comp in key for comp in self.non_aggregatable_components):
    continue
  • 尽管它被动态控制激活和冻结，但在训练轮次之后的模型聚合中，它的参数会保持本地化，而不会被更新为全局状态。

(2) 需要明确控制梯度清零

  • 当冻结 node_embedding 时，需要显式地清零其梯度：
    if not active and param.grad is not None:
    param.grad.zero_()
  • 这是因为冻结时，梯度可能在之前的计算中遗留，导致意外的梯度更新。
  • 对于其他组件（如 transformer），其梯度可能已经被框架机制管理得当，但由于 node_embedding 的特殊性（嵌入层在反向传播中经常被直接计算），需要手动清零以确保冻结行为。

(3) 在架构中属于不可替代的核心组件

  • node_embedding 是一个必要的基础层，每个客户端都需要它来完成嵌入任务。
  • 如果没有这个层，模型将无法处理输入数据。
  • 因此，框架可能需要对它进行特殊的管理，确保在激活或冻结时有额外的控制逻辑。

3. 为什么单独设置不影响 component_names 逻辑？

单独设置并不会与通用逻辑冲突，原因是：

  • component_names 提供了统一的控制接口：

    • node_embedding 仍然在 component_names 中，因此仍会被 self.component_state 控制其激活或冻结状态。

  • 单独处理是补充而非替代：

    • 在单独处理逻辑中，明确对 node_embedding 进行设置，仅是补充逻辑，确保行为符合特殊要求。
    • 例如，清零梯度的操作是其他组件不需要的，单独处理可以避免不必要的操作开销。

4. 如果不单独设置，会发生什么？

如果不单独设置 node_embedding，可能会导致以下问题：

  1. 梯度遗留问题：

    • 当冻结 node_embedding 时，其梯度可能未被正确清零，导致在下一轮训练中意外更新。

  2. 行为不一致：

    • 如果 node_embedding 和其他组件统一处理，可能会忽略其本地化特性或数据相关特性，导致行为偏离预期。

  3. 难以适配其他逻辑：

    • 由于 node_embedding 是基础组件，如果未来需要对其加入更多本地化特定的逻辑，统一处理可能不够灵活。

5. 总结

  • 为什么单独设置：

    • 为了确保 node_embedding 的冻结行为（梯度清零）和特殊本地化特性得到正确管理。
    • 单独设置避免潜在的梯度遗留问题，同时增强对该特殊组件的控制。

  • 是否与 component_names 冲突？

    • 不冲突。component_names 控制了激活或冻结的高层逻辑，单独设置只是补充实现更细粒度的控制。

  • 未来扩展性：

    • 如果 node_embedding 在未来需要更多本地化特定的操作（如特定的正则化或动态权重调整），单独设置的逻辑能够更好地支持这些扩展。

希望这个解读能够清晰地回答您的问题！

DQN的代码

在这段代码中，Q 值是通过传递 状态向量 (state) 进入网络后计算的。状态向量包含客户端的损失、全局的损失以及组件的选择历史。这些信息综合构成了系统的当前状态，Q 值是 DQN 模型根据当前状态预测的每个组件在当前条件下的潜在价值。

1. Q 值的来源

Q 值是从 状态向量 通过 DQN 网络计算得到的，而不是直接传递网络参数或损失值。以下是相关代码逻辑：

状态向量的构建

def get_state(self, client_losses, global_losses, component_history):
    """构建状态向量"""
    # 客户端最近损失
    client_loss_vector = torch.tensor([
        losses['total'][-1] if losses['total'] else 0.0 for losses in client_losses.values()
    ])
    
    # 全局损失（如 NLL、KL 等）
    global_loss_vector = torch.tensor([
        global_losses[key][-1] if global_losses[key] else 0.0 for key in ['total', 'nll', 'kl', 'conf', 'sd']
    ])
    
    # 最近 5 轮的组件选择历史
    history_vector = torch.tensor(component_history[-5:] + [0] * (5 - len(component_history[-5:])))
    
    # 拼接为状态向量
    state = torch.cat([client_loss_vector, global_loss_vector, history_vector]).to(self.device)
    return state
  • 状态向量的组成：
    1. 客户端损失向量：表示每个客户端的最新损失情况，捕捉各客户端的局部性能。
    2. 全局损失向量：表示整个系统的性能（包括 NLL、KL、置信度等多个损失项）。
    3. 组件选择历史：记录最近 5 轮的组件选择情况，便于 DQN 模型了解上下文。

Q 值的计算

def select_action(self, state):
    if random.random() < self.epsilon:
        # 随机动作：每个组件有 0.5 的概率被激活
        return torch.bernoulli(torch.ones(self.action_size) * 0.5).to(self.device)
    
    with torch.no_grad():
        # 使用 DQN 网络预测 Q 值
        q_values = self.policy_net(state)
        # 根据 Q 值选择动作（阈值处理）
        return (q_values > 0).float()
  • Q 值的输入：状态向量 state。
  • Q 值的输出：每个组件的 Q 值，表示在当前状态下，该组件被激活的潜在价值。
  • 动作选择：通过 Q 值是否大于 0 转换为二进制动作（激活或冻结）。

总结：这里的 Q 值是对当前状态进行评估，而不是直接使用网络参数或损失值，而是通过网络预测状态中每个组件的潜在价值。

2. Frozen 和 Activate 是否对所有客户端一致？

核心代码分析

以下代码段展示了如何根据 Q 值选择的动作对客户端组件进行冻结或激活：

for i, client in enumerate(self.clients):
    for component_idx, active in enumerate(self.component_state):
        component_name = self.component_names[component_idx]
        if hasattr(client.model.transformer_model, component_name):
            component = getattr(client.model.transformer_model, component_name)
            if isinstance(component, torch.nn.Module):
                for param in component.parameters():
                    param.requires_grad = bool(active)

  • 作用范围：

    • 每个客户端的组件是否激活完全取决于 self.component_state。
    • self.component_state 是通过 DQN 模型选择的动作，每个轮次都是全局一致的。

  • 冻结/激活机制：

    • 对于所有客户端，如果某个组件在 self.component_state 中标记为激活（1），则该组件的参数将被解冻（requires_grad=True）。
    • 如果某个组件被标记为冻结（0），则该组件的参数将被冻结（requires_grad=False）。

是否一致：是的，所有客户端的组件更新方式一致。

  • 理由：
    • self.component_state 是全局状态，基于所有客户端和全局损失的状态信息决定。
    • 一旦某个组件被选定为激活或冻结，这一策略将应用于所有客户端。

改进建议：允许客户端个性化的组件选择

目前的实现对所有客户端使用统一的组件选择策略。如果想让每个客户端根据自身的情况（如异质性或性能）选择组件，可以修改代码逻辑。例如：

for i, client in enumerate(self.clients):
    # 为每个客户端独立生成组件选择动作
    client_state = self.dqn_agent.select_action(self.dqn_agent.get_state(
        {i: self.client_losses[i]},  # 仅传递该客户端的损失
        self.global_losses,
        self.component_history
    ))
    
    for component_idx, active in enumerate(client_state):
        component_name = self.component_names[component_idx]
        if hasattr(client.model.transformer_model, component_name):
            component = getattr(client.model.transformer_model, component_name)
            if isinstance(component, torch.nn.Module):
                for param in component.parameters():
                    param.requires_grad = bool(active)

通过这种方式，每个客户端可以有自己的 component_state，从而实现更加灵活的组件选择策略。

总结

  1. Q 值的来源：

    • Q 值是 DQN 模型通过状态向量预测的潜在价值，用于评估每个组件在当前状态下的作用。
    • 输入状态包括客户端损失、全局损失和组件选择历史，而不是直接传递网络参数或损失值。

  2. Frozen 和 Activate 是否一致：

    • 当前实现中，所有客户端使用相同的 component_state 进行更新，组件的激活或冻结对所有客户端一致。
    • 如果需要支持客户端个性化的组件选择，可以在 train_round 中为每个客户端独立生成组件选择动作。

通过这种设计，DQN 模型能够动态调整组件的训练，减少不必要的计算，并提升联邦学习的效率。

You make a very good point! You're correct - the global loss is actually calculated as the average of the client losses, so including both is redundant. Let me propose a more meaningful state representation:

import torch
import numpy as np

class DQNStateManager:
    def __init__(self, num_clients=5):
        self.num_clients = num_clients
        
    def create_example_state(self):
        """Create an example state with realistic values"""
        # Example client losses with all components
        client_losses = {
            0: {'total': [2.5, 2.3, 2.1], 'nll': [1.5, 1.4, 1.3], 'kl': [0.5, 0.4, 0.4], 
                'conf': [0.3, 0.3, 0.2], 'sd': [0.2, 0.2, 0.2]},
            1: {'total': [2.6, 2.4, 2.2], 'nll': [1.6, 1.5, 1.4], 'kl': [0.5, 0.4, 0.4], 
                'conf': [0.3, 0.3, 0.2], 'sd': [0.2, 0.2, 0.2]},
            2: {'total': [2.4, 2.2, 2.0], 'nll': [1.4, 1.3, 1.2], 'kl': [0.5, 0.4, 0.4], 
                'conf': [0.3, 0.3, 0.2], 'sd': [0.2, 0.2, 0.2]},
            3: {'total': [2.7, 2.5, 2.3], 'nll': [1.7, 1.6, 1.5], 'kl': [0.5, 0.4, 0.4], 
                'conf': [0.3, 0.3, 0.2], 'sd': [0.2, 0.2, 0.2]},
            4: {'total': [2.3, 2.1, 1.9], 'nll': [1.3, 1.2, 1.1], 'kl': [0.5, 0.4, 0.4], 
                'conf': [0.3, 0.3, 0.2], 'sd': [0.2, 0.2, 0.2]}
        }
        
        # Recent component selection history
        component_history = [
            [1, 1, 0, 1, 1, 0, 1, 1],  # Most recent selection
            [1, 0, 1, 1, 0, 1, 1, 1],  # Second most recent
            [1, 1, 1, 0, 1, 1, 0, 1],  # Third most recent
        ]
        
        return self.construct_state_vector(client_losses, component_history)
    
    def construct_state_vector(self, client_losses, component_history):
        """
        Construct the complete state vector with meaningful metrics
        """
        # 1. Loss component ratios for each client (nll/total, kl/total, conf/total, sd/total)
        loss_ratios = []
        for client_id in range(self.num_clients):
            client_data = client_losses[client_id]
            if client_data['total']:
                total = client_data['total'][-1]
                ratios = [
                    client_data['nll'][-1] / total,
                    client_data['kl'][-1] / total,
                    client_data['conf'][-1] / total,
                    client_data['sd'][-1] / total
                ]
                loss_ratios.extend(ratios)
            else:
                loss_ratios.extend([0.0] * 4)
        loss_ratios = torch.tensor(loss_ratios)
        
        # 2. Client heterogeneity metrics
        client_totals = torch.tensor([
            client_losses[i]['total'][-1] if client_losses[i]['total'] else 0.0
            for i in range(self.num_clients)
        ])
        mean_loss = client_totals.mean()
        std_loss = client_totals.std()
        relative_performance = client_totals / mean_loss
        
        # 3. Loss improvement rates (relative change in last 3 rounds)
        improvement_rates = []
        for client_id in range(self.num_clients):
            total_losses = client_losses[client_id]['total']
            if len(total_losses) >= 2:
                rate = (total_losses[-2] - total_losses[-1]) / total_losses[-2]
                improvement_rates.append(rate)
            else:
                improvement_rates.append(0.0)
        improvement_rates = torch.tensor(improvement_rates)
        
        # 4. Component selection history (flattened last 3 rounds)
        history_tensor = torch.tensor(component_history[-3:])
        history_vector = history_tensor.flatten()
        
        # Combine all parts into final state vector
        state_vector = torch.cat([
            loss_ratios,           # Shape: [num_clients * 4]
            relative_performance,  # Shape: [num_clients]
            torch.tensor([std_loss]),  # Shape: [1]
            improvement_rates,     # Shape: [num_clients]
            history_vector        # Shape: [24] (3 rounds * 8 components)
        ])
        
        return state_vector
    
    def decode_state(self, state_vector):
        """Decode state vector into human-readable format"""
        idx = 0
        
        # Extract loss ratios
        loss_ratios = state_vector[idx:idx + self.num_clients * 4].view(self.num_clients, 4)
        idx += self.num_clients * 4
        
        # Extract relative performance
        relative_perf = state_vector[idx:idx + self.num_clients]
        idx += self.num_clients
        
        # Extract loss std
        loss_std = state_vector[idx].item()
        idx += 1
        
        # Extract improvement rates
        improvement_rates = state_vector[idx:idx + self.num_clients]
        idx += self.num_clients
        
        # Extract component history
        history = state_vector[idx:].view(3, 8)
        
        return {
            'loss_ratios': {
                f'client_{i}': {
                    'nll_ratio': loss_ratios[i][0].item(),
                    'kl_ratio': loss_ratios[i][1].item(),
                    'conf_ratio': loss_ratios[i][2].item(),
                    'sd_ratio': loss_ratios[i][3].item()
                }
                for i in range(self.num_clients)
            },
            'relative_performance': relative_perf.tolist(),
            'loss_heterogeneity': loss_std,
            'improvement_rates': improvement_rates.tolist(),
            'component_history': history.tolist()
        }

# Example usage and demonstration
def demonstrate_state_representation():
    state_manager = DQNStateManager(num_clients=5)
    state_vector = state_manager.create_example_state()
    
    print("State Vector Shape:", state_vector.shape)
    print("\nState Vector:", state_vector)
    
    decoded_state = state_manager.decode_state(state_vector)
    print("\nDecoded State:")
    print("\n1. Loss Component Ratios:")
    for client, ratios in decoded_state['loss_ratios'].items():
        print(f"{client}:", ratios)
    print("\n2. Relative Performance:", decoded_state['relative_performance'])
    print("\n3. Loss Heterogeneity:", decoded_state['loss_heterogeneity'])
    print("\n4. Improvement Rates:", decoded_state['improvement_rates'])
    print("\n5. Component History (last 3 rounds):")
    for i, round_selection in enumerate(decoded_state['component_history']):
        print(f"Round {i}: {round_selection}")
    
    return state_vector, decoded_state

# Run demonstration
state_vector, decoded_state = demonstrate_state_representation()

The revised state representation now includes:

  1. Loss Component Ratios (20 values):

    • For each client: nll/total, kl/total, conf/total, sd/total
    • Shows the relative contribution of each loss component
    • Helps identify which components need more attention

  2. Relative Performance (5 values):

    • Each client's loss relative to the mean
    • Indicates which clients are performing better/worse than average

  3. Loss Heterogeneity (1 value):

    • Standard deviation of client losses
    • Measures how different the clients are performing

  4. Improvement Rates (5 values):

    • Rate of loss improvement for each client
    • Helps identify which clients are learning faster/slower

  5. Component Selection History (24 values):

    • Last 3 rounds of component selections
    • Maintains temporal context for decision making

This new representation:

  • Eliminates the redundancy between client and global losses
  • Provides more meaningful metrics about client heterogeneity
  • Captures the relative importance of different loss components
  • Tracks improvement trends
  • Has a total of 55 dimensions (20 + 5 + 1 + 5 + 24)

Would you like me to:

  1. Add additional metrics about client heterogeneity?
  2. Include more historical information?
  3. Modify how the loss ratios are calculated?

课程学习的设置

以下是您提供内容的中文翻译：

当前异质性计算方式

以下代码实现了计算客户端异质性的函数：

def calculate_heterogeneity(self, client):
    # 获取节点嵌入
    embeddings = client.model.transformer_model.node_embedding.weight.detach()
    # 计算质心（嵌入的平均值）
    centroid = torch.mean(embeddings, dim=0)
    # 计算每个嵌入点到质心的 L2 距离
    distances = torch.norm(embeddings - centroid, dim=1)
    # 返回平均距离作为异质性度量
    return torch.mean(distances).item()
  • 描述：
    • 此函数通过计算客户端节点嵌入与其均值的平均距离，测量节点嵌入分布的“分散程度”。
    • 较高的异质性值表示客户端的节点嵌入模式更加多样化/异质性更强。

如何使用异质性进行加权聚合

以下是基于异质性得分的加权聚合实现：

def weighted_average_models(self, models):
    """使用异质性得分进行加权的模型聚合"""
    aggregated_state = {}
    
    # 为每个客户端计算异质性得分
    heterogeneity_scores = []
    for client in self.clients:
        score = self.calculate_heterogeneity(client)
        heterogeneity_scores.append(score)
    
    # 将异质性得分转换为权重（异质性越高，权重越大）
    total_score = sum(heterogeneity_scores)
    weights = [score / total_score for score in heterogeneity_scores]
    
    # 获取参考模型状态
    reference_state = models[0].state_dict()
    
    for key in reference_state:
        # 跳过不可聚合的组件
        if any(comp in key for comp in self.non_aggregatable_components):
            continue
        
        # 跳过批归一化统计量
        if 'running_' in key or 'num_batches_tracked' in key:
            continue
            
        # 使用权重对参数进行加权平均
        aggregated_state[key] = torch.zeros_like(reference_state[key])
        for model, weight in zip(models, weights):
            model_param = model.state_dict()[key]
            aggregated_state[key] += model_param * weight
            
    return aggregated_state
  • 实现逻辑：
    1. 通过异质性计算函数计算每个客户端的得分。
    2. 根据异质性得分生成权重，异质性越高的客户端权重越大。
    3. 对模型参数进行加权平均，得到聚合后的全局模型。

结合性能的更高级加权聚合方案

在只使用异质性得分的基础上，我们还可以将 异质性 和 性能 结合起来，为每个客户端生成综合权重。例如：

def advanced_weighted_average(self, models):
    """结合异质性和性能得分的加权模型聚合"""
    aggregated_state = {}
    
    # 分别计算异质性得分和性能得分
    heterogeneity_scores = []
    performance_scores = []
    
    for i, client in enumerate(self.clients):
        # 获取异质性得分
        het_score = self.calculate_heterogeneity(client)
        heterogeneity_scores.append(het_score)
        
        # 获取性能得分（基于最近的损失值）
        perf_score = 1.0 / (self.client_losses[i]['total'][-1] + 1e-8)  # 损失越低，得分越高
        performance_scores.append(perf_score)
    
    # 对异质性和性能得分进行归一化
    het_total = sum(heterogeneity_scores)
    perf_total = sum(performance_scores)
    
    het_weights = [h / het_total for h in heterogeneity_scores]
    perf_weights = [p / perf_total for p in performance_scores]
    
    # 综合权重（通过 α 调整异质性和性能的平衡）
    α = 0.5
    combined_weights = [α * h + (1 - α) * p for h, p in zip(het_weights, perf_weights)]
    
    # 对综合权重进行归一化
    weight_sum = sum(combined_weights)
    final_weights = [w / weight_sum for w in combined_weights]
    
    # 使用综合权重进行模型参数的聚合
    reference_state = models[0].state_dict()
    
    for key in reference_state:
        if any(comp in key for comp in self.non_aggregatable_components):
            continue
        if 'running_' in key or 'num_batches_tracked' in key:
            continue
            
        aggregated_state[key] = torch.zeros_like(reference_state[key])
        for model, weight in zip(models, final_weights):
            model_param = model.state_dict()[key]
            aggregated_state[key] += model_param * weight
            
    return aggregated_state
  • 改进点：
    1. 同时考虑异质性和性能：
      • 使用异质性度量每个客户端数据的多样性。
      • 使用性能（例如损失值）衡量每个客户端模型的表现。
    2. 权重平衡：
      • 通过参数 $ \alpha $ 调整异质性和性能在权重中的比例。
      • $ \alpha = 0.5 $ 时，异质性和性能各占一半。
    3. 更灵活的聚合策略：
      • 权重综合后，最终的聚合模型能够更好地兼顾客户端的多样性和表现。

整体优势

上述方法的核心是利用客户端的 异质性 和 性能指标 来动态调整聚合权重。具体优势包括：

  1. 提升全局模型的泛化能力：
    • 通过考虑异质性，增强对多样化数据的适应性。
  2. 平衡客户端贡献：
    • 高性能和高异质性的客户端对全局模型的影响更大。
  3. 处理客户端异构性：
    • 保留客户端特定的组件（如 node_embedding），而非强制统一聚合。

您是否需要进一步详细说明如何调试或优化这些方法？

如果需要，我可以提供更多关于：

  • 如何调整异质性和性能的权重比例 $ \alpha $。
  • 如何在真实的联邦学习任务中验证这些方法的效果。

起始标志是

是的，在您提供的代码中：

current_token = target[:, 0].unsqueeze(1)  # Start token

起始值 (current_token) 是真实的目标序列的第一个 token，即 target[:, 0]。这通常被称为 起始标志 (Start-of-Sequence, SOS)，在解码过程中用于指示序列的开始。

为什么使用真实的起始值？

在生成序列的任务中，无论是训练还是推理模式，解码器都需要一个 起始 token 作为生成过程的起点。这个起始 token 的来源有以下几种情况：

  1. 训练模式：

    • 通常使用目标序列的真实起始 token (target[:, 0]) 作为起点。
    • 在训练中使用真实的起始值能够减少模型的复杂性，并确保模型专注于学习生成过程，而不是受到初始输入错误的干扰。

  2. 推理模式：

    • 在推理时，解码器通常使用一个固定的特殊 token（例如 <SOS> 或某个起始标志的索引值）作为起点。
    • 代码中使用 target[:, 0] 的实现方式在推理中可能会表现出以下几种情形：
      • 目标序列是提前定义好的特殊标志：例如，将 target[:, 0] 设置为 <SOS>。
      • 依赖测试时目标序列提供的真实信息：在某些任务中，可能会部分依赖输入的上下文信息作为起点。

推理中的注意点

如果当前模式是推理（即 train=False），您需要确保 target[:, 0] 的值是定义好的起始标志。通常，这个值是一个特殊的 token，表示生成的序列开始。例如：

  • 在机器翻译任务中，这个 token 通常是 <SOS>。
  • 在轨迹生成任务中，它可能是序列的起始位置。
current_token = torch.full((batch_size, 1), fill_value=sos_index, dtype=torch.long, device=target.device)

其中：

  • sos_index 是 <SOS> token 的索引值。
  • batch_size 是当前输入的批量大小。

总结

  • 在训练中：代码中直接使用真实的目标序列起点 (target[:, 0])，这通常是合理的。
  • 在推理中：起始值需要是一个明确定义的特殊 token（如 <SOS>），否则解码过程可能会依赖外部提供的真实值，从而无法模拟真实生成场景。

您可以根据具体任务，确认 target[:, 0] 是不是合理的起始值。如果是训练，这样的设计是合适的；如果是推理，建议改为使用一个明确的 <SOS> 标志作为起始值。

关于路径的生成

在这段代码中，train 和 inference 两种生成方式的区别体现在 训练模式 和 推理模式 的具体实现。以下是两者的关键区别和详细分析：

1. 训练模式 (Train)

机制：教师强制 (Teacher Forcing)

关键点：

  • 在训练模式下，目标序列 target 的真实值直接作为输入提供给解码器（self.transformer.decoder）。
  • 解码器一次性处理整个序列，生成固定长度的输出。

代码片段：

if train:
    # Teacher forcing with fixed length
    tgt = self.node_embedding(target) * math.sqrt(self.d_model)
    tgt = self.pos_encoder(tgt)
    
    # Create causal mask
    tgt_mask = self.generate_mask(max_len, target.device)
    
    # Decode full sequence
    output = self.transformer.decoder(tgt, z, tgt_mask)

详细说明：

  1. 目标序列直接输入：

    • 解码器的输入 tgt 是经过嵌入和位置编码的目标序列。
    • 此模式下解码器有 "真实的" 上下文信息，因此生成的序列质量更高。

  2. 掩码控制未来信息：

    • tgt_mask 用来阻止解码器看到未来的 tokens，保证序列生成的正确性。

  3. 并行处理整个序列：

    • 由于有真实目标序列的辅助，整个目标序列可以一次性输入到解码器中，并进行并行处理。

优点：

  • 快速并行处理：目标序列一次性输入，不需要逐步生成。
  • 学习效率高：模型直接学习目标序列的分布。

缺点：

  • 不能完全模拟推理阶段的逐步生成机制，可能会导致推理时生成质量下降。

2. 推理模式 (Inference)

机制：自回归生成 (Autoregressive Generation)

关键点：

  • 推理模式中，解码器逐步生成序列的每个 token。
  • 每生成一个 token，就将其作为下一个时间步的输入，直到生成完整序列或达到最大长度。

代码片段：

else:
    # Auto-regressive generation with fixed length
    outputs = []
    hidden = z
    current_token = target[:, 0].unsqueeze(1)  # Start token
    
    # Always generate max_len tokens
    for i in range(max_len):
        # Embed current token
        tgt = self.node_embedding(current_token) * math.sqrt(self.d_model)
        tgt = self.pos_encoder(tgt)
        
        # Create mask for current sequence length
        tgt_mask = self.generate_mask(tgt.size(1), tgt.device)
        
        # Generate next token
        output = self.transformer.decoder(tgt, hidden, tgt_mask)
        current_output = output[:, -1:]  # Get last token prediction
        
        outputs.append(current_output)
        
        # Use predicted token as next input
        current_token = torch.argmax(self.output_projection(current_output), dim=-1)
    
    # Combine all outputs
    output = torch.cat(outputs, dim=1)

详细说明：

  1. 逐步生成：

    • 解码器从起始 token 开始（current_token），每次只生成一个 token。
    • 上一步生成的 token 被用作下一步的输入。

  2. 动态输入控制：

    • 每次只输入当前生成的 token，而不是完整目标序列。
    • 掩码 tgt_mask 会随着生成的 token 数量动态变化。

  3. 固定生成长度：

    • 当前实现中生成固定长度的序列（max_len），但可以通过动态检查结束标志（EOS token）提前停止。

优点：

  • 能够真实模拟推理阶段的逐步生成机制。
  • 避免对真实目标序列的依赖，生成更加灵活。

缺点：

  • 无法并行处理序列，计算效率较低。
  • 如果模型训练不足，可能在逐步生成时出现错误传播。

3. 训练模式和推理模式的主要区别

特性 训练模式 推理模式
输入 使用真实目标序列（教师强制） 从起始 token 开始逐步生成
掩码 静态掩码（针对完整目标序列的固定长度掩码） 动态掩码（随生成长度变化，防止未来信息泄露）
生成方式 一次性生成整个序列，支持并行处理 每次生成一个 token，逐步生成序列
生成长度 由目标序列长度决定 固定长度（max_len）或动态停止（如遇到 EOS）
效率 并行计算，速度快 序列生成需逐步迭代，速度慢
真实场景模拟 不完全符合推理阶段的机制 完全符合推理阶段的逐步生成机制

4. 应用场景的选择

  • 训练模式 (Train)：

    • 适用于训练阶段，模型快速学习目标序列分布。
    • 通过真实目标序列（教师强制）引导模型学习，更容易收敛。

  • 推理模式 (Inference)：

    • 适用于测试或实际生成场景。
    • 模拟模型在真实场景中的逐步生成行为，灵活性更高。

5. 总结

  • 在训练时，模型利用目标序列通过 教师强制 学习，快速并行生成输出。
  • 在推理时，模型采用 自回归生成，从起始 token 开始逐步生成整个序列，更贴近实际应用场景。
  • 两种模式的结合确保了训练的高效性和推理的真实表现。如果需要动态生成长度的序列，可以在推理时加入结束标志（EOS）判断逻辑进一步优化生成过程。

异常分数的计算

You make a very good point. When using these losses as anomaly scores, summing them across the sequence length would indeed bias the scores toward longer trajectories. Let's modify the code to use mean calculations instead.

class TransformerTrajectoryModel(nn.Module):
    # ... [previous init and other methods remain the same] ...

    def compute_confidence(self, h, src):
        """
        Compute confidence using VAE with proper NLL loss and KL divergence
        Using mean instead of sum for sequence-length invariance
        """
        # Encode input through confidence encoder
        encoded = self.confidence_encoder(h)
        transformed = self.confidence_transformer(encoded)
        
        mu = self.confidence_mu(transformed)
        logvar = self.confidence_logvar(transformed)
        sigma = torch.exp(0.5 * logvar)
        
        q_z = torch.distributions.Normal(mu, sigma)
        z = q_z.rsample()
        decoded = self.confidence_decoder(z)
        logits = self.output_projection1(decoded)
        log_probs = torch.log_softmax(logits, dim=-1)
        
        batch_size, seq_len = src.shape
        log_probs_flat = log_probs.reshape(batch_size * seq_len, -1)
        src_flat = src.reshape(-1)
        
        # Calculate NLL loss and reshape
        nll_loss = self.nll(log_probs_flat, src_flat)
        nll_loss = nll_loss.reshape(batch_size, seq_len)
        
        # Calculate mean NLL loss per sequence
        mask = (src != self.num_nodes-1).float()  # Create mask for padding
        nll_loss = (nll_loss * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)
        
        # Calculate KL divergence with standard normal prior
        prior = torch.distributions.Normal(0, 1)
        kl_div = torch.distributions.kl_divergence(q_z, prior)
        
        # Take mean over sequence length and hidden dimensions
        kl_div = kl_div.mean(dim=-1)  # Mean over hidden dimensions
        kl_div = (kl_div * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)  # Mean over sequence
        
        # Combine losses (both are now means)
        total_loss = nll_loss + kl_div
        
        return total_loss

    def forward(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None):
        memory = self.encode(src, src_mask)
        confidence = self.compute_confidence(memory, src)
        
        batch_size = src.size(0)
        sd = torch.cat([src[:, 0].unsqueeze(1), src[:, -1].unsqueeze(1)], dim=1)
        memory_sd = self.encode1(sd)

        mu = self.encoder_mu(memory_sd)
        logvar = self.encoder_logvar(memory_sd)
        std = torch.exp(0.5 * logvar)
        
        eps = torch.randn_like(std)
        z = mu + eps * std
        
        # Compute mean VAE KL divergence
        vae_kl = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
        
        output = self.decode(z, tgt)
        logits = self.output_projection(output)

        # SD loss (already using mean through log_soft)
        sd_hidden = self.decode_sd(z)
        sd_logits = self.sd_projection_head(sd_hidden)
        sd_log_probs = self.log_soft(sd_logits.view(-1, sd_logits.size(-1)))
        sd_loss = 0.1 * self.sd_loss(sd_log_probs, sd.view(-1))
            
        return {
            'logits': logits,
            'confidence_kl': confidence,  # Now returns mean per sequence
            'vae_kl': vae_kl,  # Now returns mean
            'sd_loss': sd_loss
        }

class Model(nn.Module):
    # ... [previous init and other methods remain the same] ...

    def forward(self, src, trg, edge_list, src_lengths=None, trg_lengths=None):
        batch_size = src.size(0)
        seq_len = trg.size(1)
        
        src_padding_mask = (src == self.label_num-1)
        tgt_padding_mask = (trg == self.label_num-1)
        tgt_mask = self.transformer_model.generate_mask(trg.size(1), trg.device)
        
        output = self.transformer_model(
            src=src,
            tgt=trg,
            src_mask=None,
            tgt_mask=tgt_mask,
            memory_mask=None
        )
        
        logits = output['logits'].view(-1, self.label_num)
        label = trg.reshape(-1)
        mask = self.get_mask(edge_list, label, batch_size, seq_len)
        
        masked_logits = logits * mask.float()
        nll_loss = self.loss_fn(masked_logits, label)
        nll_loss = nll_loss.view(batch_size, seq_len)
        
        # Calculate mean loss per sequence using valid positions
        valid_positions = (trg != self.label_num-1).float()
        nll_loss = (nll_loss * valid_positions).sum(dim=1) / valid_positions.sum(dim=1).clamp(min=1e-8)
        
        return (
            nll_loss,  # Now returns mean per sequence
            output['vae_kl'],  # Already mean from TransformerTrajectoryModel
            output['confidence_kl'],  # Already mean from TransformerTrajectoryModel
            output['sd_loss']  # Already using mean through log_soft
        )

Key changes made:

  1. In compute_confidence:

    • NLL loss is now averaged over valid sequence positions using a mask
    • KL divergence is averaged over both hidden dimensions and sequence length

  2. In the main forward method:

    • VAE KL divergence uses torch.mean instead of torch.sum
    • The confidence and SD losses were already using means effectively

  3. In the Model class:

    • Added proper masking for padding tokens
    • NLL loss is now averaged over valid sequence positions

These changes ensure that all loss values are normalized by sequence length, making them comparable across trajectories of different lengths when used as anomaly scores. The clamp(min=1e-8) is added to prevent division by zero in case of all-padding sequences.

Would you like me to make any adjustments to how the means are calculated, or would you like to see how these changes affect the anomaly detection performance?

it seems that in the reference code, it generate with the max length for all trajectory (fixing), then in the snll loss, it calculate with masking for the actual length of the trajectory

Ah yes, you're right! Looking at the reference code more carefully, it uses a fixed-length generation with masking in the loss calculation. Let's revise the decoder to match this approach:

def decode(self, z, target, target_lengths=None, train=True):
    """
    Args:
        z: latent vector [batch_size, memory_seq_len, d_model]
        target: target sequence [batch_size, seq_len, d_model]
        target_lengths: actual lengths (used only for masking in loss)
    Returns:
        output: decoded sequence of fixed maximum length
    """
    batch_size = target.size(0)
    max_len = target.size(1)  # Use the maximum length from the batch
    
    if train:
        # Teacher forcing with fixed length
        tgt = self.node_embedding(target) * math.sqrt(self.d_model)
        tgt = self.pos_encoder(tgt)
        
        # Create causal mask
        tgt_mask = self.generate_mask(max_len, target.device)
        
        # Decode full sequence
        output = self.transformer.decoder(tgt, z, tgt_mask)
        
    else:
        # Auto-regressive generation with fixed length
        outputs = []
        hidden = z
        current_token = target[:, 0].unsqueeze(1)  # Start token
        
        # Always generate max_len tokens
        for i in range(max_len):
            # Embed current token
            tgt = self.node_embedding(current_token) * math.sqrt(self.d_model)
            tgt = self.pos_encoder(tgt)
            
            # Create mask for current sequence length
            tgt_mask = self.generate_mask(tgt.size(1), tgt.device)
            
            # Generate next token
            output = self.transformer.decoder(tgt, hidden, tgt_mask)
            current_output = output[:, -1:]  # Get last token prediction
            
            outputs.append(current_output)
            
            # Use predicted token as next input
            current_token = torch.argmax(self.output_projection(current_output), dim=-1)
        
        # Combine all outputs
        output = torch.cat(outputs, dim=1)

    return output

def forward(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None):
    batch_size = src.size(0)
    seq_len = tgt.size(1)
    
    # Get start and end points for SD task
    cond_src = src[:, 0].unsqueeze(1)  # First point
    cond_trg = src[:, -1].unsqueeze(1)  # Last point
    sd = torch.cat((cond_src, cond_trg), dim=1)
    
    # Encode start-end points
    memory_sd = self.encode(sd)
    
    # Encode to latent space
    mu = self.encoder_mu(memory_sd)
    logvar = self.encoder_logvar(memory_sd)
    std = torch.exp(0.5 * logvar)
    
    # Sample using reparameterization trick
    eps = torch.randn_like(std)
    z = mu + eps * std
    
    # Compute VAE KL divergence
    vae_kl = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
    
    # Compute confidence
    confidence = self.compute_confidence(memory_sd, sd)
    
    # Generate full trajectory with fixed length
    output = self.decode(z, tgt)
    logits = self.output_projection(output)
    
    # Decode for SD task
    sd_hidden = self.decode_sd(z)
    sd_logits = self.sd_projection_head(sd_hidden)
    sd_log_probs = self.log_soft(sd_logits.view(-1, sd_logits.size(-1)))
    sd_loss = 0.1 * self.sd_loss(sd_log_probs, sd.view(-1))
    
    return {
        'logits': logits,  # This will be masked in the loss calculation
        'confidence_kl': confidence,
        'vae_kl': vae_kl,
        'sd_loss': sd_loss
    }

This revised version:

  1. Always generates sequences of fixed maximum length
  2. Doesn't use sequence lengths in generation
  3. Relies on masking in the loss function to handle actual trajectory lengths
  4. Matches the reference implementation where the loss function applies masking:
nll_loss = self.loss_fn(p_x, label, mask)  # mask handles actual lengths
nll_loss = nll_loss.view(batch_size, seq_len)

This is more efficient than generating variable-length sequences since it can be done in parallel, and the masking in the loss function ensures we only consider the valid parts of each trajectory.

关于decoding的计算 (hidden (起点的embedding和终点的embedding相加) 作为辅助,target (去掉最后一位pad，包含bos和eos) 再输入到GRU网络中，得到的是max length-1)

I'll explain how the decoder processes the data in VAE. Let's walk through the calculation steps:

  1. The decoder receives three main inputs:
def forward(self, z, target, lengths=None, train=True):
    # z: (batch_size, latent_size) - latent vector from encoder
    # target: (batch_size, seq_len, hidden_size) - embedded target sequence
    # lengths: (batch_size) - actual lengths of sequences
  1. First, it transforms the latent vector into the initial hidden state:
hidden = self.hidden_linear(z)
# z: (batch_size, latent_size) 
# -> hidden: (batch_size, hidden_size*layer_num)

hidden = hidden.view(hidden.size(0), self.layer_num, self.hidden_size).transpose(0, 1)
# Reshapes to: (layer_num, batch_size, hidden_size)
  1. For efficient processing of variable length sequences:
packed_input = pack_padded_sequence(target, lengths, batch_first=True, enforce_sorted=False)
# Packs the padded sequence to handle variable lengths efficiently
  1. The GRU processes the sequence:
output, hidden = self.lstm(packed_input, hidden)
# packed_input: PackedSequence containing target embeddings
# hidden: (layer_num, batch_size, hidden_size) initial state
# output: PackedSequence containing all hidden states
# final hidden: (layer_num, batch_size, hidden_size)
  1. Finally, the output is unpacked and dropout is applied:
output = pad_packed_sequence(output, batch_first=True)[0]
# Unpacks to: (batch_size, max_seq_len, hidden_size)

p_x = self.dropout(output)
# Applies dropout for regularization

The key transformation path is:

z (latent) -> hidden state -> GRU(target sequence, hidden) -> output sequence
(batch, latent) -> (layer, batch, hidden) -> GRU -> (batch, seq, hidden)

This decoder essentially reconstructs the trajectory sequence from the latent representation while using teacher forcing during training, meaning it uses the actual target sequence as input at each step rather than its own predictions.

output, hidden = self.lstm(packed_input, hidden) so in gru，packed_input and hidden as two input and they will not concatenate at first

Correct! In GRU (and LSTM), the packed_input and hidden state operate differently and are not concatenated at the input. Here's how it works:

  1. The packed_input contains the sequence data:
packed_input: PackedSequence containing (batch_size, seq_len, input_size)
  1. The hidden state is maintained separately:
hidden: (num_layers, batch_size, hidden_size)

For each time step t, the GRU performs these operations:

  1. Takes current input xt from packed_input
  2. Uses previous hidden state ht-1
  3. Applies its internal gates and transformations:
    # GRU internal operations (simplified)
rt = sigmoid(Wr[xt, ht-1] + br)  # reset gate
zt = sigmoid(Wz[xt, ht-1] + bz)  # update gate
nt = tanh(Wn[xt, rt * ht-1] + bn)  # new gate
ht = (1 - zt) * ht-1 + zt * nt  # final hidden state

So while both inputs are used in the computation, they maintain distinct roles:

  • packed_input provides the sequence elements
  • hidden state carries forward the sequential information

The hidden state acts as the memory of the network, while the packed_input provides the new information at each step.

关于snll loss的计算

Let's analyze how BOS and EOS are handled in the loss computation:

  1. First, recall where label comes from in Model.forward():
label = torch.clone(trg[:, 1:])  # Takes target sequence excluding BOS token

This means label starts from the first actual node (excludes BOS) and includes the EOS token.

  1. Looking at the dimensions:
p_x: [batch_size*seq_len, num_nodes]  # Predictions for each position
label: [batch_size*seq_len]  # Ground truth for each position
mask: [batch_size*seq_len, num_nodes]  # Masks for valid transitions
  1. In the loss computation (loss_fn):
nll_loss = self.loss_fn(p_x, label, mask)
  • It computes loss for all positions in the sequence
  • The ignore_index parameter in CrossEntropyLoss is set to self.label_num-1 (PAD token)
  • Loss is computed for both regular nodes and the EOS token
  • BOS is not included in loss computation since it was excluded when creating label

So:

  • BOS: Not included in loss computation
  • EOS: Included in loss computation
  • PAD: Ignored in loss computation via ignore_index

This matches the standard sequence-to-sequence training paradigm where we want the model to learn to predict both the next node and when to end the sequence (EOS), but don't need to predict anything for BOS or PAD tokens.

Yes, your observation is correct. Based on what we verified earlier about the data preprocessing in TrajectoryLoader, the actual sequence order should be:

[BOS, node1, node2, ..., nodeN, EOS, PAD, PAD, ...]

And when this sequence is input to the decoder (after trg[:, :-1]), it becomes:

[BOS, node1, node2, ..., nodeN, EOS, PAD, ...]  # Original sequence
[BOS, node1, node2, ..., nodeN, EOS, PAD]       # After trg[:, :-1], removing last token

The key points are:

  1. EOS token is explicitly added after the actual trajectory nodes
  2. PAD tokens are added after EOS
  3. When fed into the decoder, the last token is removed (which would be the last PAD token)

My previous explanation was incorrect in suggesting the EOS token wasn't in the sequence. The EOS token is indeed present and positioned between the actual trajectory nodes and the padding tokens.

posted @ 2024-12-12 16:08  GraphL  阅读(63)  评论(0)    收藏  举报