models/time_rcd/TimeRCD_pretrain_multi.py

import datetime
import itertools
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, DistributedSampler
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
import random
import numpy as np
from typing import Tuple, List, Dict, Any, Union, Optional
from dataclasses import dataclass

from .dataset import ChatTSTimeRCDPretrainDataset
from .ts_encoder_bi_bias import TimeSeriesEncoder
from .time_rcd_config import TimeRCDConfig, default_config

import warnings

warnings.filterwarnings("ignore")

@dataclass
class PretrainBatch:
    """Batch structure for pretraining tasks."""
    time_series: torch.Tensor
    labels: torch.Tensor
    masked_time_series: torch.Tensor
    mask_indices: torch.Tensor


class TimeSeriesPretrainModel(nn.Module):
    """Model for time series pretraining with masked reconstruction and anomaly detection."""

    def __init__(self, config: TimeRCDConfig):
        super().__init__()
        self.config = config

        # Extract TimeSeriesEncoder parameters from config
        ts_config = config.ts_config
        self.ts_encoder = TimeSeriesEncoder(
            d_model=ts_config.d_model,
            d_proj=ts_config.d_proj,
            patch_size=ts_config.patch_size,
            num_layers=ts_config.num_layers,
            num_heads=ts_config.num_heads,
            d_ff_dropout=ts_config.d_ff_dropout,
            use_rope=ts_config.use_rope,
            num_features=ts_config.num_features,
            activation=ts_config.activation
        )

        # Masked reconstruction head
        self.reconstruction_head = nn.Sequential(
            nn.Linear(config.ts_config.d_proj, config.ts_config.d_proj * 4),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.ts_config.d_proj * 4, config.ts_config.d_proj * 4),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.ts_config.d_proj * 4, 1)  # (B, seq_len, num_features, 1)
        )

        # Anomaly detection head
        self.anomaly_head = nn.Sequential(
            nn.Linear(config.ts_config.d_proj, config.ts_config.d_proj // 2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.ts_config.d_proj // 2, 2)  # (B, seq_len, num_features, 2) for binary classification
        )

    def forward(self, time_series: torch.Tensor, mask: Optional[torch.Tensor] = None):
        """Forward pass through the encoder."""
        local_embeddings = self.ts_encoder(time_series, mask)
        return local_embeddings

    def masked_reconstruction_loss(self,
                                   local_embeddings: torch.Tensor,  # (B, seq_len, num_features, d_proj)
                                   original_time_series: torch.Tensor,  # (B, seq_len, num_features),
                                   mask: torch.Tensor  # (B, seq_len)
                                   ) -> torch.Tensor:
        """Compute masked reconstruction loss."""
        batch_size, seq_len, num_features = original_time_series.shape
        patch_size = self.config.ts_config.patch_size

        mask = mask.bool()

        # local_embeddings: [B, seq_len, num_features, d_proj]
        reconstructed = self.reconstruction_head(local_embeddings)  # (B, seq_len, num_features, 1)
        reconstructed = reconstructed.view(batch_size, seq_len, num_features)

        mask_expanded = mask.unsqueeze(-1).expand(-1, -1, num_features)  # (B, seq_len, num_features)
        reconstruction_loss = F.mse_loss(
            reconstructed[mask_expanded],
            original_time_series[mask_expanded]
        )
        return reconstruction_loss

    def anomaly_detection_loss(self,
                               local_embeddings: torch.Tensor,  # (B, seq_len, num_features, d_proj)
                               labels: torch.Tensor) -> torch.Tensor:  # (B, seq_len)
        """Compute anomaly detection loss for each timestep."""
        # Project local embeddings to anomaly scores
        logits = self.anomaly_head(local_embeddings)  # (B, seq_len, num_features, 2)
        logits = torch.mean(logits, dim=-2)  # Average over num_features to get (B, seq_len, 2)

        # Reshape for loss computation
        batch_size, seq_len, _ = logits.shape
        logits = logits.view(-1, 2)  # (B*seq_len, 2)
        labels = labels.view(-1)  # (B*seq_len)
        labels = (labels > 0.5).long()
        # Create mask for valid labels (not padding)
        valid_mask = (labels != -1)

        # Compute loss only on valid timesteps
        if valid_mask.sum() > 0:
            anomaly_loss = F.cross_entropy(
                logits[valid_mask],
                labels[valid_mask]
            )
        else:
            anomaly_loss = torch.tensor(0.0, device=logits.device)

        return anomaly_loss


def create_random_mask(time_series: torch.Tensor,  # (B, max_seq_len, num_features)
                       attention_mask: torch.Tensor,  # (B, max_seq_len)
                       mask_ratio: float = 0.15) -> Tuple[torch.Tensor, torch.Tensor]:
    """Create random mask for time series patches, only masking valid sequence parts."""
    batch_size, seq_len, num_features = time_series.shape
    patch_size = default_config.ts_config.patch_size

    mask = torch.zeros(batch_size, seq_len)  # (B, max_seq_len)

    for i in range(batch_size):
        # Get valid sequence length for this sample
        valid_length = attention_mask[i].sum().item()

        # Calculate number of patches in valid sequence
        num_valid_patches = (valid_length - 1) // patch_size + 1
        num_masked = int(num_valid_patches * mask_ratio)

        if num_masked > 0:
            # Only select patches from valid sequence
            masked_patches = torch.randperm(num_valid_patches)[:num_masked]
            for j in masked_patches:
                start_idx = j * patch_size
                end_idx = min((j + 1) * patch_size, valid_length)  # Don't exceed valid length
                mask[i, start_idx:end_idx] = 1

    # Create masked time series - only mask valid parts
    masked_time_series = time_series.clone()
    mask_indices = mask.bool() & attention_mask  # Only mask where both mask and attention_mask are True
    mask_expanded = mask_indices.unsqueeze(-1).expand(-1, -1, num_features)  # (B, max_seq_len, num_features)
    masked_time_series[mask_expanded] = torch.randn_like(masked_time_series[mask_expanded]) * 0.1

    # Update mask to only include valid parts
    mask = mask * attention_mask.float()

    return masked_time_series, mask  # (B, max_seq_len, num_features), (B, max_seq_len)


def collate_fn(batch):
    """Collate function for pretraining dataset."""
    time_series_list, normal_time_series_list, labels_list, attribute_list = zip(*batch)

    # Convert to tensors and pad sequences
    if time_series_list[0].ndim == 1:
        time_series_tensors = [ts.unsqueeze(-1) for ts in time_series_list]  # Add feature dimension
        normal_time_series_tensors = [nts.unsqueeze(-1) for nts in normal_time_series_list]
    else:
        time_series_tensors = [ts for ts in time_series_list]
        normal_time_series_tensors = [nts for nts in normal_time_series_list]

    # standardize time series
    concatenated = torch.cat(time_series_tensors, dim=0)  # (total_length, num_features)
    mean = concatenated.mean(dim=0, keepdim=True)  # (1, num_features)
    std = concatenated.std(dim=0, keepdim=True)  # (1, num_features)
    std = std + 1e-4
    time_series_tensors_std = [(ts - mean) / std for ts in time_series_tensors]
    normal_time_series_tensors_std = [(nts - mean) / std for nts in normal_time_series_tensors]
    time_series_tensors = time_series_tensors_std
    normal_time_series_tensors = normal_time_series_tensors_std

    # labels_tensor = torch.stack(labels_list)
    labels = [label for label in labels_list]
    # Pad time series to same length
    padded_time_series = torch.nn.utils.rnn.pad_sequence(
        time_series_tensors, batch_first=True, padding_value=0.0
    )  # (B, max_seq_len, num_features)
    padded_normal_time_series = torch.nn.utils.rnn.pad_sequence(
        normal_time_series_tensors, batch_first=True, padding_value=0.0
    )  # (B, max_seq_len, num_features)
    padded_labels = torch.nn.utils.rnn.pad_sequence(
        labels, batch_first=True, padding_value=-1
    )  # (B, max_seq_len)

    sequence_lengths = [ts.size(0) for ts in time_series_tensors]
    B, max_seq_len, num_features = padded_time_series.shape
    attention_mask = torch.zeros(B, max_seq_len, dtype=torch.bool)  # (B, max_seq_len)
    for i, length in enumerate(sequence_lengths):
        attention_mask[i, :length] = True

        # Create random masks for reconstruction task - only mask valid sequence parts
    masked_time_series, mask = create_random_mask(padded_time_series, attention_mask)

    return {
        'time_series': padded_time_series,
        'normal_time_series': padded_normal_time_series,
        'masked_time_series': masked_time_series,
        'mask': mask,  # for reconstruction task
        'labels': padded_labels,
        'attention_mask': attention_mask,  # for padding
        'attribute': attribute_list
    }

def test_collate_fn(batch):
    """Collate function for pretraining dataset."""
    # Unpack the batch correctly - batch is a list of (time_series, mask) tuples
    time_series_list, mask_list = zip(*batch)

    # Stack into batch format instead of concatenating
    # This maintains the batch dimension: (B, seq_len, num_features)
    batched_time_series = torch.stack(time_series_list, dim=0)
    print(f"batched_time_series shape: {batched_time_series.shape}")
    # Stack masks into batch format: (B, seq_len)
    batched_mask = torch.stack(mask_list, dim=0)
    print(f"batched_mask shape: {batched_mask.shape}")

    return {
        'time_series': batched_time_series,
        'attention_mask': batched_mask,  # for padding
    }