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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
} |