misha-chertushkin
227 lines
7.3 KiB
Python
227 lines
7.3 KiB
Python
"""
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TimesFM Finetuner: A flexible framework for finetuning TimesFM models on custom datasets.
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Example usage:
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```python
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# Prepare datasets
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train_dataset = TimeSeriesDataset(train_data, context_length=128, horizon_length=32)
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val_dataset = TimeSeriesDataset(val_data, context_length=128, horizon_length=32)
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# Initialize model and configuration
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model = TimesFm(...)
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config = FinetuningConfig(
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batch_size=64,
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num_epochs=50,
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learning_rate=1e-4,
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use_wandb=True
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)
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# Create finetuner
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finetuner = TimesFMFinetuner(model, config)
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# Finetune model
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results = finetuner.finetune(train_dataset, val_dataset)
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```
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"""
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import abc
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import logging
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import multiprocessing as mp
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import torch
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import torch.distributed as dist
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import torch.optim as optim
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.data import DataLoader, Dataset
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import wandb
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from timesfm import TimesFm
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@dataclass
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class FinetuningConfig:
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"""Configuration for TimesFM finetuning process."""
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# Training parameters
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batch_size: int = 32
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num_epochs: int = 20
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learning_rate: float = 1e-4
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weight_decay: float = 0.01
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# Hardware parameters
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device: str = "cuda" if torch.cuda.is_available() else "cpu"
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distributed: bool = False
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world_size: int = 1
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# Logging parameters
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use_wandb: bool = False
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wandb_project: str = "timesfm-finetuning"
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gpu_ids: List[int] = field(default_factory=lambda: [0]) # List of GPU IDs to use
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distributed: bool = False
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master_port: str = "12355"
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master_addr: str = "localhost"
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class TimesFMFinetuner:
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def __init__(
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self,
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model: TimesFm,
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config: FinetuningConfig,
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rank: int = 0,
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loss_fn: Optional[callable] = None,
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logger: Optional[logging.Logger] = None,
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):
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self.model = model
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self.config = config
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self.logger = logger or logging.getLogger(__name__)
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self.rank = rank
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if config.distributed:
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self._setup_distributed(rank)
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self.device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
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self.loss_fn = loss_fn or (lambda x, y: torch.mean((x - y.squeeze(-1)) ** 2))
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if config.use_wandb and rank == 0: # Only initialize wandb on main process
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self._setup_wandb()
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def _setup_distributed(self, rank):
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"""Setup distributed training environment."""
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os.environ["MASTER_ADDR"] = self.config.master_addr
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os.environ["MASTER_PORT"] = self.config.master_port
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if not dist.is_initialized():
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dist.init_process_group(backend="nccl", world_size=len(self.config.gpu_ids), rank=rank)
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def _setup_wandb(self) -> None:
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"""Initialize Weights & Biases logging."""
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wandb.init(project=self.config.wandb_project, config=self.config.__dict__)
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def _create_dataloader(self, dataset: Dataset, name: str) -> DataLoader:
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"""Create a dataloader from a dataset."""
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if self.config.distributed:
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sampler = torch.utils.data.distributed.DistributedSampler(
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dataset, num_replicas=len(self.config.gpu_ids), rank=dist.get_rank(), shuffle=name == "train"
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)
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else:
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sampler = None
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return DataLoader(
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dataset,
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batch_size=self.config.batch_size,
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shuffle=(name == "train" and not self.config.distributed),
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num_workers=mp.cpu_count() // len(self.config.gpu_ids),
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pin_memory=self.device.type == "cuda",
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persistent_workers=True,
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prefetch_factor=2,
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sampler=sampler,
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)
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def _train_epoch(self, train_loader: DataLoader, optimizer: torch.optim.Optimizer) -> float:
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"""Train for one epoch."""
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self.model.train()
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total_loss = 0.0
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n_batches = len(train_loader)
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for batch in train_loader:
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x_context, x_padding, freq, x_future = [t.to(self.device, non_blocking=True) for t in batch]
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predictions = self.model(x_context, x_padding.float(), freq)
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predictions_mean = predictions[..., 0]
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last_patch_pred = predictions_mean[:, -1, :]
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loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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return total_loss / n_batches
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@torch.no_grad()
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def _validate(self, val_loader: DataLoader) -> float:
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"""Perform validation."""
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self.model.eval()
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total_loss = 0.0
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for batch in val_loader:
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x_context, x_padding, freq, x_future = [t.to(self.device) for t in batch]
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predictions = self.model(x_context, x_padding.float(), freq)
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predictions_mean = predictions[..., 0]
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last_patch_pred = predictions_mean[:, -1, :]
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loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))
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total_loss += loss.item()
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return total_loss / len(val_loader)
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def finetune(self, train_dataset: Dataset, val_dataset: Dataset) -> Dict[str, Any]:
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"""
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Finetune the TimesFM model on the provided datasets.
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Args:
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train_dataset: Training dataset
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val_dataset: Validation dataset
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Returns:
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Dict containing training history and best model path
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"""
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self.model = self.model.to(self.device)
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if self.config.distributed:
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self.model = DDP(
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self.model,
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device_ids=[self.config.gpu_ids[dist.get_rank()]],
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output_device=self.config.gpu_ids[dist.get_rank()],
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)
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train_loader = self._create_dataloader(train_dataset, "train")
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val_loader = self._create_dataloader(val_dataset, "val")
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optimizer = optim.Adam(
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self.model.parameters(), lr=self.config.learning_rate, weight_decay=self.config.weight_decay
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)
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history = {"train_loss": [], "val_loss": [], "learning_rate": []}
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self.logger.info(f"Starting training for {self.config.num_epochs} epochs...")
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self.logger.info(f"Training samples: {len(train_dataset)}")
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self.logger.info(f"Validation samples: {len(val_dataset)}")
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try:
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for epoch in range(self.config.num_epochs):
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train_loss = self._train_epoch(train_loader, optimizer)
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val_loss = self._validate(val_loader)
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current_lr = optimizer.param_groups[0]["lr"]
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history["train_loss"].append(train_loss)
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history["val_loss"].append(val_loss)
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history["learning_rate"].append(current_lr)
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metrics = {
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"train_loss": train_loss,
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"val_loss": val_loss,
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"learning_rate": current_lr,
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"epoch": epoch + 1,
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}
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if self.config.use_wandb:
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wandb.log(metrics)
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print(f"[Epoch {epoch+1}] Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")
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except KeyboardInterrupt:
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self.logger.info("Training interrupted by user")
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if self.config.distributed:
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dist.destroy_process_group()
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return {"history": history}
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