misha-chertushkin
294 lines
9.2 KiB
Python
294 lines
9.2 KiB
Python
"""
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Example usage of the TimesFM Finetuning Framework.
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"""
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from os import path
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from typing import Optional, Tuple
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import numpy as np
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import pandas as pd
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import torch
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import torch.multiprocessing as mp
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import yfinance as yf
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from finetuning_torch import FinetuningConfig, TimesFMFinetuner
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from huggingface_hub import snapshot_download
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from torch.utils.data import Dataset
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from timesfm import TimesFm, TimesFmCheckpoint, TimesFmHparams
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from timesfm.pytorch_patched_decoder import PatchedTimeSeriesDecoder
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import os
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class TimeSeriesDataset(Dataset):
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"""Dataset for time series data compatible with TimesFM."""
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def __init__(self, series: np.ndarray, context_length: int, horizon_length: int):
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"""
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Initialize dataset.
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Args:
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series: Time series data
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context_length: Number of past timesteps to use as input
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horizon_length: Number of future timesteps to predict
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"""
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self.series = series
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self.context_length = context_length
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self.horizon_length = horizon_length
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self._prepare_samples()
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def _prepare_samples(self) -> None:
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"""Prepare sliding window samples from the time series."""
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self.samples = []
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total_length = self.context_length + self.horizon_length
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for start_idx in range(0, len(self.series) - total_length + 1):
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end_idx = start_idx + self.context_length
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x_context = self.series[start_idx:end_idx]
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x_future = self.series[end_idx : end_idx + self.horizon_length]
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self.samples.append((x_context, x_future))
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def __len__(self) -> int:
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return len(self.samples)
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def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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x_context, x_future = self.samples[index]
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x_context = torch.tensor(x_context, dtype=torch.float32)
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x_future = torch.tensor(x_future, dtype=torch.float32)
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input_padding = torch.zeros_like(x_context)
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freq = torch.zeros(1, dtype=torch.long)
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return x_context, input_padding, freq, x_future
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def prepare_datasets(
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series: np.ndarray, context_length: int, horizon_length: int, train_split: float = 0.8
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) -> Tuple[Dataset, Dataset]:
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"""
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Prepare training and validation datasets from time series data.
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Args:
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series: Input time series data
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context_length: Number of past timesteps to use
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horizon_length: Number of future timesteps to predict
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train_split: Fraction of data to use for training
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Returns:
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Tuple of (train_dataset, val_dataset)
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"""
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train_size = int(len(series) * train_split)
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train_data = series[:train_size]
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val_data = series[train_size:]
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# Create datasets
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train_dataset = TimeSeriesDataset(train_data, context_length=context_length, horizon_length=horizon_length)
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val_dataset = TimeSeriesDataset(val_data, context_length=context_length, horizon_length=horizon_length)
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return train_dataset, val_dataset
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def get_model(load_weights: bool = False):
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device = "cuda" if torch.cuda.is_available() else "cpu"
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repo_id = "google/timesfm-2.0-500m-pytorch"
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hparams = TimesFmHparams(
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backend=device,
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per_core_batch_size=32,
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horizon_len=128,
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num_layers=50,
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use_positional_embedding=False,
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context_len=192,
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)
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tfm = TimesFm(hparams=hparams, checkpoint=TimesFmCheckpoint(huggingface_repo_id=repo_id))
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model = PatchedTimeSeriesDecoder(tfm._model_config)
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if load_weights:
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checkpoint_path = path.join(snapshot_download(repo_id), "torch_model.ckpt")
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loaded_checkpoint = torch.load(checkpoint_path, weights_only=True)
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model.load_state_dict(loaded_checkpoint)
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model = model.to(device)
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return model, hparams, tfm._model_config
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def plot_predictions(
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model: TimesFm,
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val_dataset: Dataset,
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save_path: Optional[str] = "predictions.png",
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) -> None:
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"""
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Plot model predictions against ground truth for a batch of validation data.
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Args:
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model: Trained TimesFM model
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val_dataset: Validation dataset
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save_path: Path to save the plot
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"""
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import matplotlib.pyplot as plt
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model.eval()
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x_context, x_padding, freq, x_future = val_dataset[0]
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x_context = x_context.unsqueeze(0) # Add batch dimension
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x_padding = x_padding.unsqueeze(0)
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freq = freq.unsqueeze(0)
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x_future = x_future.unsqueeze(0)
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device = next(model.parameters()).device
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x_context = x_context.to(device)
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x_padding = x_padding.to(device)
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freq = freq.to(device)
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x_future = x_future.to(device)
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with torch.no_grad():
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predictions = model(x_context, x_padding.float(), freq)
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predictions_mean = predictions[..., 0] # [B, N, horizon_len]
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last_patch_pred = predictions_mean[:, -1, :] # [B, horizon_len]
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context_vals = x_context[0].cpu().numpy()
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future_vals = x_future[0].cpu().numpy()
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pred_vals = last_patch_pred[0].cpu().numpy()
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context_len = len(context_vals)
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horizon_len = len(future_vals)
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plt.figure(figsize=(12, 6))
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plt.plot(range(context_len), context_vals, label="Historical Data", color="blue", linewidth=2)
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plt.plot(
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range(context_len, context_len + horizon_len),
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future_vals,
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label="Ground Truth",
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color="green",
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linestyle="--",
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linewidth=2,
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)
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plt.plot(range(context_len, context_len + horizon_len), pred_vals, label="Prediction", color="red", linewidth=2)
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plt.xlabel("Time Step")
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plt.ylabel("Value")
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plt.title("TimesFM Predictions vs Ground Truth")
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plt.legend()
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plt.grid(True)
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if save_path:
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plt.savefig(save_path)
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print(f"Plot saved to {save_path}")
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plt.close()
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def get_data(context_len: int, horizon_len: int) -> Tuple[Dataset, Dataset]:
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df = yf.download("AAPL", start="2010-01-01", end="2019-01-01")
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time_series = df["Close"].values
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train_dataset, val_dataset = prepare_datasets(
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series=time_series,
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context_length=context_len,
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horizon_length=horizon_len,
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train_split=0.8,
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)
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print(f"Created datasets:")
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print(f"- Training samples: {len(train_dataset)}")
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print(f"- Validation samples: {len(val_dataset)}")
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return train_dataset, val_dataset
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def single_gpu_example():
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"""Basic example of finetuning TimesFM on stock data."""
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model, hparams, tfm_config = get_model(load_weights=True)
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config = FinetuningConfig(batch_size=256, num_epochs=5, learning_rate=1e-4, use_wandb=True)
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train_dataset, val_dataset = get_data(128, tfm_config.horizon_len)
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finetuner = TimesFMFinetuner(model, config)
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print("\nStarting finetuning...")
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results = finetuner.finetune(train_dataset=train_dataset, val_dataset=val_dataset)
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print("\nFinetuning completed!")
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print(f"Training history: {len(results['history']['train_loss'])} epochs")
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plot_predictions(
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model=model,
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val_dataset=val_dataset,
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save_path="timesfm_predictions.png",
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)
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def setup_process(rank, world_size, model, config, train_dataset, val_dataset, return_dict):
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"""Initialize the distributed process."""
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# Set up the process group
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = "12355"
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# Initialize the process group
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torch.distributed.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=rank)
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# Set the device for this process
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torch.cuda.set_device(rank)
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try:
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finetuner = TimesFMFinetuner(model, config, rank=rank)
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results = finetuner.finetune(train_dataset=train_dataset, val_dataset=val_dataset)
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if rank == 0: # Only store results and plot from the main process
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return_dict["results"] = results
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plot_predictions(
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model=model,
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val_dataset=val_dataset,
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save_path="timesfm_predictions.png",
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)
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finally:
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# Cleanup - important!
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torch.distributed.destroy_process_group()
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def multi_gpu_example():
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"""Example of finetuning TimesFM using multiple GPUs."""
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# Define which GPUs to use
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gpu_ids = [0] # Just using one GPU
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world_size = len(gpu_ids)
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# Initialize model and config
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model, hparams, tfm_config = get_model(load_weights=True)
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config = FinetuningConfig(
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batch_size=256,
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num_epochs=5,
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learning_rate=1e-4,
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use_wandb=False,
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distributed=True,
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gpu_ids=gpu_ids,
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)
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# Get datasets
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train_dataset, val_dataset = get_data(128, tfm_config.horizon_len)
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# Create a multiprocessing manager to share results between processes
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manager = mp.Manager()
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return_dict = manager.dict()
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# Launch processes
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mp.spawn(
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setup_process,
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args=(world_size, model, config, train_dataset, val_dataset, return_dict),
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nprocs=world_size,
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join=True,
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)
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# Get results from the main process
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results = return_dict.get("results", None)
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print("\nFinetuning completed!")
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if results:
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print(f"Training history: {len(results['history']['train_loss'])} epochs")
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return results
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if __name__ == "__main__":
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# Use either single GPU or multi-GPU example
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# basic_example() # Single GPU
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multi_gpu_example() # Multi-GPU
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