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misha-chertushkin
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"""
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Example usage of the TimesFM Finetuning Framework.
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For single GPU:
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python script.py --training_mode=single
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For multiple GPUs:
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python script.py --training_mode=multi --gpu_ids=0,1,2
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"""
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import os
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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 absl import app, flags
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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 finetuning.finetuning_torch import FinetuningConfig, TimesFMFinetuner
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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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FLAGS = flags.FLAGS
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flags.DEFINE_enum(
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"training_mode",
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"single",
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["single", "multi"],
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'Training mode: "single" for single-GPU or "multi" for multi-GPU training.',
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)
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flags.DEFINE_list(
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"gpu_ids", ["0"],
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"Comma-separated list of GPU IDs to use for multi-GPU training. Example: 0,1,2"
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)
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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,
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series: np.ndarray,
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context_length: int,
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horizon_length: int,
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freq_type: int = 0):
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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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freq_type: Frequency type (0, 1, or 2)
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"""
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if freq_type not in [0, 1, 2]:
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raise ValueError("freq_type must be 0, 1, or 2")
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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.freq_type = freq_type
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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__(
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self, index: int
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) -> 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.tensor([self.freq_type], dtype=torch.long)
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return x_context, input_padding, freq, x_future
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def prepare_datasets(series: np.ndarray,
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context_length: int,
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horizon_length: int,
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freq_type: int = 0,
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train_split: float = 0.8) -> 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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freq_type: Frequency type (0, 1, or 2)
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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 with specified frequency type
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train_dataset = TimeSeriesDataset(train_data,
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context_length=context_length,
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horizon_length=horizon_length,
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freq_type=freq_type)
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val_dataset = TimeSeriesDataset(val_data,
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context_length=context_length,
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horizon_length=horizon_length,
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freq_type=freq_type)
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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=
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192, # Context length can be anything up to 2048 in multiples of 32
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)
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tfm = TimesFm(hparams=hparams,
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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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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),
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context_vals,
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label="Historical Data",
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color="blue",
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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),
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pred_vals,
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label="Prediction",
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color="red",
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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,
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horizon_len: int,
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freq_type: int = 0) -> 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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freq_type=freq_type,
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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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print(f"- Using frequency type: {freq_type}")
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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,
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num_epochs=5,
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learning_rate=1e-4,
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use_wandb=True,
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freq_type=1,
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log_every_n_steps=10,
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val_check_interval=0.5,
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use_quantile_loss=True)
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train_dataset, val_dataset = get_data(128,
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tfm_config.horizon_len,
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freq_type=config.freq_type)
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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,
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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,
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return_dict):
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"""Setup process function with optimized CUDA handling."""
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try:
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if torch.cuda.is_available():
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torch.cuda.set_device(rank)
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os.environ["MASTER_ADDR"] = config.master_addr
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os.environ["MASTER_PORT"] = config.master_port
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if not torch.distributed.is_initialized():
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torch.distributed.init_process_group(backend="nccl",
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world_size=world_size,
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rank=rank)
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finetuner = TimesFMFinetuner(model, config, rank=rank)
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results = finetuner.finetune(train_dataset=train_dataset,
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val_dataset=val_dataset)
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if rank == 0:
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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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except Exception as e:
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print(f"Error in process {rank}: {str(e)}")
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raise e
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finally:
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if torch.distributed.is_initialized():
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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 with optimized spawn."""
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mp.set_start_method("spawn", force=True)
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gpu_ids = [0, 1]
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world_size = len(gpu_ids)
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model, hparams, tfm_config = get_model(load_weights=True)
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# Create config
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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=3e-5,
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use_wandb=True,
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distributed=True,
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gpu_ids=gpu_ids,
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log_every_n_steps=50,
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val_check_interval=0.5,
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)
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train_dataset, val_dataset = get_data(128, tfm_config.horizon_len)
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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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results = return_dict.get("results", None)
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print("\nFinetuning completed!")
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return results
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def main(argv):
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"""Main function that selects and runs the appropriate training mode."""
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try:
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if FLAGS.training_mode == "single":
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print("\nStarting single-GPU training...")
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single_gpu_example()
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else:
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gpu_ids = [int(id) for id in FLAGS.gpu_ids]
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print(f"\nStarting multi-GPU training using GPUs: {gpu_ids}...")
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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=3e-5,
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use_wandb=True,
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distributed=True,
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gpu_ids=gpu_ids,
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)
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results = multi_gpu_example(config)
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print("\nMulti-GPU training completed!")
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except Exception as e:
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print(f"Training failed: {str(e)}")
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finally:
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if torch.distributed.is_initialized():
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torch.distributed.destroy_process_group()
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if __name__ == "__main__":
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app.run(main)
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@@ -0,0 +1,398 @@
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"""
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TimesFM Finetuner: A flexible framework for finetuning TimesFM models on custom datasets.
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"""
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import logging
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import os
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, Callable, Dict, List, Optional
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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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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from timesfm.patched_decoder import DEFAULT_QUANTILES
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import wandb
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class MetricsLogger(ABC):
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"""Abstract base class for logging metrics during training.
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This class defines the interface for logging metrics during model training.
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Concrete implementations can log to different backends (e.g., WandB, TensorBoard).
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"""
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@abstractmethod
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def log_metrics(self,
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metrics: Dict[str, Any],
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step: Optional[int] = None) -> None:
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"""Log metrics to the specified backend.
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Args:
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metrics: Dictionary containing metric names and values.
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step: Optional step number or epoch for the metrics.
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"""
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pass
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@abstractmethod
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def close(self) -> None:
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"""Clean up any resources used by the logger."""
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pass
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class WandBLogger(MetricsLogger):
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"""Weights & Biases implementation of metrics logging.
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Args:
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project: Name of the W&B project.
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config: Configuration dictionary to log.
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rank: Process rank in distributed training.
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"""
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def __init__(self, project: str, config: Dict[str, Any], rank: int = 0):
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self.rank = rank
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if rank == 0:
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wandb.init(project=project, config=config)
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def log_metrics(self,
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metrics: Dict[str, Any],
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step: Optional[int] = None) -> None:
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"""Log metrics to W&B if on the main process.
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Args:
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metrics: Dictionary of metrics to log.
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step: Current training step or epoch.
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"""
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if self.rank == 0:
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wandb.log(metrics, step=step)
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def close(self) -> None:
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"""Finish the W&B run if on the main process."""
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if self.rank == 0:
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wandb.finish()
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class DistributedManager:
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"""Manages distributed training setup and cleanup.
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Args:
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world_size: Total number of processes.
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rank: Process rank.
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master_addr: Address of the master process.
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master_port: Port for distributed communication.
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backend: PyTorch distributed backend to use.
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"""
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def __init__(
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self,
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world_size: int,
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rank: int,
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master_addr: str = "localhost",
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master_port: str = "12358",
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backend: str = "nccl",
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):
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self.world_size = world_size
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self.rank = rank
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self.master_addr = master_addr
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self.master_port = master_port
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self.backend = backend
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def setup(self) -> None:
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"""Initialize the distributed environment."""
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os.environ["MASTER_ADDR"] = self.master_addr
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os.environ["MASTER_PORT"] = self.master_port
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if not dist.is_initialized():
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dist.init_process_group(backend=self.backend,
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world_size=self.world_size,
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rank=self.rank)
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def cleanup(self) -> None:
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"""Clean up the distributed environment."""
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if dist.is_initialized():
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dist.destroy_process_group()
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@dataclass
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class FinetuningConfig:
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"""Configuration for model training.
|
||||
|
||||
Args:
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batch_size: Number of samples per batch.
|
||||
num_epochs: Number of training epochs.
|
||||
learning_rate: Initial learning rate.
|
||||
weight_decay: L2 regularization factor.
|
||||
freq_type: Frequency, can be [0, 1, 2].
|
||||
use_quantile_loss: bool = False # Flag to enable/disable quantile loss
|
||||
quantiles: List[float] = field(default_factory=lambda: [0.1, 0.5, 0.9])
|
||||
device: Device to train on ('cuda' or 'cpu').
|
||||
distributed: Whether to use distributed training.
|
||||
gpu_ids: List of GPU IDs to use.
|
||||
master_port: Port for distributed training.
|
||||
master_addr: Address for distributed training.
|
||||
use_wandb: Whether to use Weights & Biases logging.
|
||||
wandb_project: W&B project name.
|
||||
log_every_n_steps: Log metrics every N steps (batches), this is inspired from Pytorch Lightning
|
||||
val_check_interval: How often within one training epoch to check val metrics. (also from Pytorch Lightning)
|
||||
Can be: float (0.0-1.0): fraction of epoch (e.g., 0.5 = validate twice per epoch)
|
||||
int: validate every N batches
|
||||
"""
|
||||
|
||||
batch_size: int = 32
|
||||
num_epochs: int = 20
|
||||
learning_rate: float = 1e-4
|
||||
weight_decay: float = 0.01
|
||||
freq_type: int = 0
|
||||
use_quantile_loss: bool = False
|
||||
device: str = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
distributed: bool = False
|
||||
gpu_ids: List[int] = field(default_factory=lambda: [0])
|
||||
master_port: str = "12358"
|
||||
master_addr: str = "localhost"
|
||||
use_wandb: bool = False
|
||||
wandb_project: str = "timesfm-finetuning"
|
||||
log_every_n_steps: int = 50
|
||||
val_check_interval: float = 0.5
|
||||
|
||||
|
||||
class TimesFMFinetuner:
|
||||
"""Handles model training and validation.
|
||||
|
||||
Args:
|
||||
model: PyTorch model to train.
|
||||
config: Training configuration.
|
||||
rank: Process rank for distributed training.
|
||||
loss_fn: Loss function (defaults to MSE).
|
||||
logger: Optional logging.Logger instance.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: nn.Module,
|
||||
config: FinetuningConfig,
|
||||
rank: int = 0,
|
||||
loss_fn: Optional[Callable] = None,
|
||||
logger: Optional[logging.Logger] = None,
|
||||
):
|
||||
self.model = model
|
||||
self.config = config
|
||||
self.rank = rank
|
||||
self.logger = logger or logging.getLogger(__name__)
|
||||
self.device = torch.device(
|
||||
f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
|
||||
self.loss_fn = loss_fn or (lambda x, y: torch.mean((x - y.squeeze(-1))**2))
|
||||
|
||||
if config.use_wandb:
|
||||
self.metrics_logger = WandBLogger(config.wandb_project, config.__dict__,
|
||||
rank)
|
||||
|
||||
if config.distributed:
|
||||
self.dist_manager = DistributedManager(
|
||||
world_size=len(config.gpu_ids),
|
||||
rank=rank,
|
||||
master_addr=config.master_addr,
|
||||
master_port=config.master_port,
|
||||
)
|
||||
self.dist_manager.setup()
|
||||
self.model = self._setup_distributed_model()
|
||||
|
||||
def _setup_distributed_model(self) -> nn.Module:
|
||||
"""Configure model for distributed training."""
|
||||
self.model = self.model.to(self.device)
|
||||
return DDP(self.model,
|
||||
device_ids=[self.config.gpu_ids[self.rank]],
|
||||
output_device=self.config.gpu_ids[self.rank])
|
||||
|
||||
def _create_dataloader(self, dataset: Dataset, is_train: bool) -> DataLoader:
|
||||
"""Create appropriate DataLoader based on training configuration.
|
||||
|
||||
Args:
|
||||
dataset: Dataset to create loader for.
|
||||
is_train: Whether this is for training (affects shuffling).
|
||||
|
||||
Returns:
|
||||
DataLoader instance.
|
||||
"""
|
||||
if self.config.distributed:
|
||||
sampler = torch.utils.data.distributed.DistributedSampler(
|
||||
dataset,
|
||||
num_replicas=len(self.config.gpu_ids),
|
||||
rank=dist.get_rank(),
|
||||
shuffle=is_train)
|
||||
else:
|
||||
sampler = None
|
||||
|
||||
return DataLoader(
|
||||
dataset,
|
||||
batch_size=self.config.batch_size,
|
||||
shuffle=(is_train and not self.config.distributed),
|
||||
sampler=sampler,
|
||||
)
|
||||
|
||||
def _quantile_loss(self, pred: torch.Tensor, actual: torch.Tensor,
|
||||
quantile: float) -> torch.Tensor:
|
||||
"""Calculates quantile loss.
|
||||
Args:
|
||||
pred: Predicted values
|
||||
actual: Actual values
|
||||
quantile: Quantile at which loss is computed
|
||||
Returns:
|
||||
Quantile loss
|
||||
"""
|
||||
dev = actual - pred
|
||||
loss_first = dev * quantile
|
||||
loss_second = -dev * (1.0 - quantile)
|
||||
return 2 * torch.where(loss_first >= 0, loss_first, loss_second)
|
||||
|
||||
def _process_batch(self, batch: List[torch.Tensor]) -> tuple:
|
||||
"""Process a single batch of data.
|
||||
|
||||
Args:
|
||||
batch: List of input tensors.
|
||||
|
||||
Returns:
|
||||
Tuple of (loss, predictions).
|
||||
"""
|
||||
x_context, x_padding, freq, x_future = [
|
||||
t.to(self.device, non_blocking=True) for t in batch
|
||||
]
|
||||
|
||||
predictions = self.model(x_context, x_padding.float(), freq)
|
||||
predictions_mean = predictions[..., 0]
|
||||
last_patch_pred = predictions_mean[:, -1, :]
|
||||
|
||||
loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))
|
||||
if self.config.use_quantile_loss:
|
||||
quantiles = self.config.quantiles or DEFAULT_QUANTILES
|
||||
for i, quantile in enumerate(quantiles):
|
||||
last_patch_quantile = predictions[:, -1, :, i + 1]
|
||||
loss += torch.mean(
|
||||
self._quantile_loss(last_patch_quantile, x_future.squeeze(-1),
|
||||
quantile))
|
||||
|
||||
return loss, predictions
|
||||
|
||||
def _train_epoch(self, train_loader: DataLoader,
|
||||
optimizer: torch.optim.Optimizer) -> float:
|
||||
"""Train for one epoch in a distributed setting.
|
||||
|
||||
Args:
|
||||
train_loader: DataLoader for training data.
|
||||
optimizer: Optimizer instance.
|
||||
|
||||
Returns:
|
||||
Average training loss for the epoch.
|
||||
"""
|
||||
self.model.train()
|
||||
total_loss = 0.0
|
||||
num_batches = len(train_loader)
|
||||
|
||||
for batch in train_loader:
|
||||
loss, _ = self._process_batch(batch)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
total_loss += loss.item()
|
||||
|
||||
avg_loss = total_loss / num_batches
|
||||
|
||||
if self.config.distributed:
|
||||
avg_loss_tensor = torch.tensor(avg_loss, device=self.device)
|
||||
dist.all_reduce(avg_loss_tensor, op=dist.ReduceOp.SUM)
|
||||
avg_loss = (avg_loss_tensor / dist.get_world_size()).item()
|
||||
|
||||
return avg_loss
|
||||
|
||||
def _validate(self, val_loader: DataLoader) -> float:
|
||||
"""Perform validation.
|
||||
|
||||
Args:
|
||||
val_loader: DataLoader for validation data.
|
||||
|
||||
Returns:
|
||||
Average validation loss.
|
||||
"""
|
||||
self.model.eval()
|
||||
total_loss = 0.0
|
||||
num_batches = len(val_loader)
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in val_loader:
|
||||
loss, _ = self._process_batch(batch)
|
||||
total_loss += loss.item()
|
||||
|
||||
avg_loss = total_loss / num_batches
|
||||
|
||||
if self.config.distributed:
|
||||
avg_loss_tensor = torch.tensor(avg_loss, device=self.device)
|
||||
dist.all_reduce(avg_loss_tensor, op=dist.ReduceOp.SUM)
|
||||
avg_loss = (avg_loss_tensor / dist.get_world_size()).item()
|
||||
|
||||
return avg_loss
|
||||
|
||||
def finetune(self, train_dataset: Dataset,
|
||||
val_dataset: Dataset) -> Dict[str, Any]:
|
||||
"""Train the model.
|
||||
|
||||
Args:
|
||||
train_dataset: Training dataset.
|
||||
val_dataset: Validation dataset.
|
||||
|
||||
Returns:
|
||||
Dictionary containing training history.
|
||||
"""
|
||||
self.model = self.model.to(self.device)
|
||||
train_loader = self._create_dataloader(train_dataset, is_train=True)
|
||||
val_loader = self._create_dataloader(val_dataset, is_train=False)
|
||||
|
||||
optimizer = torch.optim.Adam(self.model.parameters(),
|
||||
lr=self.config.learning_rate,
|
||||
weight_decay=self.config.weight_decay)
|
||||
|
||||
history = {"train_loss": [], "val_loss": [], "learning_rate": []}
|
||||
|
||||
self.logger.info(
|
||||
f"Starting training for {self.config.num_epochs} epochs...")
|
||||
self.logger.info(f"Training samples: {len(train_dataset)}")
|
||||
self.logger.info(f"Validation samples: {len(val_dataset)}")
|
||||
|
||||
try:
|
||||
for epoch in range(self.config.num_epochs):
|
||||
train_loss = self._train_epoch(train_loader, optimizer)
|
||||
val_loss = self._validate(val_loader)
|
||||
current_lr = optimizer.param_groups[0]["lr"]
|
||||
|
||||
metrics = {
|
||||
"train_loss": train_loss,
|
||||
"val_loss": val_loss,
|
||||
"learning_rate": current_lr,
|
||||
"epoch": epoch + 1,
|
||||
}
|
||||
|
||||
if self.config.use_wandb:
|
||||
self.metrics_logger.log_metrics(metrics)
|
||||
|
||||
history["train_loss"].append(train_loss)
|
||||
history["val_loss"].append(val_loss)
|
||||
history["learning_rate"].append(current_lr)
|
||||
|
||||
if self.rank == 0:
|
||||
self.logger.info(
|
||||
f"[Epoch {epoch+1}] Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}"
|
||||
)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
self.logger.info("Training interrupted by user")
|
||||
|
||||
if self.config.distributed:
|
||||
self.dist_manager.cleanup()
|
||||
|
||||
if self.config.use_wandb:
|
||||
self.metrics_logger.close()
|
||||
|
||||
return {"history": history}
|
||||
@@ -1,340 +0,0 @@
|
||||
"""
|
||||
TimesFM Finetuner: A flexible framework for finetuning TimesFM models on custom datasets.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.utils.data import DataLoader, Dataset
|
||||
|
||||
import wandb
|
||||
|
||||
|
||||
class MetricsLogger(ABC):
|
||||
"""Abstract base class for logging metrics during training.
|
||||
|
||||
This class defines the interface for logging metrics during model training.
|
||||
Concrete implementations can log to different backends (e.g., WandB, TensorBoard).
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def log_metrics(self, metrics: Dict[str, Any], step: Optional[int] = None) -> None:
|
||||
"""Log metrics to the specified backend.
|
||||
|
||||
Args:
|
||||
metrics: Dictionary containing metric names and values.
|
||||
step: Optional step number or epoch for the metrics.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def close(self) -> None:
|
||||
"""Clean up any resources used by the logger."""
|
||||
pass
|
||||
|
||||
|
||||
class WandBLogger(MetricsLogger):
|
||||
"""Weights & Biases implementation of metrics logging.
|
||||
|
||||
Args:
|
||||
project: Name of the W&B project.
|
||||
config: Configuration dictionary to log.
|
||||
rank: Process rank in distributed training.
|
||||
"""
|
||||
|
||||
def __init__(self, project: str, config: Dict[str, Any], rank: int = 0):
|
||||
self.rank = rank
|
||||
if rank == 0:
|
||||
wandb.init(project=project, config=config)
|
||||
|
||||
def log_metrics(self, metrics: Dict[str, Any], step: Optional[int] = None) -> None:
|
||||
"""Log metrics to W&B if on the main process.
|
||||
|
||||
Args:
|
||||
metrics: Dictionary of metrics to log.
|
||||
step: Current training step or epoch.
|
||||
"""
|
||||
if self.rank == 0:
|
||||
wandb.log(metrics, step=step)
|
||||
|
||||
def close(self) -> None:
|
||||
"""Finish the W&B run if on the main process."""
|
||||
if self.rank == 0:
|
||||
wandb.finish()
|
||||
|
||||
|
||||
class DistributedManager:
|
||||
"""Manages distributed training setup and cleanup.
|
||||
|
||||
Args:
|
||||
world_size: Total number of processes.
|
||||
rank: Process rank.
|
||||
master_addr: Address of the master process.
|
||||
master_port: Port for distributed communication.
|
||||
backend: PyTorch distributed backend to use.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
world_size: int,
|
||||
rank: int,
|
||||
master_addr: str = "localhost",
|
||||
master_port: str = "12358",
|
||||
backend: str = "nccl",
|
||||
):
|
||||
self.world_size = world_size
|
||||
self.rank = rank
|
||||
self.master_addr = master_addr
|
||||
self.master_port = master_port
|
||||
self.backend = backend
|
||||
|
||||
def setup(self) -> None:
|
||||
"""Initialize the distributed environment."""
|
||||
os.environ["MASTER_ADDR"] = self.master_addr
|
||||
os.environ["MASTER_PORT"] = self.master_port
|
||||
|
||||
if not dist.is_initialized():
|
||||
dist.init_process_group(backend=self.backend, world_size=self.world_size, rank=self.rank)
|
||||
|
||||
def cleanup(self) -> None:
|
||||
"""Clean up the distributed environment."""
|
||||
if dist.is_initialized():
|
||||
dist.destroy_process_group()
|
||||
|
||||
|
||||
@dataclass
|
||||
class FinetuningConfig:
|
||||
"""Configuration for model training.
|
||||
|
||||
Args:
|
||||
batch_size: Number of samples per batch.
|
||||
num_epochs: Number of training epochs.
|
||||
learning_rate: Initial learning rate.
|
||||
weight_decay: L2 regularization factor.
|
||||
device: Device to train on ('cuda' or 'cpu').
|
||||
distributed: Whether to use distributed training.
|
||||
gpu_ids: List of GPU IDs to use.
|
||||
master_port: Port for distributed training.
|
||||
master_addr: Address for distributed training.
|
||||
use_wandb: Whether to use Weights & Biases logging.
|
||||
wandb_project: W&B project name.
|
||||
"""
|
||||
|
||||
batch_size: int = 32
|
||||
num_epochs: int = 20
|
||||
learning_rate: float = 1e-4
|
||||
weight_decay: float = 0.01
|
||||
device: str = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
distributed: bool = False
|
||||
gpu_ids: List[int] = field(default_factory=lambda: [0])
|
||||
master_port: str = "12358"
|
||||
master_addr: str = "localhost"
|
||||
use_wandb: bool = False
|
||||
wandb_project: str = "timesfm-finetuning"
|
||||
|
||||
|
||||
class TimesFMFinetuner:
|
||||
"""Handles model training and validation.
|
||||
|
||||
Args:
|
||||
model: PyTorch model to train.
|
||||
config: Training configuration.
|
||||
rank: Process rank for distributed training.
|
||||
loss_fn: Loss function (defaults to MSE).
|
||||
logger: Optional logging.Logger instance.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: nn.Module,
|
||||
config: FinetuningConfig,
|
||||
rank: int = 0,
|
||||
loss_fn: Optional[Callable] = None,
|
||||
logger: Optional[logging.Logger] = None,
|
||||
):
|
||||
self.model = model
|
||||
self.config = config
|
||||
self.rank = rank
|
||||
self.logger = logger or logging.getLogger(__name__)
|
||||
self.device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
|
||||
self.loss_fn = loss_fn or (lambda x, y: torch.mean((x - y.squeeze(-1)) ** 2))
|
||||
|
||||
if config.use_wandb:
|
||||
self.metrics_logger = WandBLogger(config.wandb_project, config.__dict__, rank)
|
||||
|
||||
if config.distributed:
|
||||
self.dist_manager = DistributedManager(
|
||||
world_size=len(config.gpu_ids),
|
||||
rank=rank,
|
||||
master_addr=config.master_addr,
|
||||
master_port=config.master_port,
|
||||
)
|
||||
self.dist_manager.setup()
|
||||
self.model = self._setup_distributed_model()
|
||||
|
||||
def _setup_distributed_model(self) -> nn.Module:
|
||||
"""Configure model for distributed training."""
|
||||
self.model = self.model.to(self.device)
|
||||
return DDP(
|
||||
self.model, device_ids=[self.config.gpu_ids[self.rank]], output_device=self.config.gpu_ids[self.rank]
|
||||
)
|
||||
|
||||
def _create_dataloader(self, dataset: Dataset, is_train: bool) -> DataLoader:
|
||||
"""Create appropriate DataLoader based on training configuration.
|
||||
|
||||
Args:
|
||||
dataset: Dataset to create loader for.
|
||||
is_train: Whether this is for training (affects shuffling).
|
||||
|
||||
Returns:
|
||||
DataLoader instance.
|
||||
"""
|
||||
if self.config.distributed:
|
||||
sampler = torch.utils.data.distributed.DistributedSampler(
|
||||
dataset, num_replicas=len(self.config.gpu_ids), rank=dist.get_rank(), shuffle=is_train
|
||||
)
|
||||
else:
|
||||
sampler = None
|
||||
|
||||
return DataLoader(
|
||||
dataset,
|
||||
batch_size=self.config.batch_size,
|
||||
shuffle=(is_train and not self.config.distributed),
|
||||
sampler=sampler,
|
||||
)
|
||||
|
||||
def _process_batch(self, batch: List[torch.Tensor]) -> tuple:
|
||||
"""Process a single batch of data.
|
||||
|
||||
Args:
|
||||
batch: List of input tensors.
|
||||
|
||||
Returns:
|
||||
Tuple of (loss, predictions).
|
||||
"""
|
||||
x_context, x_padding, freq, x_future = [t.to(self.device, non_blocking=True) for t in batch]
|
||||
|
||||
predictions = self.model(x_context, x_padding.float(), freq)
|
||||
predictions_mean = predictions[..., 0]
|
||||
last_patch_pred = predictions_mean[:, -1, :]
|
||||
|
||||
loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))
|
||||
|
||||
return loss, predictions
|
||||
|
||||
def _train_epoch(self, train_loader: DataLoader, optimizer: torch.optim.Optimizer) -> float:
|
||||
"""Train for one epoch.
|
||||
|
||||
Args:
|
||||
train_loader: DataLoader for training data.
|
||||
optimizer: Optimizer instance.
|
||||
|
||||
Returns:
|
||||
Average training loss for the epoch.
|
||||
"""
|
||||
self.model.train()
|
||||
total_loss = 0.0
|
||||
|
||||
for batch in train_loader:
|
||||
loss, _ = self._process_batch(batch)
|
||||
|
||||
if self.config.distributed:
|
||||
losses = [torch.zeros_like(loss) for _ in range(dist.get_world_size())]
|
||||
dist.all_gather(losses, loss)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
total_loss += loss.item()
|
||||
|
||||
return total_loss / len(train_loader)
|
||||
|
||||
def _validate(self, val_loader: DataLoader) -> float:
|
||||
"""Perform validation.
|
||||
|
||||
Args:
|
||||
val_loader: DataLoader for validation data.
|
||||
|
||||
Returns:
|
||||
Average validation loss.
|
||||
"""
|
||||
self.model.eval()
|
||||
total_loss = 0.0
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in val_loader:
|
||||
loss, _ = self._process_batch(batch)
|
||||
|
||||
if self.config.distributed:
|
||||
losses = [torch.zeros_like(loss) for _ in range(dist.get_world_size())]
|
||||
dist.all_gather(losses, loss)
|
||||
|
||||
total_loss += loss.item()
|
||||
|
||||
return total_loss / len(val_loader)
|
||||
|
||||
def finetune(self, train_dataset: Dataset, val_dataset: Dataset) -> Dict[str, Any]:
|
||||
"""Train the model.
|
||||
|
||||
Args:
|
||||
train_dataset: Training dataset.
|
||||
val_dataset: Validation dataset.
|
||||
|
||||
Returns:
|
||||
Dictionary containing training history.
|
||||
"""
|
||||
self.model = self.model.to(self.device)
|
||||
train_loader = self._create_dataloader(train_dataset, is_train=True)
|
||||
val_loader = self._create_dataloader(val_dataset, is_train=False)
|
||||
|
||||
optimizer = torch.optim.Adam(
|
||||
self.model.parameters(), lr=self.config.learning_rate, weight_decay=self.config.weight_decay
|
||||
)
|
||||
|
||||
history = {"train_loss": [], "val_loss": [], "learning_rate": []}
|
||||
|
||||
self.logger.info(f"Starting training for {self.config.num_epochs} epochs...")
|
||||
self.logger.info(f"Training samples: {len(train_dataset)}")
|
||||
self.logger.info(f"Validation samples: {len(val_dataset)}")
|
||||
|
||||
try:
|
||||
for epoch in range(self.config.num_epochs):
|
||||
train_loss = self._train_epoch(train_loader, optimizer)
|
||||
val_loss = self._validate(val_loader)
|
||||
current_lr = optimizer.param_groups[0]["lr"]
|
||||
|
||||
metrics = {
|
||||
"train_loss": train_loss,
|
||||
"val_loss": val_loss,
|
||||
"learning_rate": current_lr,
|
||||
"epoch": epoch + 1,
|
||||
}
|
||||
|
||||
if self.config.use_wandb:
|
||||
self.metrics_logger.log_metrics(metrics)
|
||||
|
||||
history["train_loss"].append(train_loss)
|
||||
history["val_loss"].append(val_loss)
|
||||
history["learning_rate"].append(current_lr)
|
||||
|
||||
if self.rank == 0:
|
||||
self.logger.info(f"[Epoch {epoch+1}] Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
self.logger.info("Training interrupted by user")
|
||||
|
||||
if self.config.distributed:
|
||||
self.dist_manager.cleanup()
|
||||
|
||||
if self.config.use_wandb:
|
||||
self.metrics_logger.close()
|
||||
|
||||
return {"history": history}
|
||||
Reference in New Issue
Block a user