PR Feedback
This commit is contained in:
misha-chertushkin
@@ -0,0 +1,388 @@
|
||||
"""
|
||||
Example usage of the TimesFM Finetuning Framework.
|
||||
|
||||
For single GPU:
|
||||
python script.py --training_mode=single
|
||||
|
||||
For multiple GPUs:
|
||||
python script.py --training_mode=multi --gpu_ids=0,1,2
|
||||
"""
|
||||
|
||||
import os
|
||||
from os import path
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
import torch.multiprocessing as mp
|
||||
import yfinance as yf
|
||||
from absl import app, flags
|
||||
from huggingface_hub import snapshot_download
|
||||
from torch.utils.data import Dataset
|
||||
|
||||
from finetuning.finetuning_torch import FinetuningConfig, TimesFMFinetuner
|
||||
from timesfm import TimesFm, TimesFmCheckpoint, TimesFmHparams
|
||||
from timesfm.pytorch_patched_decoder import PatchedTimeSeriesDecoder
|
||||
|
||||
FLAGS = flags.FLAGS
|
||||
|
||||
flags.DEFINE_enum(
|
||||
"training_mode",
|
||||
"single",
|
||||
["single", "multi"],
|
||||
'Training mode: "single" for single-GPU or "multi" for multi-GPU training.',
|
||||
)
|
||||
|
||||
flags.DEFINE_list(
|
||||
"gpu_ids", ["0"],
|
||||
"Comma-separated list of GPU IDs to use for multi-GPU training. Example: 0,1,2"
|
||||
)
|
||||
|
||||
|
||||
class TimeSeriesDataset(Dataset):
|
||||
"""Dataset for time series data compatible with TimesFM."""
|
||||
|
||||
def __init__(self,
|
||||
series: np.ndarray,
|
||||
context_length: int,
|
||||
horizon_length: int,
|
||||
freq_type: int = 0):
|
||||
"""
|
||||
Initialize dataset.
|
||||
|
||||
Args:
|
||||
series: Time series data
|
||||
context_length: Number of past timesteps to use as input
|
||||
horizon_length: Number of future timesteps to predict
|
||||
freq_type: Frequency type (0, 1, or 2)
|
||||
"""
|
||||
if freq_type not in [0, 1, 2]:
|
||||
raise ValueError("freq_type must be 0, 1, or 2")
|
||||
|
||||
self.series = series
|
||||
self.context_length = context_length
|
||||
self.horizon_length = horizon_length
|
||||
self.freq_type = freq_type
|
||||
self._prepare_samples()
|
||||
|
||||
def _prepare_samples(self) -> None:
|
||||
"""Prepare sliding window samples from the time series."""
|
||||
self.samples = []
|
||||
total_length = self.context_length + self.horizon_length
|
||||
|
||||
for start_idx in range(0, len(self.series) - total_length + 1):
|
||||
end_idx = start_idx + self.context_length
|
||||
x_context = self.series[start_idx:end_idx]
|
||||
x_future = self.series[end_idx:end_idx + self.horizon_length]
|
||||
self.samples.append((x_context, x_future))
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.samples)
|
||||
|
||||
def __getitem__(
|
||||
self, index: int
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
x_context, x_future = self.samples[index]
|
||||
|
||||
x_context = torch.tensor(x_context, dtype=torch.float32)
|
||||
x_future = torch.tensor(x_future, dtype=torch.float32)
|
||||
|
||||
input_padding = torch.zeros_like(x_context)
|
||||
freq = torch.tensor([self.freq_type], dtype=torch.long)
|
||||
|
||||
return x_context, input_padding, freq, x_future
|
||||
|
||||
|
||||
def prepare_datasets(series: np.ndarray,
|
||||
context_length: int,
|
||||
horizon_length: int,
|
||||
freq_type: int = 0,
|
||||
train_split: float = 0.8) -> Tuple[Dataset, Dataset]:
|
||||
"""
|
||||
Prepare training and validation datasets from time series data.
|
||||
|
||||
Args:
|
||||
series: Input time series data
|
||||
context_length: Number of past timesteps to use
|
||||
horizon_length: Number of future timesteps to predict
|
||||
freq_type: Frequency type (0, 1, or 2)
|
||||
train_split: Fraction of data to use for training
|
||||
|
||||
Returns:
|
||||
Tuple of (train_dataset, val_dataset)
|
||||
"""
|
||||
train_size = int(len(series) * train_split)
|
||||
train_data = series[:train_size]
|
||||
val_data = series[train_size:]
|
||||
|
||||
# Create datasets with specified frequency type
|
||||
train_dataset = TimeSeriesDataset(train_data,
|
||||
context_length=context_length,
|
||||
horizon_length=horizon_length,
|
||||
freq_type=freq_type)
|
||||
|
||||
val_dataset = TimeSeriesDataset(val_data,
|
||||
context_length=context_length,
|
||||
horizon_length=horizon_length,
|
||||
freq_type=freq_type)
|
||||
|
||||
return train_dataset, val_dataset
|
||||
|
||||
|
||||
def get_model(load_weights: bool = False):
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
repo_id = "google/timesfm-2.0-500m-pytorch"
|
||||
hparams = TimesFmHparams(
|
||||
backend=device,
|
||||
per_core_batch_size=32,
|
||||
horizon_len=128,
|
||||
num_layers=50,
|
||||
use_positional_embedding=False,
|
||||
context_len=
|
||||
192, # Context length can be anything up to 2048 in multiples of 32
|
||||
)
|
||||
tfm = TimesFm(hparams=hparams,
|
||||
checkpoint=TimesFmCheckpoint(huggingface_repo_id=repo_id))
|
||||
|
||||
model = PatchedTimeSeriesDecoder(tfm._model_config)
|
||||
if load_weights:
|
||||
checkpoint_path = path.join(snapshot_download(repo_id), "torch_model.ckpt")
|
||||
loaded_checkpoint = torch.load(checkpoint_path, weights_only=True)
|
||||
model.load_state_dict(loaded_checkpoint)
|
||||
return model, hparams, tfm._model_config
|
||||
|
||||
|
||||
def plot_predictions(
|
||||
model: TimesFm,
|
||||
val_dataset: Dataset,
|
||||
save_path: Optional[str] = "predictions.png",
|
||||
) -> None:
|
||||
"""
|
||||
Plot model predictions against ground truth for a batch of validation data.
|
||||
|
||||
Args:
|
||||
model: Trained TimesFM model
|
||||
val_dataset: Validation dataset
|
||||
save_path: Path to save the plot
|
||||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
model.eval()
|
||||
|
||||
x_context, x_padding, freq, x_future = val_dataset[0]
|
||||
x_context = x_context.unsqueeze(0) # Add batch dimension
|
||||
x_padding = x_padding.unsqueeze(0)
|
||||
freq = freq.unsqueeze(0)
|
||||
x_future = x_future.unsqueeze(0)
|
||||
|
||||
device = next(model.parameters()).device
|
||||
x_context = x_context.to(device)
|
||||
x_padding = x_padding.to(device)
|
||||
freq = freq.to(device)
|
||||
x_future = x_future.to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
predictions = model(x_context, x_padding.float(), freq)
|
||||
predictions_mean = predictions[..., 0] # [B, N, horizon_len]
|
||||
last_patch_pred = predictions_mean[:, -1, :] # [B, horizon_len]
|
||||
|
||||
context_vals = x_context[0].cpu().numpy()
|
||||
future_vals = x_future[0].cpu().numpy()
|
||||
pred_vals = last_patch_pred[0].cpu().numpy()
|
||||
|
||||
context_len = len(context_vals)
|
||||
horizon_len = len(future_vals)
|
||||
|
||||
plt.figure(figsize=(12, 6))
|
||||
|
||||
plt.plot(range(context_len),
|
||||
context_vals,
|
||||
label="Historical Data",
|
||||
color="blue",
|
||||
linewidth=2)
|
||||
|
||||
plt.plot(
|
||||
range(context_len, context_len + horizon_len),
|
||||
future_vals,
|
||||
label="Ground Truth",
|
||||
color="green",
|
||||
linestyle="--",
|
||||
linewidth=2,
|
||||
)
|
||||
|
||||
plt.plot(range(context_len, context_len + horizon_len),
|
||||
pred_vals,
|
||||
label="Prediction",
|
||||
color="red",
|
||||
linewidth=2)
|
||||
|
||||
plt.xlabel("Time Step")
|
||||
plt.ylabel("Value")
|
||||
plt.title("TimesFM Predictions vs Ground Truth")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path)
|
||||
print(f"Plot saved to {save_path}")
|
||||
|
||||
plt.close()
|
||||
|
||||
|
||||
def get_data(context_len: int,
|
||||
horizon_len: int,
|
||||
freq_type: int = 0) -> Tuple[Dataset, Dataset]:
|
||||
df = yf.download("AAPL", start="2010-01-01", end="2019-01-01")
|
||||
time_series = df["Close"].values
|
||||
|
||||
train_dataset, val_dataset = prepare_datasets(
|
||||
series=time_series,
|
||||
context_length=context_len,
|
||||
horizon_length=horizon_len,
|
||||
freq_type=freq_type,
|
||||
train_split=0.8,
|
||||
)
|
||||
|
||||
print(f"Created datasets:")
|
||||
print(f"- Training samples: {len(train_dataset)}")
|
||||
print(f"- Validation samples: {len(val_dataset)}")
|
||||
print(f"- Using frequency type: {freq_type}")
|
||||
return train_dataset, val_dataset
|
||||
|
||||
|
||||
def single_gpu_example():
|
||||
"""Basic example of finetuning TimesFM on stock data."""
|
||||
model, hparams, tfm_config = get_model(load_weights=True)
|
||||
config = FinetuningConfig(batch_size=256,
|
||||
num_epochs=5,
|
||||
learning_rate=1e-4,
|
||||
use_wandb=True,
|
||||
freq_type=1,
|
||||
log_every_n_steps=10,
|
||||
val_check_interval=0.5,
|
||||
use_quantile_loss=True)
|
||||
|
||||
train_dataset, val_dataset = get_data(128,
|
||||
tfm_config.horizon_len,
|
||||
freq_type=config.freq_type)
|
||||
finetuner = TimesFMFinetuner(model, config)
|
||||
|
||||
print("\nStarting finetuning...")
|
||||
results = finetuner.finetune(train_dataset=train_dataset,
|
||||
val_dataset=val_dataset)
|
||||
|
||||
print("\nFinetuning completed!")
|
||||
print(f"Training history: {len(results['history']['train_loss'])} epochs")
|
||||
|
||||
plot_predictions(
|
||||
model=model,
|
||||
val_dataset=val_dataset,
|
||||
save_path="timesfm_predictions.png",
|
||||
)
|
||||
|
||||
|
||||
def setup_process(rank, world_size, model, config, train_dataset, val_dataset,
|
||||
return_dict):
|
||||
"""Setup process function with optimized CUDA handling."""
|
||||
try:
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
os.environ["MASTER_ADDR"] = config.master_addr
|
||||
os.environ["MASTER_PORT"] = config.master_port
|
||||
if not torch.distributed.is_initialized():
|
||||
torch.distributed.init_process_group(backend="nccl",
|
||||
world_size=world_size,
|
||||
rank=rank)
|
||||
|
||||
finetuner = TimesFMFinetuner(model, config, rank=rank)
|
||||
|
||||
results = finetuner.finetune(train_dataset=train_dataset,
|
||||
val_dataset=val_dataset)
|
||||
|
||||
if rank == 0:
|
||||
return_dict["results"] = results
|
||||
plot_predictions(
|
||||
model=model,
|
||||
val_dataset=val_dataset,
|
||||
save_path="timesfm_predictions.png",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in process {rank}: {str(e)}")
|
||||
raise e
|
||||
finally:
|
||||
if torch.distributed.is_initialized():
|
||||
torch.distributed.destroy_process_group()
|
||||
|
||||
|
||||
def multi_gpu_example():
|
||||
"""Example of finetuning TimesFM using multiple GPUs with optimized spawn."""
|
||||
mp.set_start_method("spawn", force=True)
|
||||
|
||||
gpu_ids = [0, 1]
|
||||
world_size = len(gpu_ids)
|
||||
|
||||
model, hparams, tfm_config = get_model(load_weights=True)
|
||||
|
||||
# Create config
|
||||
config = FinetuningConfig(
|
||||
batch_size=256,
|
||||
num_epochs=5,
|
||||
learning_rate=3e-5,
|
||||
use_wandb=True,
|
||||
distributed=True,
|
||||
gpu_ids=gpu_ids,
|
||||
log_every_n_steps=50,
|
||||
val_check_interval=0.5,
|
||||
)
|
||||
train_dataset, val_dataset = get_data(128, tfm_config.horizon_len)
|
||||
manager = mp.Manager()
|
||||
return_dict = manager.dict()
|
||||
|
||||
# Launch processes
|
||||
mp.spawn(
|
||||
setup_process,
|
||||
args=(world_size, model, config, train_dataset, val_dataset, return_dict),
|
||||
nprocs=world_size,
|
||||
join=True,
|
||||
)
|
||||
|
||||
results = return_dict.get("results", None)
|
||||
print("\nFinetuning completed!")
|
||||
return results
|
||||
|
||||
|
||||
def main(argv):
|
||||
"""Main function that selects and runs the appropriate training mode."""
|
||||
|
||||
try:
|
||||
if FLAGS.training_mode == "single":
|
||||
print("\nStarting single-GPU training...")
|
||||
single_gpu_example()
|
||||
else:
|
||||
gpu_ids = [int(id) for id in FLAGS.gpu_ids]
|
||||
print(f"\nStarting multi-GPU training using GPUs: {gpu_ids}...")
|
||||
|
||||
config = FinetuningConfig(
|
||||
batch_size=256,
|
||||
num_epochs=5,
|
||||
learning_rate=3e-5,
|
||||
use_wandb=True,
|
||||
distributed=True,
|
||||
gpu_ids=gpu_ids,
|
||||
)
|
||||
|
||||
results = multi_gpu_example(config)
|
||||
print("\nMulti-GPU training completed!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Training failed: {str(e)}")
|
||||
finally:
|
||||
if torch.distributed.is_initialized():
|
||||
torch.distributed.destroy_process_group()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(main)
|
||||
Reference in New Issue
Block a user