training/fine-tuning clarification

[Mhdaw]

Hello, I want to train/fine-tune the chronos models, Is this a good approach?

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModelForSeq2SeqLM, AdamW
import numpy as np
from tqdm import tqdm
from chronos import ChronosConfig, MeanScaleUniformBins, ChronosModel

config = ChronosConfig(
    tokenizer_class="MeanScaleUniformBins",
    tokenizer_kwargs={"low_limit": -5.0, "high_limit": 10.0},  # Not sure about how these values should be!
    context_length=20,  
    prediction_length=10, 
    n_tokens=100,       # 4096
    n_special_tokens=2, # padding and eos?
    pad_token_id=0,
    eos_token_id=1,
    use_eos_token=False,
    model_type="seq2seq", 
    num_samples=10,
    temperature=1.0,
    top_k=50,
    top_p=0.95,
)
tokenizer = MeanScaleUniformBins(
    low_limit=config.tokenizer_kwargs["low_limit"],
    high_limit=config.tokenizer_kwargs["high_limit"],
    config=config
)
base_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny", device_map="auto")  # Or another T5 variant like amazon/chronos-t5-tiny

model = ChronosModel(config=config, model=base_model)

class TimeSeriesDataset(Dataset):
    def __init__(self, data, context_length, prediction_length):
        self.data = data
        self.context_length = context_length
        self.prediction_length = prediction_length

    def __len__(self):
        return len(self.data) - self.context_length - self.prediction_length + 1

    def __getitem__(self, idx):
        context = self.data[idx : idx + self.context_length]
        target = self.data[idx + self.context_length : idx + self.context_length + self.prediction_length]
        return torch.tensor(context), torch.tensor(target)

raw_data = df["target"].values
train_size = int(0.8 * len(raw_data))
train_data = raw_data[:train_size]
val_data = raw_data[train_size:]

train_dataset = TimeSeriesDataset(train_data, config.context_length, config.prediction_length)
val_dataset = TimeSeriesDataset(val_data, config.context_length, config.prediction_length)
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=32)

optimizer = AdamW(model.parameters(), lr=5e-5)

num_epochs = 5
for epoch in tqdm(range(num_epochs), desc="Epochs"):  
    model.train()
    total_loss = 0
    for batch in train_dataloader:
        context, target = batch
        context = context.to(model.device)
        target = target.to(model.device)

        tokenizer.boundaries = tokenizer.boundaries.to(model.device)  
        context_tokens, context_attention_mask, scale = tokenizer.context_input_transform(context)

        target_tokens, target_attention_mask = tokenizer.label_input_transform(target, scale)

        outputs = model.model(
            input_ids=context_tokens,
            attention_mask=context_attention_mask,
            labels=target_tokens,  
            decoder_attention_mask=target_attention_mask, 
        )

        loss = outputs.loss
        total_loss += loss.item()

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    avg_loss = total_loss / len(train_dataloader)
    tqdm.write(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {avg_loss:.4f}") 

    model.eval()
    val_loss = 0
    with torch.no_grad():
        for batch in val_dataloader:
            context, target = batch
            context = context.to(model.device)
            target = target.to(model.device)
            tokenizer.boundaries = tokenizer.boundaries.to(model.device)  
            context_tokens, context_attention_mask, scale = tokenizer.context_input_transform(context)
            target_tokens, target_attention_mask = tokenizer.label_input_transform(target, scale)
            outputs = model.model(
                input_ids=context_tokens,
                attention_mask=context_attention_mask,
                labels=target_tokens,
                decoder_attention_mask=target_attention_mask,
            )
            val_loss += outputs.loss.item()
    avg_val_loss = val_loss / len(val_dataloader)
    tqdm.write(f"Epoch {epoch+1}/{num_epochs}, Validation Loss: {avg_val_loss:.4f}")

model.model.save_pretrained("chronos-t5-timeseries")

is there any thing that I've missed? And any advice on the tokenizer_kwargs and n_special_tokens

[Killer3048]

@Mhdaw I think your approach is on the right track! Here are a few suggestions to enhance your training:

Training Tips

• Set use_eos_token=True for seq2seq models to signify the end of sequences.
• Optimize the learning rate (e.g., try 3e-5) and consider increasing epochs for better convergence.
• Incorporate additional validation metrics like MAE or RMSE to monitor performance.

[Mhdaw]

Thanks, I think the use_eos_token=True whould help with models performance.
adding metrics like MAE or RMSE requiers few steps, one that this model outputs are classes(in train phase) After that it outputs a probability distribution, maybe using the median and target for the metric calculation could be the answer.

[Mhdaw]

Also, If I want to use the Chronos Bolt models, What changes I need to make?

[Killer3048]

You can check how they implemented it themselves chronos-bolt.py

[Mhdaw]

@Killer3048
That is a bit different as the models inputs are not the same as in the chronos.py and this makes it a challenge.

I'm trying to see if I can do the same training process with the bolt models.

[Mhdaw]

Also, Not sure how to load the pre-trained model for fine-tuning!

[Mhdaw]

Hey @lostella @canerturkmen @huibinshen

is this approach for fine-tuning the chronos bolt model valid?

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModelForSeq2SeqLM, AdamW
import numpy as np
import pandas as pd
from tqdm import tqdm
from chronos import ChronosBoltConfig, BaseChronosPipeline

config = ChronosBoltConfig(
    context_length=20,
    prediction_length=10,
    input_patch_size=5,
    input_patch_stride=1,
    quantiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
    use_reg_token=False,
)

pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-tiny",
                                               device_map="auto",  # use "cpu" for CPU inference
                                               torch_dtype=torch.bfloat16,)

model = pipeline.model

class TimeSeriesDataset(Dataset):
    def __init__(self, data, context_length, prediction_length):
        self.data = data
        self.context_length = context_length
        self.prediction_length = prediction_length

    def __len__(self):
        return len(self.data) - self.context_length - self.prediction_length + 1

    def __getitem__(self, idx):
        context = self.data[idx : idx + self.context_length]
        target = self.data[idx + self.context_length : idx + self.context_length + self.prediction_length]
        return torch.tensor(context), torch.tensor(target)

raw_data =np.random.randn(5000)
test_data = raw_data[-20:]
raw_data[:-20]
train_size = int(0.8 * len(raw_data))
train_data = raw_data[:train_size]
val_data = raw_data[train_size:]

train_dataset = TimeSeriesDataset(train_data, config.context_length, config.prediction_length)
val_dataset = TimeSeriesDataset(val_data, config.context_length, config.prediction_length)

train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=128)

optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

# --- Training Loop ---
num_epochs = 3
train_losses = []
val_losses = []
for epoch in tqdm(range(num_epochs), desc="Epochs"):  # Wrap epoch loop with tqdm
    model.train()
    total_loss = 0
    for batch in train_dataloader:
        context, target = batch
        context = context.to(model.device)
        target = target.to(model.device)
        # Forward pass
        outputs = model(
            context=context,
            #mask=context_mask,
            target=target,
            #target_mask=target_mask,
        )

        loss = outputs.loss
        total_loss += loss.item()
        train_losses.append(loss.item())

        # Backward pass and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    avg_loss = total_loss / len(train_dataloader)
    tqdm.write(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {avg_loss:.8f}")  

    # --- Validation (Optional) ---
    model.eval()
    val_loss = 0
    with torch.no_grad():
        for batch in val_dataloader:
            context, target = batch
            context = context.to(model.device)
            target = target.to(model.device)
            outputs = model(
                context=context,
                #mask=context_mask,
                target=target,
                #target_mask=target_mask,
            )
            val_loss += outputs.loss.item()
            val_losses.append(outputs.loss.item())
    avg_val_loss = val_loss / len(val_dataloader)
    tqdm.write(f"Epoch {epoch+1}/{num_epochs}, Validation Loss: {avg_val_loss:.8f}")  
model.model.save_pretrained("chronos-t5-timeseries") # or model.save_pretrained("chronos-t5-timeseries")

[abdulfatir]

Hey @Mhdaw, we have already released fine-tuning code for Chronos in this repo. Please use that as the reference.

If you want to fine-tune Chronos-Bolt models, please use AutoGluon for a simplified interface. If you're interested in the fine-tuning code, please check: https://github.com/autogluon/autogluon/blob/a988db51ab51b5a29b0334a92eadba287be9d31d/timeseries/src/autogluon/timeseries/models/chronos/model.py#L393

[Mhdaw]

Thank you