Converting Models to ONNX

It is possible to convert any NeuralForecast model to the Open Neural Network Exchange (ONNX) format. With ONNX, you get: - faster inference - hardware acceleration - easy deployment to edge devices - a broader device support In this tutorial, we show how you can convert a NeuralForecast model to ONNX. We show an example for a univariate and multivariate model using all types of exogenous features, and with multiple unique series, making it the most general case possible. You can run these experiments using GPU with Google Colab.

Install dependencies

%%capture
!pip install neuralforecast onnxruntime onnxscript onnx

Key considerations

There are some key elements to understand when converting a NeuralForecast model to ONNX. It comes from our the library works and it explains why directly to_onnx() doesn’t work.

The forward method in neuralforecast takes a dictionary (windows_batch), but that cannot be traced, so using to_onnx() directly fails. We must define a wrapper that takes tensors and rebuilds the dictionary internally.
Recall that futr_exog spans the history and the forecast horizon. When running inference, make sure to pass values that cover the input window and the horizon
The series order matters and it must match the order of training. Internally, series are sorted by unique_id. At the inference, the same order must be passed.
The scaler matters. With scaler_type="identity", the output is in the same scale of the series. Any other scaler requires you to inverse-transform the predictions manually.
The batch size used when exporting to ONNX becomes fixed and you must use the same batch size at inference. To keep it flexible, we can use torch.onnx.export(..., dynamo=True).
For multivariate models, n_series must be constant between training and inference. We set batch size to 1 because predictions are done in one joint window.

Converting a univariate model

Let’s see an example of converting the univariate MLP to ONNX.

Import packages

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import onnxruntime as ort
from utilsforecast.data import generate_series

from neuralforecast import NeuralForecast
from neuralforecast.models import MLP, MLPMultivariate
from neuralforecast.losses.pytorch import MAE

Set constants

HIST_EXOG = ["hist_measure"]
FUTR_EXOG = ["sin_doy", "cos_doy"]
STAT_EXOG = ["static_0", "static_1"]

Function to create synthetic data

def add_exog_features(df: pd.DataFrame) -> pd.DataFrame:
    """Add future (calendar) and historical exogenous columns."""
    df = df.copy()
    doy = df["ds"].dt.dayofyear.to_numpy()
    # Future exog: deterministic calendar features, computable for any date.
    df["sin_doy"] = np.sin(2 * np.pi * doy / 365.25)
    df["cos_doy"] = np.cos(2 * np.pi * doy / 365.25)
    # Historical exog: a measurement we only have for the past.
    rng = np.random.default_rng(0)
    df["hist_measure"] = rng.normal(size=len(df))
    return df


def make_dataset():
    series = generate_series(
        n_series=4,
        freq="D",
        min_length=120,
        max_length=120,
        n_static_features=2,
        static_as_categorical=False,
        equal_ends=True,
    )
    # NeuralForecast requires static features in a separate static_df.
    static_df = series[["unique_id", *STAT_EXOG]].drop_duplicates("unique_id")
    df = add_exog_features(series.drop(columns=STAT_EXOG))
    return df, static_df

Step1: Create an ONNX wrapper

class MLPONNXWrapper(nn.Module):
    def __init__(self, model: MLP):
        super().__init__()
        self.model = model.eval()

    def forward(self, insample_y, futr_exog, hist_exog, stat_exog):
        windows_batch = {
            "insample_y": insample_y,
            "insample_mask": None,
            "futr_exog": futr_exog,
            "hist_exog": hist_exog,
            "stat_exog": stat_exog,
        }
        return self.model(windows_batch)

Step 2: Train the model in `neuralforecast`

df, static_df = make_dataset()
horizon = 14

model = MLP(
    h=horizon,
    input_size=30,
    hist_exog_list=HIST_EXOG,
    futr_exog_list=FUTR_EXOG,
    stat_exog_list=STAT_EXOG,
    max_steps=50,
    loss=MAE(),
    scaler_type="identity",  # ONNX output stays in the raw data scale
)
nf = NeuralForecast(models=[model], freq="D")
nf.fit(df, static_df=static_df)

model = nf.models[0] # extract fitted model

Step 3: Convert to ONNX

def convert_to_onnx(model: MLP, n_windows, path="mlp_univariate.onnx") -> str:
    wrapper = MLPONNXWrapper(model)
    L, h = model.input_size, model.h
    n_futr, n_hist, n_stat = len(FUTR_EXOG), len(HIST_EXOG), len(STAT_EXOG)
    B = n_windows

    example = (
        torch.randn(B, L, 1),           # insample_y
        torch.randn(B, L + h, n_futr),  # futr_exog (input window + horizon)
        torch.randn(B, L, n_hist),      # hist_exog
        torch.randn(B, n_stat),         # stat_exog
    )
    input_names = ["insample_y", "futr_exog", "hist_exog", "stat_exog"]

    torch.onnx.export(
        wrapper,
        example,
        path,
        input_names=input_names,
        output_names=["forecast"],
        opset_version=17,
    )
    return path

n_windows = df["unique_id"].nunique()
onnx_path = convert_to_onnx(model, n_windows)

Step 4: Prepare input for inference

def build_inputs(df, static_df, futr_df, model: MLP):
    L, h = model.input_size, model.h
    ids = sorted(df["unique_id"].unique())
    B = len(ids)

    insample_y = np.zeros((B, L, 1), np.float32)
    hist_exog = np.zeros((B, L, len(HIST_EXOG)), np.float32)
    futr_exog = np.zeros((B, L + h, len(FUTR_EXOG)), np.float32)
    stat_exog = np.zeros((B, len(STAT_EXOG)), np.float32)

    static_df = static_df.set_index("unique_id")
    for i, uid in enumerate(ids):
        win = df[df["unique_id"] == uid].iloc[-L:]
        fut = futr_df[futr_df["unique_id"] == uid]

        insample_y[i, :, 0] = win["y"].to_numpy()
        hist_exog[i] = win[HIST_EXOG].to_numpy()
        futr_exog[i] = np.concatenate(
            [win[FUTR_EXOG].to_numpy(), fut[FUTR_EXOG].to_numpy()], axis=0
        )
        stat_exog[i] = static_df.loc[uid, STAT_EXOG].to_numpy()

    feeds = {
        "insample_y": insample_y,
        "futr_exog": futr_exog,
        "hist_exog": hist_exog,
        "stat_exog": stat_exog,
    }
    return ids, feeds

futr_df = nf.make_future_dataframe()
futr_df = add_exog_features(futr_df)

ids, feeds = build_inputs(df, static_df, futr_df, model)

Step 5: Predict

def predict(path, feeds):
    sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
    return sess.run(["forecast"], feeds)[0]

forecast = predict(onnx_path, feeds) # [n_series, h, 1]

Converting a multivariate model

Now, let’s see an example of converting the multivariate MLP to ONNX. Note that we don’t repeat the steps to create the synthetic dataset.

Step 1: Create the ONNX wrapper

class MLPMultivariateONNXWrapper(nn.Module):
    def __init__(self, model: MLPMultivariate):
        super().__init__()
        self.model = model.eval()

    def forward(self, insample_y, futr_exog, hist_exog, stat_exog):
        windows_batch = {
            "insample_y": insample_y,
            "insample_mask": None,
            "futr_exog": futr_exog,
            "hist_exog": hist_exog,
            "stat_exog": stat_exog,
        }
        return self.model(windows_batch)

Step 2: Train the model

df, static_df = make_dataset()
horizon = 14
n_series = df["unique_id"].nunique()

model = MLPMultivariate(
    h=horizon,
    input_size=30,
    n_series=n_series,
    hist_exog_list=HIST_EXOG,
    futr_exog_list=FUTR_EXOG,
    stat_exog_list=STAT_EXOG,
    max_steps=50,
    loss=MAE(),
    scaler_type="identity",
)
nf = NeuralForecast(models=[model], freq="D")
nf.fit(df, static_df=static_df)

model = nf.models[0]

Step 3: Convert to ONNX

Recall that all series are predicted in one joint window, so batch size is set to 1. Also, n_series is fixed by the trained model.

def convert_to_onnx(model: MLPMultivariate, n_series, path="mlpmultivariate.onnx"):
    wrapper = MLPMultivariateONNXWrapper(model)
    L, h, N = model.input_size, model.h, n_series
    n_futr, n_hist, n_stat = len(FUTR_EXOG), len(HIST_EXOG), len(STAT_EXOG)

    example = (
        torch.randn(1, L, N),               # insample_y      [1, L, N]
        torch.randn(1, n_futr, L + h, N),   # futr_exog       [1, F, L+h, N]
        torch.randn(1, n_hist, L, N),       # hist_exog       [1, X, L, N]
        torch.randn(N, n_stat),             # stat_exog       [N, S]
    )
    input_names = ["insample_y", "futr_exog", "hist_exog", "stat_exog"]
    
    torch.onnx.export(
        wrapper,
        example,
        path,
        input_names=input_names,
        output_names=["forecast"],
        opset_version=17,
    )
    return path

onnx_path = convert_to_onnx(model, n_series)

Step 4: Prepare input for inference

def build_inputs(df, static_df, futr_df, model: MLPMultivariate):
    L, h = model.input_size, model.h
    ids = sorted(df["unique_id"].unique())
    N = len(ids)

    insample_y = np.zeros((1, L, N), np.float32)
    hist_exog = np.zeros((1, len(HIST_EXOG), L, N), np.float32)
    futr_exog = np.zeros((1, len(FUTR_EXOG), L + h, N), np.float32)
    stat_exog = np.zeros((N, len(STAT_EXOG)), np.float32)

    static_df = static_df.set_index("unique_id")
    for j, uid in enumerate(ids):  # j indexes the series axis
        win = df[df["unique_id"] == uid].iloc[-L:]
        fut = futr_df[futr_df["unique_id"] == uid]

        insample_y[0, :, j] = win["y"].to_numpy()
        for k, col in enumerate(HIST_EXOG):
            hist_exog[0, k, :, j] = win[col].to_numpy()
        for k, col in enumerate(FUTR_EXOG):
            # future exog spans the input window AND the horizon
            futr_exog[0, k, :, j] = np.concatenate(
                [win[col].to_numpy(), fut[col].to_numpy()]
            )
        stat_exog[j] = static_df.loc[uid, STAT_EXOG].to_numpy()

    feeds = {
        "insample_y": insample_y,
        "futr_exog": futr_exog,
        "hist_exog": hist_exog,
        "stat_exog": stat_exog,
    }
    return ids, feeds

futr_df = nf.make_future_dataframe()
futr_df = add_exog_features(futr_df)

ids, feeds = build_inputs(df, static_df, futr_df, model)

Step 5: Predict

def predict(path, feeds):
    sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
    return sess.run(["forecast"], feeds)[0]

forecast = predict(onnx_path, feeds)  # [1, h, N]

​Install dependencies

​Key considerations

​Converting a univariate model

​Import packages

​Set constants

​Function to create synthetic data

​Step1: Create an ONNX wrapper

​Step 2: Train the model in neuralforecast

​Step 3: Convert to ONNX

​Step 4: Prepare input for inference

​Step 5: Predict

​Converting a multivariate model

​Step 1: Create the ONNX wrapper

​Step 2: Train the model

​Step 3: Convert to ONNX

​Step 4: Prepare input for inference

​Step 5: Predict

Install dependencies

Key considerations

Converting a univariate model

Import packages

Set constants

Function to create synthetic data

Step1: Create an ONNX wrapper

Step 2: Train the model in `neuralforecast`

Step 3: Convert to ONNX

Step 4: Prepare input for inference

Step 5: Predict

Converting a multivariate model

Step 1: Create the ONNX wrapper

Step 2: Train the model

Step 3: Convert to ONNX

Step 4: Prepare input for inference

Step 5: Predict