101: Linear Regression and export to ONNX¶

scikit-learn and torch to train a linear regression.

data¶

import numpy as np
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression, SGDRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
import torch
from onnxruntime import InferenceSession
from experimental_experiment.helpers import pretty_onnx
from onnx_array_api.plotting.graphviz_helper import plot_dot
from experimental_experiment.torch_interpreter import to_onnx


X, y = make_regression(1000, n_features=5, noise=10.0, n_informative=2)
print(X.shape, y.shape)

X_train, X_test, y_train, y_test = train_test_split(X, y)

(1000, 5) (1000,)

scikit-learn: the simple regression¶

$A^* = (X'X)^{-1}X'Y$

clr = LinearRegression()
clr.fit(X_train, y_train)

print(f"coefficients: {clr.coef_}, {clr.intercept_}")

coefficients: [-0.8246638   0.22991437 14.47114979  6.33408646  0.01458035], 0.5162035987048611

Evaluation¶

y_pred = clr.predict(X_test)
l2 = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"LinearRegression: l2={l2}, r2={r2}")

LinearRegression: l2=113.36720174581683, r2=0.6652072977967414

scikit-learn: SGD algorithm¶

SGD = Stochastic Gradient Descent

clr = SGDRegressor(max_iter=5, verbose=1)
clr.fit(X_train, y_train)

print(f"coefficients: {clr.coef_}, {clr.intercept_}")

-- Epoch 1
Norm: 13.23, NNZs: 5, Bias: 0.480517, T: 750, Avg. loss: 77.900059
Total training time: 0.00 seconds.
-- Epoch 2
Norm: 15.05, NNZs: 5, Bias: 0.494138, T: 1500, Avg. loss: 52.883114
Total training time: 0.00 seconds.
-- Epoch 3
Norm: 15.57, NNZs: 5, Bias: 0.454208, T: 2250, Avg. loss: 51.752959
Total training time: 0.00 seconds.
-- Epoch 4
Norm: 15.68, NNZs: 5, Bias: 0.466510, T: 3000, Avg. loss: 51.589618
Total training time: 0.00 seconds.
-- Epoch 5
Norm: 15.87, NNZs: 5, Bias: 0.494202, T: 3750, Avg. loss: 51.526956
Total training time: 0.00 seconds.
~/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:1608: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.
  warnings.warn(
coefficients: [-8.73275784e-01  3.54594523e-01  1.45100734e+01  6.36508130e+00
  4.33184515e-03], [0.49420183]

Evaluation

y_pred = clr.predict(X_test)
sl2 = mean_squared_error(y_test, y_pred)
sr2 = r2_score(y_test, y_pred)
print(f"SGDRegressor: sl2={sl2}, sr2={sr2}")

SGDRegressor: sl2=113.89062680363068, sr2=0.6636615342354311

Linrar Regression with pytorch¶

class TorchLinearRegression(torch.nn.Module):
    def __init__(self, n_dims: int, n_targets: int):
        super().__init__()
        self.linear = torch.nn.Linear(n_dims, n_targets)

    def forward(self, x):
        return self.linear(x)


def train_loop(dataloader, model, loss_fn, optimizer):
    total_loss = 0.0

    # Set the model to training mode - important for batch normalization and dropout layers
    # Unnecessary in this situation but added for best practices
    model.train()
    for X, y in dataloader:
        # Compute prediction and loss
        pred = model(X)
        loss = loss_fn(pred.ravel(), y)

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

        # training loss
        total_loss += loss

    return total_loss


model = TorchLinearRegression(X_train.shape[1], 1)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
loss_fn = torch.nn.MSELoss()

device = "cpu"
model = model.to(device)
dataset = torch.utils.data.TensorDataset(
    torch.Tensor(X_train).to(device), torch.Tensor(y_train).to(device)
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1)


for i in range(5):
    loss = train_loop(dataloader, model, loss_fn, optimizer)
    print(f"iteration {i}, loss={loss}")

iteration 0, loss=135284.796875
iteration 1, loss=80241.0
iteration 2, loss=77726.859375
iteration 3, loss=77632.921875
iteration 4, loss=77634.4765625

Let’s check the error

y_pred = model(torch.Tensor(X_test)).detach().numpy()
tl2 = mean_squared_error(y_test, y_pred)
tr2 = r2_score(y_test, y_pred)
print(f"TorchLinearRegression: tl2={tl2}, tr2={tr2}")

TorchLinearRegression: tl2=113.57995195885037, tr2=0.6645790100943112

And the coefficients.

print("coefficients:")
for p in model.parameters():
    print(p)

coefficients:
Parameter containing:
tensor([[-0.6010,  0.0796, 14.6170,  6.5385, -0.0490]], requires_grad=True)
Parameter containing:
tensor([0.6163], requires_grad=True)

Conversion to ONNX¶

Let’s convert it to ONNX.

onx = to_onnx(model, (torch.Tensor(X_test[:2]),), input_names=["x"])

Let’s check it is work.

sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
res = sess.run(None, {"x": X_test.astype(np.float32)[:2]})
print(res)

[array([[17.213045],
       [-8.344327]], dtype=float32)]

And the model.

plot_dot(onx)

With dynamic shapes¶

The dynamic shapes are used by torch.export.export() and must follow the convention described there. The dynamic dimension allows any value. The model is then valid for many different shapes. That’s usually what users need.

onx = to_onnx(
    model,
    (torch.Tensor(X_test[:2]),),
    input_names=["x"],
    dynamic_shapes={"x": {0: torch.export.Dim("batch")}},
)

print(pretty_onnx(onx))

opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 5)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.6162818], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

For simplicity, it is possible to use torch.export.Dim.DYNAMIC or torch.export.Dim.AUTO.

onx = to_onnx(
    model,
    (torch.Tensor(X_test[:2]),),
    input_names=["x"],
    dynamic_shapes={"x": {0: torch.export.Dim.DYNAMIC}},
)

print(pretty_onnx(onx))

opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 5)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.6162818], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

Total running time of the script: (0 minutes 1.580 seconds)