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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
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¶
clr = LinearRegression()
clr.fit(X_train, y_train)
print(f"coefficients: {clr.coef_}, {clr.intercept_}")
coefficients: [-1.52678363e-01 4.81531186e+01 6.42954043e+01 -1.90152247e-02
-2.10422973e-01], -0.0024843283844364628
Evaluation¶
LinearRegression: l2=92.55085646195683, r2=0.9871892691280045
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: 69.10, NNZs: 5, Bias: 0.294272, T: 750, Avg. loss: 687.174653
Total training time: 0.00 seconds.
-- Epoch 2
Norm: 77.45, NNZs: 5, Bias: 0.130717, T: 1500, Avg. loss: 79.308657
Total training time: 0.00 seconds.
-- Epoch 3
Norm: 79.51, NNZs: 5, Bias: -0.001988, T: 2250, Avg. loss: 58.927697
Total training time: 0.00 seconds.
-- Epoch 4
Norm: 80.03, NNZs: 5, Bias: -0.057954, T: 3000, Avg. loss: 57.496833
Total training time: 0.00 seconds.
-- Epoch 5
Norm: 80.36, NNZs: 5, Bias: -0.117119, T: 3750, Avg. loss: 57.316691
Total training time: 0.00 seconds.
/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:1603: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.
warnings.warn(
coefficients: [-1.75637160e-01 4.83375481e+01 6.41925528e+01 1.36168917e-02
-2.77860867e-01], [-0.11711857]
Evaluation
SGDRegressor: sl2=92.0893419873538, sr2=0.9872531511703074
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=1643633.375
iteration 1, loss=158838.421875
iteration 2, loss=89816.515625
iteration 3, loss=86460.359375
iteration 4, loss=86275.578125
Let’s check the error
TorchLinearRegression: tl2=92.1086611070357, tr2=0.9872504770508839
And the coefficients.
print("coefficients:")
for p in model.parameters():
print(p)
coefficients:
Parameter containing:
tensor([[-0.4810, 47.9366, 64.2478, -0.3368, -0.4574]], requires_grad=True)
Parameter containing:
tensor([0.0190], requires_grad=True)
Conversion to ONNX¶
Let’s convert it to ONNX.
ep = torch.onnx.export(model, (torch.Tensor(X_test[:2]),), dynamo=True)
onx = ep.model_proto
[torch.onnx] Obtain model graph for `TorchLinearRegression([...]` with `torch.export.export(..., strict=False)`...
[torch.onnx] Obtain model graph for `TorchLinearRegression([...]` with `torch.export.export(..., strict=False)`... ✅
[torch.onnx] Run decomposition...
[torch.onnx] Run decomposition... ✅
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... ✅
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([[ 80.546555],
[-33.39189 ]], dtype=float32)]
And the model.
Optimization¶
By default, the exported model is not optimized and leaves many local functions. They can be inlined and the model optimized with method optimize.
With dynamic shapes¶
The dynamic shapes are used by torch.export.export() and must
follow the convention described there.
ep = torch.onnx.export(
model,
(torch.Tensor(X_test[:2]),),
dynamic_shapes={"x": {0: torch.export.Dim("batch")}},
dynamo=True,
)
ep.optimize()
onx = ep.model_proto
print(pretty_onnx(onx))
[torch.onnx] Obtain model graph for `TorchLinearRegression([...]` with `torch.export.export(..., strict=False)`...
[torch.onnx] Obtain model graph for `TorchLinearRegression([...]` with `torch.export.export(..., strict=False)`... ✅
[torch.onnx] Run decomposition...
[torch.onnx] Run decomposition... ✅
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... ✅
opset: domain='pkg.onnxscript.torch_lib.common' version=1
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['s0', 5]
init: name='linear.bias' type=float32 shape=(1,) -- array([0.01901066], dtype=float32)
Constant(value=[[-0.48103...) -> t
Gemm(x, t, linear.bias, beta=1.00, transB=0, alpha=1.00, transA=0) -> addmm
output: name='addmm' type=dtype('float32') shape=['s0', 1]
Total running time of the script: (0 minutes 3.257 seconds)
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