apache--tvm/tests/python/relax/test_training_trainer_numeric.py

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import numpy as np
import pytest

import tvm
import tvm.testing
from tvm import relax
from tvm.relax.training import SetupTrainer, Trainer
from tvm.relax.training.loss import MSELoss
from tvm.relax.training.optimizer import SGD, Adam
from tvm.script import ir as I
from tvm.script import relax as R


def _get_backbone():
    @I.ir_module
    class MLP:
        I.module_attrs({"param_num": 2, "state_num": 0})

        @R.function
        def backbone(
            x: R.Tensor((1, 10), "float32"),
            w0: R.Tensor((10, 5), "float32"),
            b0: R.Tensor((5,), "float32"),
        ):
            with R.dataflow():
                lv0 = R.matmul(x, w0)
                lv1 = R.add(lv0, b0)
                out = R.nn.relu(lv1)
                R.output(out)
            return out

    return MLP


def _make_dataset():
    N = 100
    return [[np.ones((1, 10)).astype(np.float32), np.array([[0, 0, 1, 0, 0]], np.float32)]] * N


@pytest.mark.skipif(not tvm.testing.device_enabled("llvm"), reason="llvm not enabled")
def test_execute():
    target = "llvm"
    dev = tvm.device(target)
    backbone = _get_backbone()
    pred_ty = relax.TensorType((1, 5), "float32")

    setup_trainer = SetupTrainer(
        MSELoss(reduction="sum"),
        Adam(0.01),
        [pred_ty, pred_ty],
    )

    train_mod = setup_trainer(backbone)
    ex = tvm.compile(train_mod, target)
    vm = relax.VirtualMachine(ex, dev)

    trainer = Trainer(train_mod, vm, dev, False)
    trainer.zero_init_params()
    trainer.xaiver_uniform_init_params()

    dataset = _make_dataset()
    trainer.predict(dataset[0][0])
    trainer.update(dataset[0][0], dataset[0][1])


@pytest.mark.skipif(not tvm.testing.device_enabled("llvm"), reason="llvm not enabled")
def test_execute_numeric():
    target = "llvm"
    dev = tvm.device(target)
    backbone = _get_backbone()
    pred_ty = relax.TensorType((1, 5), "float32")

    setup_trainer = SetupTrainer(
        MSELoss(reduction="sum"),
        SGD(0.01),
        [pred_ty, pred_ty],
    )

    train_mod = setup_trainer(backbone)
    ex = tvm.compile(train_mod, target)
    vm = relax.VirtualMachine(ex, dev)

    trainer = Trainer(train_mod, vm, dev, False)
    trainer.zero_init_params()

    dataset = _make_dataset()
    for _ in range(2):
        for input, label in dataset:
            loss = trainer.update(input, label)
    tvm.testing.assert_allclose(loss.numpy(), 3.1974423e-14)

    result = trainer.predict(dataset[0][0])
    result_expected = np.array([[0, 0, 0.9999998, 0, 0]], np.float32)
    tvm.testing.assert_allclose(result.numpy(), result_expected)


@pytest.mark.skipif(not tvm.testing.device_enabled("llvm"), reason="llvm not enabled")
def test_load_export_params():
    target = "llvm"
    dev = tvm.device(target)
    backbone = _get_backbone()
    pred_ty = relax.TensorType((1, 5), "float32")

    setup_trainer = SetupTrainer(
        MSELoss(reduction="sum"),
        SGD(0.01),
        [pred_ty, pred_ty],
    )

    train_mod = setup_trainer(backbone)
    ex = tvm.compile(train_mod, target)
    vm = relax.VirtualMachine(ex, dev)

    trainer = Trainer(train_mod, vm, dev, False)
    trainer.xaiver_uniform_init_params()

    dataset = _make_dataset()
    for input, label in dataset:
        trainer.update(input, label)

    param_dict = trainer.export_params()
    assert "w0" in param_dict
    assert "b0" in param_dict

    trainer1 = Trainer(train_mod, vm, dev, False)
    trainer1.load_params(param_dict)

    x_sample = dataset[np.random.randint(len(dataset))][0]
    tvm.testing.assert_allclose(
        trainer.predict(x_sample).numpy(), trainer1.predict(x_sample).numpy()
    )


@pytest.mark.skipif(not tvm.testing.device_enabled("llvm"), reason="llvm not enabled")
def test_setting_error():
    target = "llvm"
    dev = tvm.device(target)
    backbone = _get_backbone()
    pred_ty = relax.TensorType((1, 5), "float32")

    setup_trainer = SetupTrainer(
        MSELoss(reduction="sum"),
        SGD(0.01),
        [pred_ty, pred_ty],
    )

    train_mod = setup_trainer(backbone)
    ex = tvm.compile(train_mod, target)
    vm = relax.VirtualMachine(ex, dev)

    trainer = Trainer(train_mod, vm, dev, False)

    dataset = _make_dataset()
    # parameters are not inited
    with pytest.raises(RuntimeError):
        trainer.predict(dataset[0][0])
    with pytest.raises(RuntimeError):
        trainer.update(dataset[0][0], dataset[0][1])


if __name__ == "__main__":
    tvm.testing.main()