tests/python/relax/test_training_optimizer_numeric.py

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"""Numeric tests for relax optimizer APIs."""
from typing import Callable, List

import numpy as np

import tvm
import tvm.testing
from tvm import IRModule, relax
from tvm.relax.training.optimizer import SGD, Adam, MomentumSGD
from tvm.runtime.vm import VirtualMachine
from tvm.script.parser import relax as R
from tvm.testing import assert_allclose


def _legalize_and_build(mod: IRModule, target, dev):
    ex = tvm.compile(mod, target)
    vm = VirtualMachine(ex, dev)
    return vm


def _numpy_to_tvm(data):
    if isinstance(data, (list, tuple)):
        return [_numpy_to_tvm(_data) for _data in data]
    return tvm.runtime.tensor(data)


def _tvm_to_numpy(data):
    if isinstance(data, (list, tuple, tvm.ir.Array)):
        return [_tvm_to_numpy(_data) for _data in data]
    return data.numpy()


def _assert_allclose_nested(data1, data2):
    if isinstance(data1, (list, tuple)):
        assert isinstance(data2, (list, tuple))
        assert len(data1) == len(data2)
        for x, y in zip(data1, data2):
            _assert_allclose_nested(x, y)
    else:
        assert_allclose(data1, data2)


def _assert_run_result_same(tvm_func: Callable, np_func: Callable, np_inputs: List):
    result = _tvm_to_numpy(tvm_func(*[_numpy_to_tvm(i) for i in np_inputs]))
    expected = np_func(*np_inputs)
    _assert_allclose_nested(result, expected)


@tvm.testing.parametrize_targets("llvm")
def _test_optimizer(target, dev, np_func, opt_type, *args, **kwargs):
    x = relax.Var("x", R.Tensor((3, 3), "float32"))
    y = relax.Var("y", R.Tensor((3,), "float32"))
    opt = opt_type(*args, **kwargs).init([x, y])
    mod = IRModule.from_expr(opt.get_function().with_attr("global_symbol", "main"))
    tvm_func = _legalize_and_build(mod, target, dev)["main"]

    param_arr = [np.random.rand(3, 3).astype(np.float32), np.random.rand(3).astype(np.float32)]
    grad_arr = [np.random.rand(3, 3).astype(np.float32), np.random.rand(3).astype(np.float32)]
    state_arr = _tvm_to_numpy(opt.state)

    _assert_run_result_same(tvm_func, np_func, [param_arr, grad_arr, state_arr])


lr, weight_decay = tvm.testing.parameters(
    (0.01, 0),
    (0.01, 0.02),
)


@tvm.testing.parametrize_targets("llvm")
def test_sgd(target, dev, lr, weight_decay):
    def np_func(param_tuple, grad_tuple, state_tuple):
        num_steps = state_tuple[0]
        param_tuple_new, state_tuple_new = [], []
        state_tuple_new.append(num_steps + 1)
        for i in range(len(param_tuple)):
            param = param_tuple[i]
            grad = grad_tuple[i]
            param_tuple_new.append(param - lr * (grad + weight_decay * param))
        return param_tuple_new, state_tuple_new

    _test_optimizer(target, dev, np_func, SGD, lr, weight_decay)


lr, momentum, dampening, weight_decay, nesterov = tvm.testing.parameters(
    (0.01, 0.9, 0, 0, False),
    (0.01, 0.9, 0.85, 0.02, False),
    (0.01, 0.9, 0.85, 0.02, True),
)


@tvm.testing.parametrize_targets("llvm")
def test_momentum_sgd(target, dev, lr, momentum, dampening, weight_decay, nesterov):
    def np_func(param_tuple, grad_tuple, state_tuple):
        num_steps = state_tuple[0]
        param_tuple_new, state_tuple_new = [], []
        state_tuple_new.append(num_steps + 1)

        for i in range(len(param_tuple)):
            param = param_tuple[i]
            grad = grad_tuple[i]
            velocity = state_tuple[i + 1]
            grad = param * weight_decay + grad
            velocity = momentum * velocity + grad * (1 - dampening)
            if nesterov:
                param = param - (grad + momentum * velocity) * lr
            else:
                param = param - velocity * lr
            param_tuple_new.append(param)
            state_tuple_new.append(velocity)

        return param_tuple_new, state_tuple_new

    _test_optimizer(
        target, dev, np_func, MomentumSGD, lr, momentum, dampening, weight_decay, nesterov
    )


lr, betas, eps, weight_decay = tvm.testing.parameters(
    (0.01, (0.9, 0.999), 1e-08, 0),
    (0.01, (0.8, 0.85), 1e-07, 0.1),
)


@tvm.testing.parametrize_targets("llvm")
def test_adam(target, dev, lr, betas, eps, weight_decay):
    def np_func(param_tuple, grad_tuple, state_tuple):
        num_steps = state_tuple[0]
        num_steps_new = num_steps + 1

        param_tuple_new = []
        state_tuple_new = [None] * len(state_tuple)  # type: ignore
        state_tuple_new[0] = num_steps_new
        state_tuple_new[1] = state_tuple[1] * betas[0]
        state_tuple_new[2] = state_tuple[2] * betas[1]

        for i in range(len(param_tuple)):
            param = param_tuple[i]
            grad = grad_tuple[i]
            m = state_tuple[i + 3]
            v = state_tuple[i + 3 + len(param_tuple)]
            grad = grad + weight_decay * param
            m = betas[0] * m + (1 - betas[0]) * grad
            v = betas[1] * v + (1 - betas[1]) * grad * grad
            m_hat = m / (1 - betas[0] ** num_steps_new)
            v_hat = v / (1 - betas[1] ** num_steps_new)
            param = param - lr * m_hat / (np.sqrt(v_hat) + eps)
            param_tuple_new.append(param)
            state_tuple_new[i + 3] = m
            state_tuple_new[i + 3 + len(param_tuple)] = v

        return param_tuple_new, state_tuple_new

    _test_optimizer(target, dev, np_func, Adam, lr, betas, eps, weight_decay)


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