f70c7b7b1e246e32e322ba059f8bf0e5d01a22be,tests/python/unittest/test_optimizer.py,,test_sparse_sgd,#,281

Before Change

    wd_options = [{}, {"wd": 0.03}, {"wd": 0.05}, {"wd": 0.07}]
    mp_options = [{}, {"multi_precision": False}, {"multi_precision": True}]
    for dtype in [np.float32]:
        for mom_option in mom_options:
            for cg_option in cg_options:
                for rg_option in rg_options:
                    for wd_option in wd_options:
                        for mp_option in mp_options:
                            kwarg = {}
                            kwarg.update(mom_option)
                            kwarg.update(cg_option)
                            kwarg.update(rg_option)
                            kwarg.update(wd_option)
                            kwarg.update(mp_option)
                            compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype,
                                              w_stype="row_sparse", g_stype="row_sparse")
                            compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype,
                                              w_stype="default", g_stype="row_sparse")


@with_seed()
def test_std_sparse_sgd():
    opt1 = PySGD
    opt2 = mx.optimizer.SGD

After Change

    agg_options = [{"aggregate_num": 0}, {"aggregate_num": 1},
                   {"aggregate_num": 4}, {"aggregate_num": np.inf}]
    for dtype in [np.float32]:
        for params in itertools.product(mom_options, cg_options, rg_options,
                                        wd_options, agg_options):
            kwarg = {k: v for param in params for k, v in param.items()}
            compare_optimizer(opt1(**kwarg),
                              opt2(use_fused_step=True, lazy_update=True, **kwarg), shapes, dtype,
                              w_stype="row_sparse", g_stype="row_sparse")
            compare_optimizer(opt1(**kwarg),
                              opt2(use_fused_step=True, lazy_update=True, **kwarg), shapes, dtype,
                              w_stype="default", g_stype="row_sparse")


@with_seed()
def test_std_sparse_sgd():
    opt1 = mx.optimizer.SGD
    opt2 = mx.optimizer.SGD

Instances