test_tensor.py

# -*- coding: utf-8 -*-
import os
import platform

import numpy as np
import pytest
from utils import get_var_value, make_tensor, opr_test

import megengine.functional as F
from megengine import Tensor
from megengine.core._trace_option import use_symbolic_shape
from megengine.core.tensor import megbrain_graph as G
from megengine.core.tensor.utils import astensor1d
from megengine.jit import trace
from megengine.utils.network import Network, set_symbolic_shape
from megengine.utils.network_node import VarNode


def test_eye():
    dtypes = [np.float32, np.bool]
    cases = [{"input": [10, 20]}, {"input": [30]}]
    for dtype in dtypes:
        for case in cases:
            np.testing.assert_allclose(
                F.eye(case["input"], dtype=dtype).numpy(),
                np.eye(*case["input"]).astype(dtype),
            )
            np.testing.assert_allclose(
                F.eye(*case["input"], dtype=dtype).numpy(),
                np.eye(*case["input"]).astype(dtype),
            )
            np.testing.assert_allclose(
                F.eye(Tensor(case["input"]), dtype=dtype).numpy(),
                np.eye(*case["input"]).astype(dtype),
            )


@pytest.mark.parametrize("is_varnode", [False, True])
def test_diag(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    shapes = [(10, 10), (6, 9), (8, 7), (8,)]
    cases = []
    for shp in shapes:
        cases.append({"input": [np.random.random(shp).astype("float32")]})

    for axis in range(-2, 3):

        def run(data):
            return F.diag(data, k=axis)

        opr_test(cases, run, ref_fn=lambda x: np.diag(x, axis), network=network)


def test_full():
    shape = (2, 3)
    values = [True, 4, 5.0]
    for value in values:
        np.testing.assert_allclose(F.full(shape, value).numpy(), np.full(shape, value))
        assert F.full(shape, value).dtype == Tensor(value).dtype


@pytest.mark.parametrize("is_varnode", [True, False])
def test_cumsum(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = Tensor([[1, 2, 3], [4, 5, 6]], np.int32)
    y = F.cumsum(x, -1)
    np.testing.assert_equal(
        y.numpy(), np.array([[1, 3, 6], [4, 9, 15]]).astype(np.int32)
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_concat(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    def get_data_shape(length: int):
        return (length, 2, 3)

    data1 = np.random.random(get_data_shape(5)).astype("float32")
    data2 = np.random.random(get_data_shape(6)).astype("float32")
    data3 = np.random.random(get_data_shape(7)).astype("float32")

    def run(data1, data2):
        return F.concat([data1, data2])

    cases = [{"input": [data1, data2]}, {"input": [data1, data3]}]
    opr_test(cases, run, ref_fn=lambda x, y: np.concatenate([x, y]), network=network)

    x1 = Tensor(np.arange(0, 6, dtype=np.float32).reshape((2, 3)))
    x2 = Tensor(np.arange(6, 12, dtype=np.float32).reshape((2, 3)))
    y = F.concat([x1, x2], axis=-1)
    np.testing.assert_equal(
        y.numpy(),
        np.array([[0, 1, 2, 6, 7, 8], [3, 4, 5, 9, 10, 11]]).astype(np.float32),
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_condtake(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = np.array([[1, 2, 3], [4, 5, 6]]).astype("float32")
    y = np.array([[True, False, True], [False, True, True]])
    xx = make_tensor(x, network)
    yy = make_tensor(y, network)
    val, idx = F.cond_take(yy, xx)
    if is_varnode:
        np.testing.assert_equal(get_var_value(val), x[y])
        np.testing.assert_equal(get_var_value(idx), np.where(y.reshape(-1))[0])
    else:
        np.testing.assert_equal(val.numpy(), x[y])
        np.testing.assert_equal(idx.numpy(), np.where(y.reshape(-1))[0])


@pytest.mark.parametrize("is_varnode", [True, False])
def test_concat_stack_device(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    data1 = make_tensor(np.random.random((2, 2, 2)).astype("float32"), network, "cpu0")
    data2 = make_tensor(np.random.random((2, 2, 2)).astype("float32"), network, "cpu1")
    data3 = make_tensor(np.random.random((2, 2, 2)).astype("float32"), network, "cpu0")

    for func in [F.concat, F.stack]:
        out = F.concat([data1, data2], device="cpu1")
        assert str(out.device).split(":")[0] == "cpu1"
        out = F.concat([data1, data3])
        assert str(out.device).split(":")[0] == "cpu0"

        with pytest.raises(RuntimeError):
            try:
                out = F.concat([data1, data2])
            except:
                raise RuntimeError("inputs have different devices")


@pytest.mark.parametrize("is_varnode", [True, False])
def test_stack(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    data1 = np.random.random((3, 2, 2)).astype("float32")
    data2 = np.random.random((3, 2, 2)).astype("float32")
    data3 = np.random.random((3, 2, 2)).astype("float32")

    cases = [{"input": [data1, data2]}, {"input": [data1, data3]}]
    for ai in range(3):

        def run(data1, data2):
            return F.stack([data1, data2], axis=ai)

        opr_test(
            cases, run, ref_fn=lambda x, y: np.stack([x, y], axis=ai), network=network
        )

    x1 = Tensor(np.arange(0, 3, dtype=np.float32).reshape((3)))
    x2 = Tensor(np.arange(6, 9, dtype=np.float32).reshape((3)))
    y = F.stack([x1, x2], axis=-1)
    np.testing.assert_equal(
        y.numpy(), np.array([[0, 6], [1, 7], [2, 8]]).astype(np.float32)
    )

    x1 = Tensor(np.arange(0, 3, dtype=np.float32).reshape((3)))
    x2 = Tensor(np.arange(6, 9, dtype=np.float32).reshape((3)))
    y = F.stack([x1, x2], axis=-1)
    np.testing.assert_equal(
        y.numpy(), np.array([[0, 6], [1, 7], [2, 8]]).astype(np.float32)
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_split_basic(is_varnode):
    if is_varnode:
        network = Network()
        saved_symbolic_shape = set_symbolic_shape(False)
    else:
        network = None

    data = np.random.random((2, 3, 4, 5)).astype(np.float32)
    inp = make_tensor(data, network)

    mge_out0 = F.split(inp, 2, axis=3)
    mge_out1 = F.split(inp, [3], axis=3)

    np_out = np.split(data, [3, 5], axis=3)

    assert len(mge_out0) == 2
    assert len(mge_out1) == 2

    np.testing.assert_equal(mge_out0[0].numpy(), np_out[0])
    np.testing.assert_equal(mge_out1[0].numpy(), np_out[0])

    np.testing.assert_equal(mge_out0[1].numpy(), np_out[1])
    np.testing.assert_equal(mge_out1[1].numpy(), np_out[1])

    try:
        F.split(inp, 4)
        assert False
    except ValueError as e:
        pass

    try:
        F.split(inp, [3, 2, 5], axis=3)
        assert False
    except ValueError as e:
        assert str(e) == "Invalid nsplits_or_secions: [3, 2, 5]"

    if is_varnode:
        set_symbolic_shape(saved_symbolic_shape)


def test_concat_and_stack():
    import copy
    from megengine.autodiff import GradManager
    import torch

    def generate_test_data(max_nr_inp, max_dim, max_dim_len, test_concat=True):
        nr_inp = np.random.randint(1, max_nr_inp) if max_nr_inp > 1 else 1
        dims = np.random.randint(1, max_dim)
        cat_axis = (
            np.random.randint(-dims, dims)
            if test_concat
            else np.random.randint(-dims - 1, dims + 1)
        )

        ishape = [np.random.randint(0, max_dim_len) for _ in range(dims)]
        ishapes = [copy.deepcopy(ishape) for _ in range(nr_inp)]
        if test_concat:
            for i in range(nr_inp):
                ishapes[i][cat_axis] = np.random.randint(0, max_dim_len)

        inp_nps = []
        for ishape in ishapes:
            inp_nps.append(np.random.randn(*ishape))
        return inp_nps, cat_axis

    def test_impl(max_nr_inp, max_dim, max_dim_len, test_concat):
        inp_nps, cat_axis = generate_test_data(
            max_nr_inp, max_dim, max_dim_len, test_concat
        )
        inp_mges = [Tensor(inp_np) for inp_np in inp_nps]
        inp_torchs = [torch.tensor(inp_np, requires_grad=True) for inp_np in inp_nps]
        if test_concat:
            np_func, mge_func, torch_func = np.concatenate, F.concat, torch.cat
        else:
            np_func, mge_func, torch_func = np.stack, F.stack, torch.stack

        res_np = np_func(inp_nps, axis=cat_axis)
        grad_np = np.random.randn(*res_np.shape).astype(np.float32)

        gm = GradManager().attach(inp_mges)
        with gm:
            res_mge = mge_func(inp_mges, axis=cat_axis)
            gm.backward(res_mge, Tensor(grad_np))

        res_torch = torch_func(inp_torchs, dim=cat_axis)
        res_torch.backward(torch.tensor(grad_np))

        np.testing.assert_allclose(res_mge.numpy(), res_torch.detach().cpu().numpy())
        for inp_mge, inp_torch in zip(inp_mges, inp_torchs):
            np.testing.assert_allclose(
                inp_mge.grad.numpy(), inp_torch.grad.detach().cpu().numpy()
            )

    def test_concat(max_nr_inp, max_dim, max_dim_len):
        test_impl(max_nr_inp, max_dim, max_dim_len, test_concat=True)

    def test_stack(max_nr_inp, max_dim, max_dim_len):
        test_impl(max_nr_inp, max_dim, max_dim_len, test_concat=False)

    # test only one input
    test_concat(1, 7, 16)
    test_stack(1, 7, 16)

    # test zero shape
    test_concat(10, 7, 1)
    test_stack(10, 7, 1)

    for _ in range(3):
        test_concat(10, 7, 16)

    for _ in range(3):
        test_stack(10, 7, 16)


@pytest.mark.parametrize("symbolic", [None, False, True])
def test_split(symbolic):
    x = Tensor(np.random.random((10, 20)), dtype=np.float32)
    y = F.split(x, 3, axis=-1)
    z = F.split(x, [6, 17], axis=-1)
    assert str([i.numpy().shape for i in y]) == "[(10, 7), (10, 7), (10, 6)]"
    assert str([i.numpy().shape for i in z]) == "[(10, 6), (10, 11), (10, 3)]"

    inp1 = np.random.random((3, 4, 5, 6)).astype(np.float32)
    inp2 = np.random.random((0, 4, 5, 6)).astype(np.float32)

    def ref(inp, nsplits_or_sections, axis):
        return np.split(inp, nsplits_or_sections, axis)

    def func(inp, nsplits_or_sections, axis):
        return F.split(inp, nsplits_or_sections, axis)

    cases = [
        (inp1, 2, 3),
        (inp1, [3], 3),
        (inp1, [3, 3, 5], 3),
        (inp2, 2, 3),
        (inp2, [3], 3),
        (inp2, [3, 3, 5], 3),
    ]

    for case in cases:
        if symbolic is None:
            fn = func
        else:
            fn = trace(symbolic=symbolic)(func)
        for i in range(3 if symbolic is not None else 1):
            ref_out = ref(*case)
            out = fn(Tensor(case[0]), case[1], case[2])
            assert len(ref_out) == len(out)
            for idx in range(len(ref_out)):
                np.testing.assert_equal(ref_out[idx], out[idx].numpy())


def test_gather():
    x = Tensor([[1, 2], [3, 4], [5, 6],])
    index = Tensor([[0, 1], [1, 0], [1, 1]])
    y = F.gather(x, 1, index)
    np.testing.assert_equal(
        y.numpy(), np.array([[1, 2], [4, 3], [6, 6]]).astype(np.int32)
    )


def test_scatter():
    x = Tensor(np.zeros(shape=(3, 5), dtype=np.float32))
    source = Tensor(
        [
            [0.9935, 0.9465, 0.2256, 0.8926, 0.4396],
            [0.7723, 0.0718, 0.5939, 0.357, 0.4576],
        ]
    )
    index = Tensor([[0, 2, 0, 2, 1], [2, 0, 1, 1, 2]])
    y = F.scatter(x, -2, index, source)
    np.testing.assert_equal(
        y.numpy().round(decimals=4),
        np.array(
            [
                [0.9935, 0.0718, 0.2256, 0.0, 0.0],
                [0.0, 0.0, 0.5939, 0.357, 0.4396],
                [0.7723, 0.9465, 0.0, 0.8926, 0.4576],
            ]
        ).astype(np.float32),
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_swapaxes(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = Tensor(np.array([[1, 2, 3]], dtype=np.int32))
    y = F.swapaxes(x, 0, 1)
    np.testing.assert_equal(y.numpy(), np.array([[1], [2], [3]]).astype(np.int32))


@pytest.mark.parametrize("is_varnode", [True, False])
def test_reshape(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = np.arange(6, dtype="float32")
    xx = make_tensor(x, network)
    y = x.reshape(1, 2, 3)

    for shape in [
        (1, 2, 3),
        (1, -1, 3),
        (1, make_tensor(-1, network), 3),
        np.array([1, -1, 3], dtype="int32"),
        make_tensor([1, -1, 3], network),
    ]:
        yy = F.reshape(xx, shape)
        np.testing.assert_equal(yy.numpy(), y)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_broadcast_auto_infer(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = np.random.random((1, 2, 3)).astype(np.float32)
    xx = make_tensor(x, network)

    for shape in [
        (1, 2, 3),
        (1, None, 3),
    ]:
        yy = F.broadcast_to(xx, shape)
        np.testing.assert_equal(yy.numpy(), x)

    with pytest.raises(ValueError):
        F.broadcast_to(xx, (1, -1, 3))

    with pytest.raises(ValueError):
        F.broadcast_to(xx, (None, 1, 2, 3))

    F.broadcast_to(xx, (1, None, 2, 3))
    t = make_tensor(2, network)
    F.broadcast_to(xx, (t, None, 2, 3))


@pytest.mark.parametrize("is_trace", [True, False])
def test_reshape_on_empty_tensor(is_trace):
    input1_shape = (100, 0, 1)
    output1_shape = (100, 0, 10)
    data1 = Tensor(np.random.random(input1_shape).astype(np.float32))

    input2_shape = (10, 0)
    output2_shape = (0,)
    data2 = Tensor(np.random.random(input2_shape).astype(np.float32))

    input3_shape = (10, 0, 10)
    output3_shape = (0, 1, 2, 3)
    data3 = Tensor(np.random.random(input3_shape).astype(np.float32))

    def comp(out, target_shp):
        assert out._tuple_shape == target_shp

    def func(x, shp):
        return F.reshape(x, shp)

    cases = [
        [data1, output1_shape],
        [data2, output2_shape],
        [data3, output3_shape],
    ]

    def test(func, inp, comp, target_shp):
        out = func(inp, target_shp)
        comp(out, target_shp)

    if is_trace:
        for symbolic in [False, True]:
            for inp, target_shp in cases:
                func_traced = trace(symbolic=symbolic)(func)
                test(func_traced, inp, comp, target_shp)
                test(func_traced, inp, comp, target_shp)
                test(func_traced, inp, comp, target_shp)
    else:
        for inp, target_shp in cases:
            test(func, inp, comp, target_shp)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_reshape_shape_inference(is_varnode):
    if is_varnode:
        network = Network()
        saved_symbolic_shape = set_symbolic_shape(False)
    else:
        network = None

    x_shape_known = make_tensor([1, 2, 3, 4], network)
    x_shape_unknown = F.broadcast_to(
        make_tensor([1.0], network), shape=make_tensor([1, 1, 1, 1], network).sum()
    )
    tshp_unknown = astensor1d(
        (make_tensor([2], network), make_tensor([2], network)), x_shape_known
    )
    tshp_known = astensor1d((2, 2), x_shape_known)
    tshp_known_unspec = astensor1d((2, -1), x_shape_known)

    def check_shape(output, target):
        source = output.shape
        if isinstance(source, Tensor):
            source = source.numpy()
        np.testing.assert_equal(source, target.shape)

    def func(x, target_shape):
        return x.reshape(target_shape)

    cases = [
        {"input": [x_shape_known, tshp_unknown], "output": [np.zeros((2, 2)),]},
        {"input": [x_shape_unknown, tshp_unknown], "output": [np.zeros((2, 2)),]},
        {"input": [x_shape_known, tshp_known], "output": [np.zeros((2, 2)),]},
        {"input": [x_shape_known, tshp_known_unspec], "output": [np.zeros((2, 2)),]},
        {"input": [x_shape_unknown, tshp_known], "output": [np.zeros((2, 2)),]},
        {"input": [x_shape_unknown, tshp_known_unspec], "output": [np.zeros((2, 2)),]},
    ]
    opr_test(cases, func, compare_fn=check_shape, test_trace=True, network=network)
    if is_varnode:
        set_symbolic_shape(saved_symbolic_shape)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_squeeze(is_varnode):
    if is_varnode:
        network = Network()
        saved_symbolic_shape = set_symbolic_shape(False)
    else:
        network = None

    x = Tensor(np.array([1, 2], dtype=np.int32).reshape(1, 1, 2, 1))
    y = F.squeeze(x, -1)
    np.testing.assert_equal(y.numpy(), np.array([[[1, 2]]]).astype(np.int32))

    x = np.arange(6, dtype="float32").reshape(1, 2, 3, 1)
    xx = make_tensor(x, network)

    for axis in [None, 3, -4, (3, -4)]:
        y = np.squeeze(x, axis)
        yy = F.squeeze(xx, axis)
        np.testing.assert_equal(y, yy.numpy())

    if is_varnode:
        set_symbolic_shape(saved_symbolic_shape)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_expand_dims(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = Tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3))
    y = F.expand_dims(x, -1)
    np.testing.assert_equal(
        y.numpy(), np.array([[[1], [2], [3]], [[4], [5], [6]]]).astype(np.int32)
    )

    x = np.arange(6, dtype="float32").reshape(2, 3)
    xx = make_tensor(x, network)

    for axis in [2, -3, (3, -4), (1, -4)]:
        y = np.expand_dims(x, axis)
        yy = F.expand_dims(xx, axis)
        np.testing.assert_equal(y, yy.numpy())


def test_expand_dims_for_scalar():
    x = np.array(1, dtype="float32")
    xx = make_tensor(x, None)
    for axis in [0, -1, (0, 1), (-1, -2), (0, -1)]:
        y = np.expand_dims(x, axis)
        yy = F.expand_dims(xx, axis)
        np.testing.assert_equal(y, yy.numpy())

    for axis in [1, -2, (1, 2), (-2, -3)]:
        np.testing.assert_raises(np.AxisError, np.expand_dims, x, axis)
        np.testing.assert_raises(RuntimeError, F.expand_dims, xx, axis)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_elemwise_dtype_promotion(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = np.random.rand(2, 3).astype("float32")
    y = np.random.rand(1, 3).astype("float16")
    xx = make_tensor(x, network)
    yy = make_tensor(y, network)
    z = xx * yy
    np.testing.assert_equal(z.numpy(), x * y)

    z = xx + y
    np.testing.assert_equal(z.numpy(), x + y)

    z = x - yy
    np.testing.assert_equal(z.numpy(), x - y)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_linspace(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    cases = [
        {"input": [1, 9, 9]},
        {"input": [3, 10, 8]},
    ]
    opr_test(
        cases,
        F.linspace,
        ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32),
        network=network,
    )

    cases = [
        {"input": [9, 1, 9]},
        {"input": [10, 3, 8]},
    ]
    opr_test(
        cases,
        F.linspace,
        ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32),
        network=network,
    )

    cases = [
        {"input": [1, make_tensor(9, network), 9]},
        {"input": [make_tensor(1, network), 9, make_tensor(9, network)]},
    ]
    opr_test(
        cases,
        F.linspace,
        ref_fn=lambda start, end, step: np.linspace(1, 9, 9, dtype=np.float32),
        network=network,
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_arange(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    cases = [
        {"input": [1, 9, 1]},
        {"input": [2, 10, 2]},
    ]
    opr_test(
        cases,
        F.arange,
        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
        network=network,
    )

    cases = [
        {"input": [9, 1, -1]},
        {"input": [10, 2, -2]},
    ]
    opr_test(
        cases,
        F.arange,
        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
        network=network,
    )

    cases = [
        {"input": [9.3, 1.2, -0.5]},
        {"input": [10.3, 2.1, -1.7]},
    ]
    opr_test(
        cases,
        F.arange,
        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
        network=network,
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_round(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    data1_shape = (15,)
    data2_shape = (25,)
    data1 = np.random.random(data1_shape).astype(np.float32)
    data2 = np.random.random(data2_shape).astype(np.float32)

    cases = [{"input": data1}, {"input": data2}]
    opr_test(cases, F.round, ref_fn=np.round, network=network)


@pytest.mark.parametrize("is_varnode", [True, False])
def test_flatten(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    inp_shape = (2, 2, 3, 3)
    x = Tensor(np.arange(36, dtype=np.int32).reshape(inp_shape),)
    y = F.flatten(x, -2, -1)
    np.testing.assert_equal(
        y.numpy(),
        np.array(
            [
                [[0, 1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12, 13, 14, 15, 16, 17]],
                [
                    [18, 19, 20, 21, 22, 23, 24, 25, 26],
                    [27, 28, 29, 30, 31, 32, 33, 34, 35],
                ],
            ]
        ).astype(np.int32),
    )

    data0_shape = (2, 3, 4, 5)
    data1_shape = (4, 5, 6, 7)
    data0 = np.random.random(data0_shape).astype(np.float32)
    data1 = np.random.random(data1_shape).astype(np.float32)

    cases = [
        {"input": data0, "output": data0.flatten()},
        {"input": data1, "output": data1.flatten()},
    ]
    opr_test(cases, F.flatten, network=network)

    cases = [
        {"input": data0, "output": data0.reshape(2, -1)},
        {"input": data1, "output": data1.reshape(4, -1)},
    ]
    opr_test(cases, F.flatten, start_axis=1, network=network)

    cases = [
        {"input": data0, "output": data0.reshape(2, 3, -1)},
        {"input": data1, "output": data1.reshape(4, 5, -1)},
    ]
    opr_test(cases, F.flatten, start_axis=2, network=network)

    cases = [
        {"input": data0, "output": data0.reshape(2, -1, 5)},
        {"input": data1, "output": data1.reshape(4, -1, 7)},
    ]
    opr_test(
        cases, F.flatten, start_axis=1, end_axis=2, network=network,
    )


@pytest.mark.parametrize("is_varnode", [True, False])
def test_broadcast(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    input1_shape = (20, 30)
    output1_shape = (30, 20, 30)
    data1 = np.random.random(input1_shape).astype(np.float32)

    input2_shape = (10, 1)
    output2_shape = (20, 10, 20)
    data2 = np.random.random(input2_shape).astype(np.float32)

    input3_shape = (10, 10)
    output3_shape = (10, 10)
    data3 = np.random.random(input3_shape).astype(np.float32)

    cases = [
        {
            "input": [data1, output1_shape],
            "output": np.broadcast_to(data1, output1_shape),
        },
        {
            "input": [data2, output2_shape],
            "output": np.broadcast_to(data2, output2_shape),
        },
        {
            "input": [data3, output3_shape],
            "output": np.broadcast_to(data3, output3_shape),
        },
    ]

    opr_test(cases, F.broadcast_to, network=network)

    x = F.ones((2, 1, 3))
    with pytest.raises(RuntimeError):
        F.broadcast_to(x, (2, 3, 4))

    with pytest.raises(RuntimeError):
        F.broadcast_to(x, (4, 1, 3))

    with pytest.raises(RuntimeError):
        F.broadcast_to(x, (1, 3))


@pytest.mark.parametrize("is_trace", [True, False])
def test_broadcast_on_empty_tensor(is_trace):
    input1_shape = (100, 0, 1)
    output1_shape = (100, 0, 10)
    data1 = Tensor(np.random.random(input1_shape).astype(np.float32))

    input2_shape = (10, 0)
    output2_shape = (10, 10, 0)
    data2 = Tensor(np.random.random(input2_shape).astype(np.float32))

    input3_shape = (0, 0, 1, 10)
    output3_shape = (10, 0, 0, 10, 10)
    data3 = Tensor(np.random.random(input3_shape).astype(np.float32))

    def comp(out, target_shp):
        assert out._tuple_shape == target_shp

    def func(x, shp):
        return F.broadcast_to(x, shp)

    cases = [
        [data1, output1_shape],
        [data2, output2_shape],
        [data3, output3_shape],
    ]

    def test(func, inp, comp, target_shp):
        out = func(inp, target_shp)
        comp(out, target_shp)

    if is_trace:
        for symbolic in [False, True]:
            for inp, target_shp in cases:
                func_traced = trace(symbolic=symbolic)(func)
                test(func_traced, inp, comp, target_shp)
                test(func_traced, inp, comp, target_shp)
                test(func_traced, inp, comp, target_shp)
    else:
        for inp, target_shp in cases:
            test(func, inp, comp, target_shp)


@pytest.mark.parametrize(
    "input_shape, target_shapes",
    [
        ((3,), [(2, 1, 3), (1, 2, 3), (2, 2, 3)]),
        ((1, 3, 1), [(2, None, 3), (3, None, 3), (1, None, 1)]),
    ],
)
@pytest.mark.parametrize("is_symbolic", [True, False])
def test_broadcast_on_trace(is_symbolic, input_shape, target_shapes):
    x = F.ones(input_shape)

    @trace(symbolic=is_symbolic)
    def broadcast(inp, shape):
        return F.broadcast_to(inp, shape)

    for target_shape in target_shapes:
        if None in target_shape:
            symbolic_target_shape = tuple(
                map(lambda x: None if x is None else Tensor(x), target_shape)
            )
            output = broadcast(x, symbolic_target_shape)
            for i in range(len(target_shape)):
                if target_shape[i] is not None:
                    assert output._tuple_shape[i] == target_shape[i]
                else:
                    assert (
                        output._tuple_shape[i] == x._tuple_shape[i - len(target_shape)]
                    )
        else:
            symbolic_target_shape = Tensor(target_shape)
            output = broadcast(x, symbolic_target_shape)
            assert output._tuple_shape == target_shape


@pytest.mark.parametrize("is_varnode", [True, False])
def test_utils_astensor1d(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    reference = make_tensor(0, network)

    # literal
    x = [1, 2, 3]
    for dtype in [None, "float32"]:
        xx = astensor1d(x, reference, dtype=dtype)
        assert isinstance(xx, type(reference))
        np.testing.assert_equal(xx.numpy(), x)

    # numpy array
    x = np.asarray([1, 2, 3], dtype="int32")
    for dtype in [None, "float32"]:
        xx = astensor1d(x, reference, dtype=dtype)
        assert isinstance(xx, type(reference))
        np.testing.assert_equal(xx.numpy(), x.astype(dtype) if dtype else x)

    # tensor
    x = make_tensor([1, 2, 3], network)
    for dtype in [None, "float32"]:
        xx = astensor1d(x, reference, dtype=dtype)
        assert isinstance(xx, type(reference))
        np.testing.assert_equal(xx.numpy(), x.numpy())

    # mixed
    x = [1, make_tensor(2, network), 3]
    for dtype in [None, "float32"]:
        xx = astensor1d(x, reference, dtype=dtype)
        assert isinstance(xx, type(reference))
        np.testing.assert_equal(xx.numpy(), [1, 2, 3])

    # varnode
    if is_varnode:
        a = np.array([[1, 2, 3], [4, 5, 6]]).astype("float32")
        b = np.array([[True, False, True], [False, True, True]])
        aa = make_tensor(a, network)
        bb = make_tensor(b, network)
        x, y = F.cond_take(bb, aa)
        for dtype in [None, "float32"]:
            xx = astensor1d(x, reference, dtype=dtype)
            assert isinstance(xx, type(reference))
            np.testing.assert_equal(get_var_value(xx), get_var_value(x))


def test_device():
    x = Tensor([1, 2, 3], dtype="float32")

    y1 = F.eye(x.shape, dtype="float32")
    y2 = F.eye(x.shape, dtype="float32", device=None)
    np.testing.assert_almost_equal(y1.numpy(), y2.numpy())

    y3 = F.eye(x.shape, dtype="float32", device="xpux")
    y4 = F.eye(x.shape, dtype="float32", device=x.device)
    np.testing.assert_almost_equal(y3.numpy(), y4.numpy())

    y5 = F.full((3, 2), 4, device=x.device)
    y6 = F.full((3, 2), 4, device="xpux")
    np.testing.assert_almost_equal(y5.numpy(), y6.numpy())


@pytest.mark.parametrize("is_varnode", [True, False])
def test_identity(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = make_tensor(np.random.random((5, 10)).astype(np.float32), network)
    y = F.copy(x)
    np.testing.assert_equal(y.numpy(), x)


def copy_test(dst, src, network):
    data = np.random.random((2, 3)).astype(np.float32)
    x = make_tensor(data, device=src, network=network)
    y = F.copy(x, dst)
    assert np.allclose(data, y.numpy())
    if network is None:
        z = x.to(dst)
        assert np.allclose(data, z.numpy())


@pytest.mark.require_ngpu(1)
@pytest.mark.parametrize("is_varnode", [True, False])
def test_copy_h2d(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    copy_test("cpu0", "gpu0", network=network)


@pytest.mark.require_ngpu(1)
@pytest.mark.parametrize("is_varnode", [True, False])
def test_copy_d2h(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    copy_test("gpu0", "cpu0", network=network)


@pytest.mark.require_ngpu(2)
@pytest.mark.parametrize("is_varnode", [True, False])
def test_copy_d2d(is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    copy_test("gpu0", "gpu1", network=network)
    copy_test("gpu0:0", "gpu0:1", network=network)


@pytest.mark.require_ngpu(2)
@pytest.mark.parametrize(
    "shape, device_src, device_dst",
    [
        ((0,), "cpu0", "cpu0"),
        ((10, 0), "cpu0", "cpu1"),
        ((2, 0, 3), "cpu0", "gpu0"),
        ((1, 0, 1, 0), "gpu0", "cpu0"),
        ((2, 3, 4, 5, 0), "gpu0", "gpu1"),
    ],
)
@pytest.mark.parametrize("is_symbolic", [None, True, False])
def test_copy_empty(shape, device_src, device_dst, is_symbolic):
    inp = Tensor(np.random.randn(*shape).astype("float32"), device=device_src)

    def func(inp):
        return F.copy(inp, device_dst)

    if is_symbolic is not None:
        func = trace(symbolic=is_symbolic)(func)

    for _ in range(3):
        out = func(inp)
        assert out.numpy().shape == shape
        assert out.device == device_dst
        if is_symbolic is None:
            break


@pytest.mark.parametrize(
    "shape, repeats, axis",
    [
        ((2,), 2, 0),
        ((2, 3, 4, 5), 3, 0),
        ((2, 3, 4, 5), 4, 3),
        ((2,), 2, None),
        ((2, 3, 4, 5), 3, None),
        ((), 1, None),
        ((), 10, None),
    ],
)
@pytest.mark.parametrize("is_varnode", [True, False])
def test_repeat(shape, repeats, axis, is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    def repeat_func(inp):
        return F.repeat(inp=inp, repeats=repeats, axis=axis)

    if shape != ():
        cases = [
            {"input": np.random.randn(*shape).astype("float32")},
        ]
    else:
        cases = [{"input": np.array(1.23)}]

    opr_test(
        cases,
        repeat_func,
        ref_fn=lambda inp: np.repeat(inp, repeats, axis),
        network=network,
    )


@pytest.mark.parametrize(
    "shape, reps",
    [
        ((2,), (2,)),
        ((2, 3, 4, 5), (1, 1, 1, 1)),
        ((2, 3, 4, 5), (1, 2, 3, 4)),
        # FIXME: tile does not support ndim 7
        # ((2, 3, 4, 5), (2, 2, 2, 2, 2, 2, 2)),
    ],
)
@pytest.mark.parametrize("is_varnode", [True])
def test_tile(shape, reps, is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    def tile_func(inp):
        return F.tile(inp=inp, reps=reps)

    cases = [{"input": np.random.randn(*shape).astype("float32")}]

    opr_test(cases, tile_func, ref_fn=lambda inp: np.tile(inp, reps), network=network)


@pytest.mark.parametrize(
    "shape, shifts, axis",
    [
        ((2, 3), 0, None),
        ((2, 3), 1, 0),
        ((2, 3), 100, 0),
        ((2, 3), -100, 0),
        ((2, 3, 4, 5), (-1, 1), (0, 1)),
        ((2, 3, 4, 5), (-2, 1, 2), (1, 2, 3)),
    ],
)
@pytest.mark.parametrize("is_varnode", [True, False])
def test_roll(shape, shifts, axis, is_varnode):
    if is_varnode:
        network = Network()
    else:
        network = None

    x = Tensor([[1, 2], [3, 4], [5, 6]], np.int32)
    y = F.roll(x, 1, -1)
    np.testing.assert_equal(
        y.numpy(), np.array([[2, 1], [4, 3], [6, 5]]).astype(np.int32)
    )

    inp = np.random.randn(*shape).astype("float32")

    def func(inp):
        return F.roll(inp, shifts, axis)

    cases = [
        {"input": inp},
    ]

    opr_test(
        cases, func, ref_fn=lambda inp: np.roll(inp, shifts, axis), network=network
    )


@pytest.mark.parametrize(
    "shape, shifts, axis", [((10, 0), 5, 1), ((10, 0), -10, 1),],
)
@pytest.mark.parametrize("is_symbolic", [None, True, False])
def test_roll_empty_tensor(shape, shifts, axis, is_symbolic):
    inp = Tensor(np.random.randn(*shape).astype("float32"))

    def func(inp):
        return F.roll(inp, shifts, axis)

    if is_symbolic is not None:
        func = trace(symbolic=is_symbolic)(func)

    out_ref = np.roll(inp.numpy(), shifts, axis)
    for _ in range(3):
        out = F.roll(inp, shifts, axis)
        np.testing.assert_equal(out.numpy(), out_ref)
        if is_symbolic is None:
            break