mindspore2022/tests/st/ops/gpu/test_sequence_mask_op.py

import numpy as np
import pytest

from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
import mindspore.nn as nn
import mindspore.context as context

def sequence_mask(x, maxlen):
    sequence_mask_op = P.SequenceMask()
    return sequence_mask_op(Tensor(x.astype(np.int32)), maxlen)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_sequence_mask_1d():
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    a = np.array([2, 3, 1])

    maxlen = 4
    ms_out = sequence_mask(a, maxlen)
    expected_out = Tensor(np.array([[True, True, False, False],
                                    [True, True, True, False],
                                    [True, False, False, False]]))
    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_sequence_mask_2d():
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    a = np.array([[0, 1, 3, 2], [1, 4, 4, 2]])

    maxlen = 6
    ms_out = sequence_mask(a, maxlen)
    expected_out = Tensor(np.array([[[False, False, False, False, False, False],
                                     [True, False, False, False, False, False],
                                     [True, True, True, False, False, False],
                                     [True, True, False, False, False, False]],
                                    [[True, False, False, False, False, False],
                                     [True, True, True, True, False, False],
                                     [True, True, True, True, False, False],
                                     [True, True, False, False, False, False]]]))
    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_sequence_mask_3d():
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    a = np.array([[[2, 2], [1, 1]],
                  [[2, 0], [2, 1]],
                  [[0, 0], [0, 0]]])

    maxlen = 2
    ms_out = sequence_mask(a, maxlen)
    expected_out = Tensor(np.array([[[[True, True], [True, True]], [[True, False], [True, False]]],
                                    [[[True, True], [False, False]], [[True, True], [True, False]]],
                                    [[[False, False], [False, False]], [[False, False], [False, False]]]]))

    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_sequence_mask_maxlen_1():
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    a = np.array([[[0, 1], [1, 1]],
                  [[1, 0], [1, 1]],
                  [[0, 1], [0, 1]]])

    maxlen = 1
    ms_out = sequence_mask(a, maxlen)
    expected_out = Tensor(np.array([[[[False], [True]], [[True], [True,]]],
                                    [[[True], [False]], [[True], [True]]],
                                    [[[False], [True]], [[False], [True]]]]))

    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_sequence_mask_dynamic():
    class SequenceMaskDynamicNet(nn.Cell):
        def __init__(self, maxlen):
            super(SequenceMaskDynamicNet, self).__init__()
            self.maxlen = maxlen
            self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
            self.sequence_mask = P.SequenceMask()

        def construct(self, x):
            converted_to_dynamic_shape = self.convert_to_dynamic_shape(x)
            return self.sequence_mask(converted_to_dynamic_shape, self.maxlen)

    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

    sequence_mask_net = SequenceMaskDynamicNet(4)

    a = Tensor(np.array([0, 1, 0, 2, 0, 5]))
    ms_out = sequence_mask_net(a)
    expected_out = Tensor(np.array([[False, False, False, False],
                                    [True, False, False, False],
                                    [False, False, False, False],
                                    [True, True, False, False],
                                    [False, False, False, False],
                                    [True, True, True, True]]))
    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())

    a = Tensor(np.array([[4, 3, 0], [0, 1, 3]]))
    ms_out = sequence_mask_net(a)
    expected_out = Tensor(np.array([[[True, True, True, True],
                                     [True, True, True, False],
                                     [False, False, False, False]],
                                    [[False, False, False, False],
                                     [True, False, False, False],
                                     [True, True, True, False]]]))