forked from mindspore-Ecosystem/mindspore
262 lines
12 KiB
Python
262 lines
12 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, context
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
|
|
|
|
class MatrixBandPartDynamicShapeNet(nn.Cell):
|
|
def __init__(self):
|
|
super(MatrixBandPartDynamicShapeNet, self).__init__()
|
|
self.test_dynamic = inner.GpuConvertToDynamicShape()
|
|
|
|
def construct(self, x, lower, upper):
|
|
x = self.test_dynamic(x)
|
|
lower = self.test_dynamic(lower)
|
|
upper = self.test_dynamic(upper)
|
|
return F.matrix_band_part(x, lower, upper)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
@pytest.mark.parametrize('dtype', [np.int32, np.float16, np.float32, np.float64])
|
|
@pytest.mark.parametrize('batch_shape, rows, cols',
|
|
[([], 1, 1), ([], 1, 7), ([], 7, 1), ([], 7, 7),
|
|
([2], 1, 1), ([2], 1, 7), ([2], 7, 1), ([2], 7, 7),
|
|
([1, 3, 2], 1, 1), ([1, 3, 2], 1, 7), ([1, 3, 2], 7, 1), ([1, 3, 2], 7, 7)])
|
|
def test_matrix_band_part(mode, dtype, batch_shape, rows, cols):
|
|
"""
|
|
Feature: ALL TO ALL
|
|
Description: test general matrix cases for matrix_band_diag
|
|
Expectation: the result match numpy.
|
|
"""
|
|
context.set_context(mode=mode, device_target="GPU")
|
|
input_x = np.ones(batch_shape + [rows, cols]).astype(dtype)
|
|
for lower in (-1, 0, 1, rows - 1):
|
|
for upper in (-1, 0, 1, cols - 1):
|
|
np_output = input_x
|
|
if lower >= 0:
|
|
np_output = np.triu(np_output, -lower)
|
|
if upper >= 0:
|
|
np_output = np.tril(np_output, upper)
|
|
ms_output = F.matrix_band_part(Tensor(input_x), lower, upper)
|
|
np.testing.assert_array_almost_equal(ms_output.asnumpy(), np_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_matrix_band_part_vmap(mode):
|
|
"""
|
|
Feature: test matrix_band_part vmap feature.
|
|
Description: test matrix_band_part vmap feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="GPU")
|
|
x = Tensor(np.ones((2, 2, 3, 5)).astype(np.float32))
|
|
# Case 1
|
|
lower = 1
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, None, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 2
|
|
lower = 1
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (-1, None, None), -1)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.],
|
|
[0., 0., 0., 0., 0.]],
|
|
[[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.]]],
|
|
[[[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.],
|
|
[0., 0., 0., 0., 0.]],
|
|
[[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.],
|
|
[1., 1., 1., 1., 1.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 3
|
|
lower = Tensor(np.array([[1], [0]]).astype(np.int64))
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, 0, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 4
|
|
lower = Tensor(np.array([1, 0]).astype(np.int64))
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, 0, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 5
|
|
lower = Tensor(np.array([[1, 0], [1, 0]]).astype(np.int64))
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, 0, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 6
|
|
lower = Tensor(np.array([[1, 0]]).astype(np.int64))
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, 1, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[0., 1., 1., 0., 0.],
|
|
[0., 0., 1., 1., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 7
|
|
lower = Tensor(np.array([[1, 0], [1, 0]]).astype(np.int32))
|
|
upper = Tensor(np.array([[1, 0], [1, 0]]).astype(np.int32))
|
|
output = F.vmap(F.matrix_band_part, (0, 0, 0), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 0., 0., 0., 0.],
|
|
[0., 1., 0., 0., 0.],
|
|
[0., 0., 1., 0., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 0., 0., 0., 0.],
|
|
[0., 1., 0., 0., 0.],
|
|
[0., 0., 1., 0., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 8
|
|
lower = Tensor(np.array([[1, -1], [1, 0]]).astype(np.int64))
|
|
upper = Tensor(np.array([[1, 0], [1, -1]]).astype(np.int64))
|
|
output = F.vmap(F.matrix_band_part, (0, 0, 0), 0)(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 0., 0., 0., 0.],
|
|
[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 1., 1., 1.],
|
|
[0., 1., 1., 1., 1.],
|
|
[0., 0., 1., 1., 1.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 9
|
|
x = Tensor(np.ones((2, 3, 5)).astype(np.float32))
|
|
lower = Tensor(np.array([[1], [1]]).astype(np.int64))
|
|
upper = 1
|
|
output = F.vmap(F.matrix_band_part, (0, 0, None), 0)(x, lower, upper)
|
|
expect_output = np.array([[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_matrix_band_part_dynamic_shape(mode):
|
|
"""
|
|
Feature: test matrix_band_part dynamic_shape feature.
|
|
Description: test matrix_band_part dynamic_shape feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="GPU")
|
|
x = Tensor(np.ones((2, 2, 3, 5)).astype(np.float32))
|
|
lower = Tensor(np.array([[1, 0], [1, 0]]).astype(np.int32))
|
|
upper = Tensor(np.array([[1, 0], [1, 0]]).astype(np.int32))
|
|
output = MatrixBandPartDynamicShapeNet()(x, lower, upper)
|
|
expect_output = np.array([[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 0., 0., 0., 0.],
|
|
[0., 1., 0., 0., 0.],
|
|
[0., 0., 1., 0., 0.]]],
|
|
[[[1., 1., 0., 0., 0.],
|
|
[1., 1., 1., 0., 0.],
|
|
[0., 1., 1., 1., 0.]],
|
|
[[1., 0., 0., 0., 0.],
|
|
[0., 1., 0., 0., 0.],
|
|
[0., 0., 1., 0., 0.]]]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|