forked from mindspore-Ecosystem/mindspore
311 lines
14 KiB
Python
311 lines
14 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""st for scipy.ops_wrapper."""
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import numpy as onp
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import pytest
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import mindspore.scipy.ops_wrapper as ops_wrapper
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from mindspore import context, Tensor
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from tests.st.scipy_st.utils import match_array
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DEFAULT_ALIGNMENT = "LEFT_LEFT"
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ALIGNMENT_LIST = ["RIGHT_LEFT", "LEFT_RIGHT", "LEFT_LEFT", "RIGHT_RIGHT"]
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def pack_diagonals_in_matrix(matrix, num_rows, num_cols, alignment=None):
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if alignment == DEFAULT_ALIGNMENT or alignment is None:
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return matrix
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packed_matrix = dict()
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for diag_index, (diagonals, padded_diagonals) in matrix.items():
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align = alignment.split("_")
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d_lower, d_upper = diag_index
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batch_dims = diagonals.ndim - (2 if d_lower < d_upper else 1)
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max_diag_len = diagonals.shape[-1]
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index = (slice(None),) * batch_dims
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packed_diagonals = onp.zeros_like(diagonals)
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for d_index in range(d_lower, d_upper + 1):
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diag_len = min(num_rows + min(0, d_index), num_cols - max(0, d_index))
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row_index = d_upper - d_index
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padding_len = max_diag_len - diag_len
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left_align = (d_index >= 0 and
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align[0] == "LEFT") or (d_index <= 0 and
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align[1] == "LEFT")
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extra_dim = tuple() if d_lower == d_upper else (row_index,)
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packed_last_dim = (slice(None),) if left_align else (slice(0, diag_len, 1),)
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repacked_last_dim = (slice(None),) if left_align else (slice(
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padding_len, max_diag_len, 1),)
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packed_index = index + extra_dim + packed_last_dim
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repacked_index = index + extra_dim + repacked_last_dim
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packed_diagonals[repacked_index] = diagonals[packed_index]
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packed_matrix[diag_index] = (packed_diagonals, padded_diagonals)
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return packed_matrix
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def square_matrix(alignment=None, data_type=None):
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mat = onp.array([[[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 1],
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[3, 4, 5, 6, 7],
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[8, 9, 1, 2, 3],
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[4, 5, 6, 7, 8]],
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[[9, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 1],
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[2, 3, 4, 5, 6]]], dtype=data_type)
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num_rows, num_cols = mat.shape[-2:]
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tests = dict()
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# tests[d_lower, d_upper] = packed_diagonals
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tests[-1, -1] = (onp.array([[6, 4, 1, 7],
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[5, 2, 8, 5]], dtype=data_type),
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onp.array([[[0, 0, 0, 0, 0],
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[6, 0, 0, 0, 0],
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[0, 4, 0, 0, 0],
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[0, 0, 1, 0, 0],
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[0, 0, 0, 7, 0]],
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[[0, 0, 0, 0, 0],
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[5, 0, 0, 0, 0],
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[0, 2, 0, 0, 0],
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[0, 0, 8, 0, 0],
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[0, 0, 0, 5, 0]]], dtype=data_type))
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tests[-4, -3] = (onp.array([[[8, 5],
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[4, 0]],
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[[6, 3],
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[2, 0]]], dtype=data_type),
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onp.array([[[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[8, 0, 0, 0, 0],
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[4, 5, 0, 0, 0]],
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[[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[6, 0, 0, 0, 0],
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[2, 3, 0, 0, 0]]], dtype=data_type))
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tests[-2, 1] = (onp.array([[[2, 8, 6, 3, 0],
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[1, 7, 5, 2, 8],
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[6, 4, 1, 7, 0],
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[3, 9, 6, 0, 0]],
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[[1, 7, 4, 1, 0],
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[9, 6, 3, 9, 6],
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[5, 2, 8, 5, 0],
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[1, 7, 4, 0, 0]]], dtype=data_type),
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onp.array([[[1, 2, 0, 0, 0],
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[6, 7, 8, 0, 0],
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[3, 4, 5, 6, 0],
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[0, 9, 1, 2, 3],
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[0, 0, 6, 7, 8]],
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[[9, 1, 0, 0, 0],
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[5, 6, 7, 0, 0],
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[1, 2, 3, 4, 0],
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[0, 7, 8, 9, 1],
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[0, 0, 4, 5, 6]]], dtype=data_type))
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tests[2, 4] = (onp.array([[[5, 0, 0],
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[4, 1, 0],
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[3, 9, 7]],
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[[4, 0, 0],
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[3, 9, 0],
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[2, 8, 5]]], dtype=data_type),
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onp.array([[[0, 0, 3, 4, 5],
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[0, 0, 0, 9, 1],
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[0, 0, 0, 0, 7],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]],
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[[0, 0, 2, 3, 4],
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[0, 0, 0, 8, 9],
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[0, 0, 0, 0, 5],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]]], dtype=data_type))
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return mat, pack_diagonals_in_matrix(tests, num_rows, num_cols, alignment)
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def tall_matrix(alignment=None, data_type=None):
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mat = onp.array([[[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9],
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[9, 8, 7],
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[6, 5, 4]],
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[[3, 2, 1],
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[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9],
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[9, 8, 7]]], dtype=data_type)
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num_rows, num_cols = mat.shape[-2:]
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tests = dict()
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tests[0, 0] = (onp.array([[1, 5, 9],
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[3, 2, 6]], dtype=data_type),
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onp.array([[[1, 0, 0],
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[0, 5, 0],
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[0, 0, 9],
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[0, 0, 0]],
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[[3, 0, 0],
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[0, 2, 0],
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[0, 0, 6],
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[0, 0, 0]]], dtype=data_type))
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tests[-4, -3] = (onp.array([[[9, 5],
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[6, 0]],
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[[7, 8],
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[9, 0]]], dtype=data_type),
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onp.array([[[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[9, 0, 0],
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[6, 5, 0]],
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[[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[7, 0, 0],
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[9, 8, 0]]], dtype=data_type))
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tests[-2, -1] = (onp.array([[[4, 8, 7],
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[7, 8, 4]],
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[[1, 5, 9],
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[4, 8, 7]]], dtype=data_type),
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onp.array([[[0, 0, 0],
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[4, 0, 0],
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[7, 8, 0],
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[0, 8, 7],
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[0, 0, 4]],
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[[0, 0, 0],
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[1, 0, 0],
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[4, 5, 0],
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[0, 8, 9],
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[0, 0, 7]]], dtype=data_type))
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tests[-2, 1] = (onp.array([[[2, 6, 0],
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[1, 5, 9],
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[4, 8, 7],
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[7, 8, 4]],
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[[2, 3, 0],
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[3, 2, 6],
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[1, 5, 9],
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[4, 8, 7]]], dtype=data_type),
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onp.array([[[1, 2, 0],
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[4, 5, 6],
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[7, 8, 9],
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[0, 8, 7],
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[0, 0, 4]],
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[[3, 2, 0],
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[1, 2, 3],
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[4, 5, 6],
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[0, 8, 9],
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[0, 0, 7]]], dtype=data_type))
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tests[1, 2] = (onp.array([[[3, 0],
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[2, 6]],
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[[1, 0],
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[2, 3]]], dtype=data_type),
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onp.array([[[0, 2, 3],
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[0, 0, 6],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]],
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[[0, 2, 1],
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[0, 0, 3],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]]], dtype=data_type))
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return mat, pack_diagonals_in_matrix(tests, num_rows, num_cols, alignment)
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def fat_matrix(alignment=None, data_type=None):
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mat = onp.array([[[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 1, 2, 3]],
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[[4, 5, 6, 7],
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[8, 9, 1, 2],
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[3, 4, 5, 6]]], dtype=data_type)
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num_rows, num_cols = mat.shape[-2:]
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tests = dict()
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tests[2, 2] = (onp.array([[3, 8],
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[6, 2]], dtype=data_type),
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onp.array([[[0, 0, 3, 0],
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[0, 0, 0, 8],
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[0, 0, 0, 0]],
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[[0, 0, 6, 0],
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[0, 0, 0, 2],
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[0, 0, 0, 0]]], dtype=data_type))
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tests[-2, 0] = (onp.array([[[1, 6, 2],
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[5, 1, 0],
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[9, 0, 0]],
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[[4, 9, 5],
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[8, 4, 0],
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[3, 0, 0]]], dtype=data_type),
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onp.array([[[1, 0, 0, 0],
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[5, 6, 0, 0],
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[9, 1, 2, 0]],
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[[4, 0, 0, 0],
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[8, 9, 0, 0],
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[3, 4, 5, 0]]], dtype=data_type))
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tests[-1, 1] = (onp.array([[[2, 7, 3],
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[1, 6, 2],
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[5, 1, 0]],
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[[5, 1, 6],
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[4, 9, 5],
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[8, 4, 0]]], dtype=data_type),
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onp.array([[[1, 2, 0, 0],
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[5, 6, 7, 0],
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[0, 1, 2, 3]],
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[[4, 5, 0, 0],
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[8, 9, 1, 0],
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[0, 4, 5, 6]]], dtype=data_type))
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tests[0, 3] = (onp.array([[[4, 0, 0],
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[3, 8, 0],
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[2, 7, 3],
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[1, 6, 2]],
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[[7, 0, 0],
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[6, 2, 0],
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[5, 1, 6],
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[4, 9, 5]]], dtype=data_type),
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onp.array([[[1, 2, 3, 4],
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[0, 6, 7, 8],
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[0, 0, 2, 3]],
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[[4, 5, 6, 7],
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[0, 9, 1, 2],
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[0, 0, 5, 6]]], dtype=data_type))
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return mat, pack_diagonals_in_matrix(tests, num_rows, num_cols, alignment)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('data_type', [onp.int32, onp.int64, onp.float32, onp.float64])
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def test_matrix_set_diag(data_type):
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"""
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Feature: ALL TO ALL
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Description: test geneal matrix cases for matrix_set_diag in pynative or graph mode
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Expectation: the result match expected_diag_matrix.
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"""
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onp.random.seed(0)
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context.set_context(mode=context.PYNATIVE_MODE)
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for align in ALIGNMENT_LIST:
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for _, tests in [square_matrix(align, data_type), tall_matrix(align, data_type), fat_matrix(align, data_type)]:
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for k_vec, (diagonal, banded_mat) in tests.items():
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mask = banded_mat[0] == 0
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input_mat = onp.random.randint(10, size=mask.shape).astype(dtype=data_type)
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expected_diag_matrix = input_mat * mask + banded_mat[0]
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output = ops_wrapper.matrix_set_diag(
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Tensor(input_mat), Tensor(diagonal[0]), k=k_vec, alignment=align)
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match_array(output.asnumpy(), expected_diag_matrix)
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context.set_context(mode=context.GRAPH_MODE)
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for align in ALIGNMENT_LIST:
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for _, tests in [square_matrix(align, data_type), tall_matrix(align, data_type), fat_matrix(align, data_type)]:
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for k_vec, (diagonal, banded_mat) in tests.items():
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mask = banded_mat[0] == 0
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input_mat = onp.random.randint(10, size=mask.shape).astype(dtype=data_type)
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expected_diag_matrix = input_mat * mask + banded_mat[0]
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output = ops_wrapper.matrix_set_diag(
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Tensor(input_mat), Tensor(diagonal[0]), k=k_vec, alignment=align)
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match_array(output.asnumpy(), expected_diag_matrix)
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