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- # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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 unittest
-
- import numpy as np
- from get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test import skip_check_grad_ci
- from op_test_xpu import XPUOpTest
-
- import paddle
-
- paddle.enable_static()
-
-
- def fill_diagonal_ndarray(x, value, offset=0, dim1=0, dim2=1):
- """Fill value into the diagonal of x that offset is ${offset} and the coordinate system is (dim1, dim2)."""
- strides = x.strides
- shape = x.shape
- if dim1 > dim2:
- dim1, dim2 = dim2, dim1
- assert 0 <= dim1 < dim2 <= 2
- assert len(x.shape) == 3
-
- dim_sum = dim1 + dim2
- dim3 = len(x.shape) - dim_sum
- if offset >= 0:
- diagdim = min(shape[dim1], shape[dim2] - offset)
- diagonal = np.lib.stride_tricks.as_strided(
- x[:, offset:] if dim_sum == 1 else x[:, :, offset:],
- shape=(shape[dim3], diagdim),
- strides=(strides[dim3], strides[dim1] + strides[dim2]),
- )
- else:
- diagdim = min(shape[dim2], shape[dim1] + offset)
- diagonal = np.lib.stride_tricks.as_strided(
- x[-offset:, :] if dim_sum in [1, 2] else x[:, -offset:],
- shape=(shape[dim3], diagdim),
- strides=(strides[dim3], strides[dim1] + strides[dim2]),
- )
-
- diagonal[...] = value
- return x
-
-
- def fill_gt(x, y, offset, dim1, dim2):
- if dim1 > dim2:
- dim1, dim2 = dim2, dim1
- offset = -offset
- xshape = x.shape
- yshape = y.shape
- if len(xshape) != 3:
- perm_list = []
- unperm_list = [0] * len(xshape)
- idx = 0
-
- for i in range(len(xshape)):
- if i != dim1 and i != dim2:
- perm_list.append(i)
- unperm_list[i] = idx
- idx += 1
- perm_list += [dim1, dim2]
- unperm_list[dim1] = idx
- unperm_list[dim2] = idx + 1
-
- x = np.transpose(x, perm_list)
- y = y.reshape(-1, yshape[-1])
- nxshape = x.shape
- x = x.reshape((-1, xshape[dim1], xshape[dim2]))
- out = fill_diagonal_ndarray(x, y, offset, 1, 2)
-
- if len(xshape) != 3:
- out = out.reshape(nxshape)
- out = np.transpose(out, unperm_list)
- return out
-
-
- class XPUTestFillDiagTensorOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'fill_diagonal_tensor'
- self.use_dynamic_create_class = False
-
- @skip_check_grad_ci(
- reason="xpu fill_diagonal_tensor is not implemented yet"
- )
- class TensorFillDiagTensor_Test(XPUOpTest):
- def setUp(self):
- self.op_type = "fill_diagonal_tensor"
- self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
- self.init_kernel_type()
- x = np.random.random((10, 10)).astype(self.dtype)
- y = np.random.random((10,)).astype(self.dtype)
- dim1 = 0
- dim2 = 1
- offset = 0
- out = fill_gt(x, y, offset, dim1, dim2)
-
- self.inputs = {"X": x, "Y": y}
- self.outputs = {'Out': out}
- self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
-
- def init_kernel_type(self):
- self.dtype = self.in_type
-
- def test_check_output(self):
- self.check_output_with_place(paddle.XPUPlace(0))
-
- class TensorFillDiagTensor_Test2(TensorFillDiagTensor_Test):
- def setUp(self):
- self.op_type = "fill_diagonal_tensor"
- self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
- self.init_kernel_type()
- x = np.random.random((2, 20, 25)).astype(self.dtype)
- y = np.random.random((2, 20)).astype(self.dtype)
- dim1 = 2
- dim2 = 1
- offset = -3
- out = fill_gt(x, y, offset, dim1, dim2)
-
- self.inputs = {"X": x, "Y": y}
- self.outputs = {'Out': out}
- self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
-
- class TensorFillDiagTensor_Test3(TensorFillDiagTensor_Test):
- def setUp(self):
- self.op_type = "fill_diagonal_tensor"
- self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
- self.init_kernel_type()
- x = np.random.random((2, 20, 20, 3)).astype(self.dtype)
- y = np.random.random((2, 3, 18)).astype(self.dtype)
- dim1 = 1
- dim2 = 2
- offset = 2
- out = fill_gt(x, y, offset, dim1, dim2)
-
- self.inputs = {"X": x, "Y": y}
- self.outputs = {'Out': out}
- self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
-
-
- support_types = get_xpu_op_support_types('fill_diagonal_tensor')
- for stype in support_types:
- create_test_class(globals(), XPUTestFillDiagTensorOp, stype)
-
- if __name__ == '__main__':
- paddle.enable_static()
- unittest.main()
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