wehub-resource-sync
211 lines
6.4 KiB
Python
211 lines
6.4 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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def fill_diagonal_ndarray(x, value, offset=0, dim1=0, dim2=1):
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"""Fill value into the diagonal of x that offset is ${offset} and the coordinate system is (dim1, dim2)."""
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strides = x.strides
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shape = x.shape
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if dim1 > dim2:
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dim1, dim2 = dim2, dim1
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assert 0 <= dim1 < dim2 <= 2
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assert len(x.shape) == 3
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dim_sum = dim1 + dim2
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dim3 = len(x.shape) - dim_sum
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if offset >= 0:
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diagdim = min(shape[dim1], shape[dim2] - offset)
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diagonal = np.lib.stride_tricks.as_strided(
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x[:, offset:] if dim_sum == 1 else x[:, :, offset:],
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shape=(shape[dim3], diagdim),
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strides=(strides[dim3], strides[dim1] + strides[dim2]),
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)
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else:
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diagdim = min(shape[dim2], shape[dim1] + offset)
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diagonal = np.lib.stride_tricks.as_strided(
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x[-offset:, :] if dim_sum in [1, 2] else x[:, -offset:],
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shape=(shape[dim3], diagdim),
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strides=(strides[dim3], strides[dim1] + strides[dim2]),
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)
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diagonal[...] = value
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return x
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def fill_gt(x, y, offset, dim1, dim2):
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if dim1 > dim2:
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dim1, dim2 = dim2, dim1
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offset = -offset
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xshape = x.shape
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yshape = y.shape
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if len(xshape) != 3:
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perm_list = []
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unperm_list = [0] * len(xshape)
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idx = 0
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for i in range(len(xshape)):
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if i != dim1 and i != dim2:
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perm_list.append(i)
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unperm_list[i] = idx
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idx += 1
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perm_list += [dim1, dim2]
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unperm_list[dim1] = idx
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unperm_list[dim2] = idx + 1
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x = np.transpose(x, perm_list)
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y = y.reshape(-1, yshape[-1])
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nxshape = x.shape
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x = x.reshape((-1, xshape[dim1], xshape[dim2]))
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out = fill_diagonal_ndarray(x, y, offset, 1, 2)
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if len(xshape) != 3:
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out = out.reshape(nxshape)
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out = np.transpose(out, unperm_list)
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return out
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class TensorFillDiagTensor_Test(OpTest):
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def setUp(self):
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self.op_type = "fill_diagonal_tensor"
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self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
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self.init_kernel_type()
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x = np.random.random((10, 10)).astype(self.dtype)
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y = np.random.random((10,)).astype(self.dtype)
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dim1 = 0
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dim2 = 1
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offset = 0
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out = fill_gt(x, y, offset, dim1, dim2)
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self.inputs = {"X": x, "Y": y}
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self.outputs = {'Out': out}
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self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
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def init_kernel_type(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class TensorFillDiagTensor_Test2(TensorFillDiagTensor_Test):
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def setUp(self):
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self.op_type = "fill_diagonal_tensor"
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self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
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self.init_kernel_type()
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x = np.random.random((2, 20, 25)).astype(self.dtype)
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y = np.random.random((2, 20)).astype(self.dtype)
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dim1 = 2
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dim2 = 1
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offset = -3
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out = fill_gt(x, y, offset, dim1, dim2)
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self.inputs = {"X": x, "Y": y}
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self.outputs = {'Out': out}
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self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
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def init_kernel_type(self):
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self.dtype = np.float32
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class TensorFillDiagTensor_Test3(TensorFillDiagTensor_Test):
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def setUp(self):
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self.op_type = "fill_diagonal_tensor"
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self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
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self.init_kernel_type()
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x = np.random.random((2, 20, 20, 3)).astype(self.dtype)
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y = np.random.random((2, 3, 18)).astype(self.dtype)
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dim1 = 1
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dim2 = 2
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offset = 2
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out = fill_gt(x, y, offset, dim1, dim2)
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self.inputs = {"X": x, "Y": y}
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self.outputs = {'Out': out}
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self.attrs = {"offset": offset, "dim1": dim1, "dim2": dim2}
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def init_kernel_type(self):
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self.dtype = np.float16
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class TensorFillDiagTensorFP16OP(TensorFillDiagTensor_Test):
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def init_kernel_type(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TensorFillDiagTensorBF16(OpTest):
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def setUp(self):
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self.op_type = "fill_diagonal_tensor"
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self.python_api = paddle.tensor.manipulation.fill_diagonal_tensor
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self.init_kernel_type()
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self.init_config()
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self.init_input_output()
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def init_kernel_type(self):
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self.dtype = np.uint16
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def init_config(self):
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self.x = np.random.random((10, 10)).astype(np.float32)
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self.y = np.random.random((10,)).astype(np.float32)
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self.dim1 = 0
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self.dim2 = 1
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self.offset = 0
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def init_input_output(self):
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out = fill_gt(self.x, self.y, self.offset, self.dim1, self.dim2)
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self.inputs = {
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"X": convert_float_to_uint16(self.x),
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"Y": convert_float_to_uint16(self.y),
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}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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self.attrs = {
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"offset": self.offset,
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"dim1": self.dim1,
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"dim2": self.dim2,
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}
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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