wehub-resource-sync
367 lines
12 KiB
Python
367 lines
12 KiB
Python
# Copyright (c) 2023 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 convert_float_to_uint16, get_device_place, is_custom_device
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import paddle
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from paddle import base
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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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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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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 TestDiagonalScatterAPI(unittest.TestCase):
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def set_args(self):
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self.dtype = "float32"
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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.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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def set_api(self):
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self.ref_api = fill_gt
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self.paddle_api = paddle.diagonal_scatter
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def get_output(self):
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self.output = self.ref_api(
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self.x, self.y, self.offset, self.axis1, self.axis2
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)
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def setUp(self):
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# init the test case
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self.set_api()
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self.set_args()
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self.get_output()
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def test_dygraph(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x, self.dtype)
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y = paddle.to_tensor(self.y, self.dtype)
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result = paddle.diagonal_scatter(
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x, y, offset=self.offset, axis1=self.axis1, axis2=self.axis2
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)
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np.testing.assert_allclose(self.output, result.numpy(), rtol=1e-5)
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paddle.enable_static()
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def test_static(self):
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if self.dtype not in [
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"float16",
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"float32",
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"float64",
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"int16",
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"int32",
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"int64",
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"bool",
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"uint16",
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]:
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return
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paddle.enable_static()
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(startup_program, train_program):
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x = paddle.static.data(
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name="X", shape=self.x.shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="Y", shape=self.y.shape, dtype=self.dtype
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)
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out = paddle.diagonal_scatter(
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x, y, offset=self.offset, axis1=self.axis1, axis2=self.axis2
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)
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place = get_device_place()
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exe = base.Executor(place)
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result = exe.run(
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base.default_main_program(),
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feed={"X": self.x, "Y": self.y},
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fetch_list=[out],
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)
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np.testing.assert_allclose(self.output, result[0], rtol=1e-5)
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paddle.disable_static()
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# check the data type of the input
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class TestDiagonalScatterFloat16(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "float16"
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self.x = np.random.random([10, 10]).astype(np.float16)
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self.y = np.random.random([10]).astype(np.float16)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagonalScatterFloat64(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "float64"
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self.x = np.random.random([10, 10]).astype(np.float64)
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self.y = np.random.random([10]).astype(np.float64)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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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 or not support bfloat16",
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)
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class TestDiagonalScatterBFloat16(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "uint16"
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self.x = convert_float_to_uint16(
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np.random.random([10, 10]).astype(np.float32)
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)
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self.y = convert_float_to_uint16(
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np.random.random([10]).astype(np.float32)
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)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterUInt8(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "uint8"
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self.x = np.random.randint(0, 255, [10, 10]).astype(np.uint8)
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self.y = np.random.randint(0, 255, [10]).astype(np.uint8)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterInt8(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "int8"
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self.x = np.random.randint(-128, 127, [10, 10]).astype(np.int8)
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self.y = np.random.randint(-128, 127, [10]).astype(np.int8)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterInt16(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "int16"
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self.x = np.random.randint(-128, 127, [10, 10]).astype(np.int16)
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self.y = np.random.randint(-128, 127, [10]).astype(np.int16)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterInt32(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "int32"
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self.x = np.random.randint(-256, 255, [10, 10]).astype(np.int32)
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self.y = np.random.randint(-256, 255, [10]).astype(np.int32)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterInt64(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "int64"
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self.x = np.random.randint(-1024, 1023, [10, 10]).astype(np.int64)
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self.y = np.random.randint(-1024, 1023, [10]).astype(np.int64)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterBool(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "bool"
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self.x = np.random.randint(0, 1, [10, 10]).astype(np.bool_)
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self.y = np.random.randint(0, 1, [10]).astype(np.bool_)
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterComplex64(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "complex64"
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self.x = np.random.random([10, 10]).astype(np.float32)
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self.x = self.x + 1j * self.x
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self.y = np.random.random([10]).astype(np.float32)
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self.y = self.y + 1j * self.y
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterComplex128(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "complex128"
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self.x = np.random.random([10, 10]).astype(np.float64)
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self.x = self.x + 1j * self.x
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self.y = np.random.random([10]).astype(np.float64)
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self.y = self.y + 1j * self.y
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self.offset = 0
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self.axis1 = 0
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self.axis2 = 1
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# check offset, axis
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class TestDiagoalScatterOffset(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "float32"
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self.x = np.random.random([10, 10]).astype(np.float32)
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self.y = np.random.random([9]).astype(np.float32)
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self.offset = 1
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterOffset2(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "float32"
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self.x = np.random.random([10, 10]).astype(np.float32)
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self.y = np.random.random([8]).astype(np.float32)
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self.offset = -2
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self.axis1 = 0
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self.axis2 = 1
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class TestDiagoalScatterAxis1(TestDiagonalScatterAPI):
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def set_args(self):
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self.dtype = "float32"
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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.offset = 0
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self.axis1 = 1
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self.axis2 = 0
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# check error
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class TestDiagonalScatterError(TestDiagonalScatterAPI):
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def test_tensor_x_dimension_error(self):
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# Tensor x must be at least 2-dimensional in diagonal_scatter
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paddle.disable_static()
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x = paddle.to_tensor([1.0], "float32")
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y = paddle.to_tensor([], "float32")
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with self.assertRaises(AssertionError):
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paddle.diagonal_scatter(x, y)
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paddle.enable_static()
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def test_tensor_y_dimension_error(self):
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# y.shape should be (10,), but received (1,)
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paddle.disable_static()
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x = paddle.to_tensor(self.x, self.dtype)
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y = paddle.to_tensor([1.0], "float32")
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with self.assertRaises(AssertionError):
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paddle.diagonal_scatter(x, y)
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paddle.enable_static()
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def test_axis1_out_of_range_error(self):
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# axis1 is out of range in diagonal_scatter (expected to be in range of [-2, 2), but got 1000)
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paddle.disable_static()
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x = paddle.to_tensor(self.x, self.dtype)
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y = paddle.to_tensor(self.y, self.dtype)
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axis1 = 1000
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with self.assertRaises(AssertionError):
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paddle.diagonal_scatter(x, y, self.offset, axis1, self.axis2)
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paddle.enable_static()
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def test_axis2_out_of_range_error(self):
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# axis2 is out of range in diagonal_scatter (expected to be in range of [-2, 2), but got -1000)
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paddle.disable_static()
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x = paddle.to_tensor(self.x, self.dtype)
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y = paddle.to_tensor(self.y, self.dtype)
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axis2 = -1000
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with self.assertRaises(AssertionError):
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paddle.diagonal_scatter(x, y, self.offset, self.axis1, axis2)
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paddle.enable_static()
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def test_axis1_axis2_be_identical_error(self):
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# axis1 and axis2 should not be identical in diagonal_scatter, but received axis1 = 0, axis2 = 0
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paddle.disable_static()
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x = paddle.to_tensor(self.x, self.dtype)
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y = paddle.to_tensor(self.y, self.dtype)
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axis1 = 0
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axis2 = 0
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with self.assertRaises(AssertionError):
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paddle.diagonal_scatter(x, y, self.offset, axis1, axis2)
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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