wehub-resource-sync
442 lines
16 KiB
Python
442 lines
16 KiB
Python
# Copyright (c) 2021 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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get_places,
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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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paddle.enable_static()
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class TestSearchSorted(OpTest):
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def setUp(self):
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self.python_api = paddle.searchsorted
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self.op_type = "searchsorted"
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self.init_test_case()
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self.inputs = {
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'SortedSequence': self.sorted_sequence,
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'Values': self.values,
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}
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self.attrs = {"out_int32": False, "right": False}
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self.attrs["right"] = True if self.side == 'right' else False
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self.outputs = {
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'Out': np.searchsorted(
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self.sorted_sequence, self.values, side=self.side
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)
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}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def init_shape(self):
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self.shape = None
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def init_test_case(self):
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self.init_shape()
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if self.shape is None:
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("float32")
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else:
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self.sorted_sequence = np.random.randn(*self.shape).astype(
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"float32"
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)
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype("float32")
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self.side = "left"
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class TestSearchSortedOp1(TestSearchSorted):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("int32")
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype("int32")
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self.side = "right"
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class TestSearchSortedOp2(TestSearchSorted):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("int64")
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype("int64")
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self.side = "left"
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class TestSearchSortedOp3(TestSearchSorted):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("float64")
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self.values = np.array([[np.nan, np.nan, np.nan], [3, 6, 9]]).astype(
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"float64"
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)
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self.side = "left"
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class TestSearchSortedOp4(TestSearchSorted):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("float64")
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self.values = np.array([[np.inf, np.inf, np.inf], [3, 6, 9]]).astype(
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"float64"
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)
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self.side = "right"
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class TestSearchSortedOp5(TestSearchSorted):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype("float64")
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self.values = np.array(
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[[np.inf, np.inf, np.inf], [np.nan, np.nan, np.nan]]
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).astype("float64")
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self.side = "right"
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class TestSearchSorted_ZeroSize(TestSearchSorted):
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def init_shape(self):
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self.shape = (0,)
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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_float16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the float16",
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)
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class TestSearchSortedFP16OP(TestSearchSorted):
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def setUp(self):
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self.python_api = paddle.searchsorted
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self.op_type = "searchsorted"
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self.dtype = np.float16
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self.init_test_case()
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self.inputs = {
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'SortedSequence': self.sorted_sequence.astype(self.dtype),
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'Values': self.values.astype(self.dtype),
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}
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self.attrs = {"out_int32": False, "right": False}
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self.attrs["right"] = True if self.side == 'right' else False
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self.outputs = {
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'Out': np.searchsorted(
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self.sorted_sequence, self.values, side=self.side
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)
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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 init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9])
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self.values = np.array([[3, 6, 9], [3, 6, 9]])
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self.side = "left"
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class TestSearchSortedFP16OP_2(TestSearchSortedFP16OP):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9])
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self.values = np.array([[3, 6, 9], [3, 6, 9]])
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self.side = "right"
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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 TestSearchSortedBF16(TestSearchSorted):
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def setUp(self):
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self.python_api = paddle.searchsorted
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self.public_python_api = paddle.searchsorted
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self.op_type = "searchsorted"
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self.python_out_sig = ["Out"]
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.init_test_case()
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self.inputs = {
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'SortedSequence': convert_float_to_uint16(self.sorted_sequence),
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'Values': convert_float_to_uint16(self.values),
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}
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self.attrs = {"out_int32": False, "right": False}
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self.attrs["right"] = True if self.side == 'right' else False
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self.outputs = {
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'Out': np.searchsorted(
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self.sorted_sequence, self.values, side=self.side
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)
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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 init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype(self.np_dtype)
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype(self.np_dtype)
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self.side = "left"
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class TestSearchSortedBF16_2(TestSearchSortedBF16):
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def init_test_case(self):
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self.sorted_sequence = np.array([1, 3, 5, 7, 9]).astype(self.np_dtype)
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype(self.np_dtype)
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self.side = "right"
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class TestSearchSortedAPI(unittest.TestCase):
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def init_test_case(self):
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self.sorted_sequence = np.array([2, 4, 6, 8, 10]).astype("float64")
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self.values = np.array([[3, 6, 9], [3, 6, 9]]).astype("float64")
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def setUp(self):
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self.init_test_case()
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self.place = get_places()
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def test_static_api(self):
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paddle.enable_static()
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def run(place):
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence',
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shape=self.sorted_sequence.shape,
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dtype="float64",
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)
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values = paddle.static.data(
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'Values', shape=self.values.shape, dtype="float64"
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)
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out = paddle.searchsorted(sorted_sequence, values)
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exe = paddle.static.Executor(place)
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(res,) = exe.run(
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feed={
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'SortedSequence': self.sorted_sequence,
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'Values': self.values,
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},
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fetch_list=out,
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)
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out_ref = np.searchsorted(self.sorted_sequence, self.values)
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np.testing.assert_allclose(out_ref, res, rtol=1e-05)
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for place in self.place:
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run(place)
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def test_dygraph_api(self):
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def run(place):
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with paddle.base.dygraph.guard():
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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values = paddle.to_tensor(self.values)
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out = paddle.searchsorted(sorted_sequence, values, right=True)
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out_ref = np.searchsorted(
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self.sorted_sequence, self.values, side='right'
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)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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for place in self.place:
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run(place)
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def test_out_int32(self):
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paddle.disable_static()
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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values = paddle.to_tensor(self.values)
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out = paddle.searchsorted(sorted_sequence, values, out_int32=True)
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self.assertTrue(out.type, 'int32')
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class TestSearchSortedError(unittest.TestCase):
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def test_error_api(self):
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paddle.enable_static()
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def test_searchsorted_dims_matched_before_lastdim_error1():
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence', shape=[2, 2, 3], dtype="float64"
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)
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values = paddle.static.data(
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'Values', shape=[2, 5], dtype="float64"
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)
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out = paddle.searchsorted(sorted_sequence, values)
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self.assertRaises(
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RuntimeError, test_searchsorted_dims_matched_before_lastdim_error1
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)
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def test_searchsorted_dims_matched_before_lastdim_error2():
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence', shape=[2, 2, 3], dtype="float64"
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)
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values = paddle.static.data(
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'Values', shape=[2, 3, 5], dtype="float64"
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)
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out = paddle.searchsorted(sorted_sequence, values)
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self.assertRaises(
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RuntimeError, test_searchsorted_dims_matched_before_lastdim_error2
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)
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def test_searchsorted_sortedsequence_size_error():
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence', shape=[2, 2, pow(2, 34)], dtype="float64"
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)
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values = paddle.static.data(
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'Values', shape=[2, 2, 5], dtype="float64"
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)
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out = paddle.searchsorted(
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sorted_sequence, values, out_int32=True
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)
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self.assertRaises(
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RuntimeError, test_searchsorted_sortedsequence_size_error
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)
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def test_check_type_error(self):
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paddle.enable_static()
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def test_sortedsequence_values_type_error():
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence', shape=[2, 3], dtype="int16"
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)
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values = paddle.static.data(
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'Values', shape=[2, 5], dtype="int16"
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)
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out = paddle.searchsorted(sorted_sequence, values)
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self.assertRaises(TypeError, test_sortedsequence_values_type_error)
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class TestSearchSortedAPI_Extended(unittest.TestCase):
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def init_test_case(self):
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self.sorted_sequence = np.array([2, 4, 6, 8, 10]).astype("float64")
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self.values_2d = np.array([[3, 6, 9], [3, 6, 9]]).astype("float64")
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self.values_1d = np.array([3, 6, 9]).astype("float64")
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self.unsorted_seq = np.array([6, 2, 10, 4, 8]).astype("float64")
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# sorter such that unsorted_seq[sorter] is sorted
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self.sorter = np.argsort(self.unsorted_seq).astype("int64")
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def setUp(self):
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self.init_test_case()
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self.place = get_places()
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def test_dygraph_side_and_right_priority_and_out_int32(self):
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# Test: side takes precedence over right, out_int32 controls the returned dtype
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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vals = paddle.to_tensor(self.values_2d)
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# Mixed parameter passing: right=False, side='right' -> side takes precedence, should be interpreted as right=True
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out = paddle.searchsorted(
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seq, vals, right=False, side="right", out_int32=True
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)
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ref = np.searchsorted(
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self.sorted_sequence, self.values_2d, side="right"
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)
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self.assertEqual(out.dtype, paddle.int32)
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np.testing.assert_allclose(
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ref, out.numpy().astype("int64"), rtol=1e-05
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)
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def test_dygraph_out_parameter_and_return_is_out(self):
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# Test out parameter: Pass in an existing tensor, write the function on it, and return the same Tensor
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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vals = paddle.to_tensor(self.values_2d)
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out_tensor = paddle.empty(
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shape=self.values_2d.shape, dtype="int64"
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)
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ret = paddle.searchsorted(seq, vals, out=out_tensor)
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ref = np.searchsorted(self.sorted_sequence, self.values_2d)
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np.testing.assert_allclose(ref, out_tensor.numpy(), rtol=1e-05)
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def test_dygraph_sorter_behavior(self):
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# Test sorter parameter: When the sequence is unsorted but a sorter is given, the behavior is consistent with numpy
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.unsorted_seq)
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vals = paddle.to_tensor(self.values_1d)
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sorter_t = paddle.to_tensor(self.sorter)
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out = paddle.searchsorted(seq, vals, sorter=sorter_t)
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ref = np.searchsorted(
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self.unsorted_seq, self.values_1d, sorter=self.sorter
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)
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np.testing.assert_allclose(ref, out.numpy(), rtol=1e-05)
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def test_static_side_and_sorter(self):
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# Test side parameters and sorter parameters under static images (aligned with numpy)
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paddle.enable_static()
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for place in self.place:
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with paddle.static.program_guard(paddle.static.Program()):
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seq = paddle.static.data(
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name="seq", shape=self.unsorted_seq.shape, dtype="float64"
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)
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vals = paddle.static.data(
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name="vals", shape=self.values_1d.shape, dtype="float64"
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)
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sorter = paddle.static.data(
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name="sorter", shape=self.sorter.shape, dtype="int64"
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)
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out_left = paddle.searchsorted(
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seq, vals, side="left", sorter=sorter
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)
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out_right = paddle.searchsorted(
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seq, vals, side="right", sorter=sorter
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)
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exe = paddle.static.Executor(place)
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(res_left, res_right) = exe.run(
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feed={
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"seq": self.unsorted_seq,
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"vals": self.values_1d,
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"sorter": self.sorter,
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},
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fetch_list=[out_left, out_right],
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)
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ref_left = np.searchsorted(
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self.unsorted_seq,
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self.values_1d,
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side="left",
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sorter=self.sorter,
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)
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ref_right = np.searchsorted(
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self.unsorted_seq,
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self.values_1d,
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side="right",
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sorter=self.sorter,
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)
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np.testing.assert_allclose(ref_left, res_left, rtol=1e-05)
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np.testing.assert_allclose(ref_right, res_right, rtol=1e-05)
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paddle.disable_static()
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def test_dygraph_1d_values_and_name_param(self):
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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vals = paddle.to_tensor(self.values_1d)
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out = paddle.searchsorted(seq, vals, name="my_search")
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ref = np.searchsorted(self.sorted_sequence, self.values_1d)
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np.testing.assert_allclose(ref, out.numpy(), rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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