chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 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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from utils import dygraph_guard
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import paddle
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from paddle import base
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from paddle.base import Program, program_guard
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def call_nonzero(x):
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input = paddle.to_tensor(x)
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return paddle.nonzero(x=input)
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class TestNonZeroAPI(unittest.TestCase):
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def test_nonzero_api_as_tuple(self):
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paddle.enable_static()
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data = np.array([[1, 0], [0, 1]], dtype='float32')
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[-1, 2], dtype='float32')
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.nonzero(x, as_tuple=True)
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self.assertEqual(type(y), tuple)
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self.assertEqual(len(y), 2)
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z = paddle.concat(list(y), axis=0)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': data}, fetch_list=[z], return_numpy=False
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)
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expect_out = np.array([0, 1, 0, 1])
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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data = np.array([1, 1, 0], dtype="float32")
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[-1], dtype='float32')
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.nonzero(x, as_tuple=True)
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self.assertEqual(type(y), tuple)
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self.assertEqual(len(y), 1)
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z = paddle.concat(list(y), axis=0)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': data}, fetch_list=[z], return_numpy=False
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)
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expect_out = np.array([0, 1])
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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data = np.zeros([10, 3, 0], dtype="float32")
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[10, 3, 0], dtype='float32')
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.nonzero(x, as_tuple=True)
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self.assertEqual(type(y), tuple)
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self.assertEqual(len(y), 3)
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expect_out = np.zeros([0])
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for item in y:
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np.testing.assert_array_equal(expect_out, item)
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def test_nonzero_api(self):
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paddle.enable_static()
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data = np.array([[1, 0], [0, 1]], dtype="float32")
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[-1, 2], dtype='float32')
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.nonzero(x)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': data}, fetch_list=[y], return_numpy=False
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)
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expect_out = np.array([[0, 0], [1, 1]])
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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data = np.array([1, 1, 0], dtype="float32")
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[-1], dtype='float32')
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.nonzero(x)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': data}, fetch_list=[y], return_numpy=False
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)
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expect_out = np.array([[0], [1]])
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_dygraph_api(self):
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data_x = np.array([[True, False], [False, True]])
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with base.dygraph.guard():
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x = paddle.to_tensor(data_x)
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z = paddle.nonzero(x)
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np_z = z.numpy()
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expect_out = np.array([[0, 0], [1, 1]])
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# Base case
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class TestNonzeroOp(OpTest):
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def setUp(self):
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'''Test where_index op with random value'''
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np.random.seed(2023)
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self.op_type = "where_index"
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self.python_api = call_nonzero
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self.init_shape()
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self.init_dtype()
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self.inputs = self.create_inputs()
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self.outputs = self.return_outputs()
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def init_shape(self):
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self.shape = [8, 8]
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def init_dtype(self):
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self.dtype = np.float64
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def create_inputs(self):
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return {
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'Condition': np.random.randint(5, size=self.shape).astype(
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self.dtype
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)
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}
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def return_outputs(self):
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return {'Out': np.transpose(np.nonzero(self.inputs['Condition']))}
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class TestNonzeroComplex64Op(TestNonzeroOp):
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def init_shape(self):
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self.shape = [1, 2, 3]
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def init_dtype(self):
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self.dtype = np.complex64
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class TestNonzeroComplex128Op(TestNonzeroOp):
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def init_shape(self):
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self.shape = [1, 2, 3]
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def init_dtype(self):
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self.dtype = np.complex128
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class TestNonzeroFP32Op(TestNonzeroOp):
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def init_shape(self):
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self.shape = [2, 10, 2]
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def init_dtype(self):
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self.dtype = np.float32
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class TestNonzeroFP16Op(TestNonzeroOp):
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def init_shape(self):
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self.shape = [3, 4, 7]
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def init_dtype(self):
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self.dtype = np.float16
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class TestNonzeroBF16(OpTest):
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def setUp(self):
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'''Test where_index op with bfloat16 dtype'''
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np.random.seed(2023)
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self.op_type = "where_index"
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self.python_api = call_nonzero
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self.init_shape()
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self.init_dtype()
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self.inputs = self.create_inputs()
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self.outputs = self.return_outputs()
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def init_shape(self):
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self.shape = [12, 9]
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def init_dtype(self):
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self.dtype = np.uint16
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def create_inputs(self):
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return {
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'Condition': convert_float_to_uint16(
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np.random.randint(5, size=self.shape).astype(np.float32)
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)
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}
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def return_outputs(self):
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return {'Out': np.transpose(np.nonzero(self.inputs['Condition']))}
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class TestZeroSizeOp(TestNonzeroOp):
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def init_shape(self):
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self.shape = [0, 10]
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def init_dtype(self):
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self.dtype = np.float64
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class TestZeroSizeOpCase2(TestNonzeroOp):
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def init_shape(self):
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self.shape = [0, 10]
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def init_dtype(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, check_symbol_infer=True)
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class TestNonzeroCompatibility(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.CPUPlace()]
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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self.places.append(get_device_place())
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self.input_data = [[1, 0, 3], [0, 5, 0], [7, 0, 9]]
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self.expected_indices = np.array(
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[[0, 0], [0, 2], [1, 1], [2, 0], [2, 2]]
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)
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def test_nonzero_with_param_aliases(self):
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with dygraph_guard():
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for place in self.places:
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paddle.device.set_device(place)
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input_tensor = paddle.to_tensor(
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self.input_data, dtype='float32'
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)
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for param_name in ['x', 'input']:
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for as_tuple in [False, True]:
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kwargs = {
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param_name: input_tensor,
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'as_tuple': as_tuple,
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}
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result = paddle.nonzero(**kwargs)
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if as_tuple:
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combined = np.stack(
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[r.numpy() for r in result], axis=1
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)
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np.testing.assert_array_equal(
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combined, self.expected_indices
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)
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else:
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np.testing.assert_array_equal(
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result.numpy(), self.expected_indices
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)
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def test_nonzero_with_out(self):
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def run_nonzero(test_type):
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x = paddle.to_tensor(self.input_data, dtype='float32')
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x.stop_gradient = False
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out_shape = [len(self.expected_indices), 2]
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out = (
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paddle.zeros(out_shape, dtype='int64')
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if test_type in ["with_out", "both"]
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else None
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)
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if test_type == "return":
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out = paddle.nonzero(x, out=None)
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elif test_type == "with_out":
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paddle.nonzero(x, out=out)
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elif test_type == "both":
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out = paddle.nonzero(x, out=out)
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expected = paddle._C_ops.nonzero(x)
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np.testing.assert_array_equal(out.numpy(), expected.numpy())
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loss = out.sum().astype('float32')
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loss.backward()
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return out, x.grad
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with dygraph_guard():
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for place in self.places:
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paddle.device.set_device(place)
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out1, _ = run_nonzero("return")
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out2, _ = run_nonzero("with_out")
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out3, _ = run_nonzero("both")
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for out in [out2, out3]:
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np.testing.assert_allclose(
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out1.numpy(), out.numpy(), rtol=1e-10
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)
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if __name__ == "__main__":
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unittest.main()
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