wehub-resource-sync
568 lines
18 KiB
Python
568 lines
18 KiB
Python
# Copyright (c) 2018 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 sys
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import unittest
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import numpy as np
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sys.path.append("../../legacy_test")
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from op import Operator
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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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paddle_static_guard,
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)
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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_wrapper(shape, value=0.0):
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out = paddle.full(shape=shape, fill_value=value)
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return out
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# Situation 1: Attr(shape) is a list(without tensor)
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# Base case
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class TestFillConstantOp(OpTest):
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def setUp(self):
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'''Test fill_constant op with default value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_dtype()
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self.init_shape()
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self.init_value()
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self.inputs = {}
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self.attrs = {'shape': self.shape, 'value': self.value}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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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_dtype(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = [123, 92]
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def init_value(self):
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self.value = 0.0
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class TestFillConstantFP32Op(TestFillConstantOp):
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'''Test fill_constant op with specified value'''
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def init_dtype(self):
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self.dtype = np.float32
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def init_value(self):
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self.value = 3.8
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class TestFillConstantFP16Op(TestFillConstantOp):
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'''Test fill_constant op with specified value'''
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def init_dtype(self):
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self.dtype = np.float16
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def init_value(self):
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self.value = 3.8
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class TestFillConstantINT64Op(TestFillConstantOp):
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'''Test fill_constant op with specified int64 value'''
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def init_dtype(self):
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self.dtype = np.int64
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def init_value(self):
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self.value = 10000000000
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class TestFillConstantINT32Op(TestFillConstantOp):
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'''Test fill_constant op with specified int value'''
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def init_dtype(self):
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self.dtype = np.int32
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def init_value(self):
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self.value = 3
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFillConstantBF16Op(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.dtype = np.uint16
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self.inputs = {}
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self.attrs = {
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'shape': [123, 92],
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'value': 3.8,
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'dtype': core.VarDesc.VarType.BF16,
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}
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self.outputs = {'Out': convert_float_to_uint16(np.full((123, 92), 3.8))}
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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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class TestFillConstantOpWithSelectedRows(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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# create Out Variable
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out = scope.var('Out').get_selected_rows()
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# create and run fill_constant_op operator
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fill_constant_op = Operator(
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"fill_constant", shape=[123, 92], value=3.8, Out='Out'
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)
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fill_constant_op.run(scope, place)
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# get result from Out
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result_array = np.array(out.get_tensor())
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full_array = np.full((123, 92), 3.8, 'float32')
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np.testing.assert_array_equal(result_array, full_array)
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def test_fill_constant_with_selected_rows(self):
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for place in get_places():
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self.check_with_place(place)
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# Situation 2: Attr(shape) is a list(with tensor)
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class TestFillConstantOp1_ShapeTensorList(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_data()
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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self.attrs = {'shape': self.infer_shape, 'value': self.value}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, 92]
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self.value = 3.8
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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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class TestFillConstantOp2_ShapeTensorList(OpTest):
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def setUp(self):
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'''Test fill_constant op with default value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_data()
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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self.attrs = {'shape': self.infer_shape}
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self.outputs = {'Out': np.full(self.shape, 0.0)}
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, -1]
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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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class TestFillConstantOp3_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.value = 10000000000
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class TestFillConstantOp4_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.value = 3
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# Situation 3: shape is a tensor
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class TestFillConstantOp1_ShapeTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_data()
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self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
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self.attrs = {'value': self.value}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3.8
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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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# Situation 4: value is a tensor
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class TestFillConstantOp1_ValueTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_data()
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self.inputs = {
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"ShapeTensor": np.array(self.shape).astype("int32"),
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'ValueTensor': np.array([self.value]).astype("float32"),
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}
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self.attrs = {'value': self.value + 1.0}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3.8
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self.dtype = np.float32
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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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# Situation 5: value is a tensor
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class TestFillConstantOp2_ValueTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value'''
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self.op_type = "fill_constant"
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self.python_api = fill_wrapper
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self.init_data()
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self.inputs = {
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"ShapeTensor": np.array(self.shape).astype("int32"),
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'ValueTensor': np.array([self.value]).astype("int32"),
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}
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self.attrs = {'value': self.value, 'dtype': 2}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3
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self.dtype = np.int32
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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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# Test python API
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class TestFillConstantAPI(unittest.TestCase):
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def test_api(self):
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paddle.enable_static()
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positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
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positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
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shape_tensor_int32 = paddle.static.data(
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name="shape_tensor_int32", shape=[2], dtype="int32"
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)
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shape_tensor_int64 = paddle.static.data(
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name="shape_tensor_int64", shape=[2], dtype="int64"
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)
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out_1 = paddle.tensor.fill_constant(
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shape=[1, 2], dtype="float32", value=1.1
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)
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out_2 = paddle.tensor.fill_constant(
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shape=[1, positive_2_int32], dtype="float32", value=1.1
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)
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out_3 = paddle.tensor.fill_constant(
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shape=[1, positive_2_int64], dtype="float32", value=1.1
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)
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out_4 = paddle.tensor.fill_constant(
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shape=shape_tensor_int32, dtype="float32", value=1.1
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)
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out_5 = paddle.tensor.fill_constant(
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shape=shape_tensor_int64, dtype="float32", value=1.1
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)
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out_6 = paddle.tensor.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=1.1
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)
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val1 = paddle.tensor.fill_constant(
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shape=[1], dtype=np.float32, value=1.1
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)
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val2 = paddle.tensor.fill_constant(
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shape=[1], dtype=np.float64, value=1.1
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)
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out_7 = paddle.tensor.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=val1
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)
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out_8 = paddle.tensor.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=val2
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)
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exe = base.Executor(place=base.CPUPlace())
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res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8 = exe.run(
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paddle.static.default_main_program(),
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feed={
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"shape_tensor_int32": np.array([1, 2]).astype("int32"),
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"shape_tensor_int64": np.array([1, 2]).astype("int64"),
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},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8],
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)
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np.testing.assert_array_equal(
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res_1, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_2, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_3, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_4, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_5, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_6, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_7, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_8, np.full([1, 2], 1.1, dtype="float32")
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)
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class TestFillConstantImperative(unittest.TestCase):
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def test_api(self):
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with base.dygraph.guard():
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data1 = np.array([1, 2]).astype('int32')
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data2 = np.array([1.1]).astype('float32')
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data3 = np.array([88]).astype('int32')
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shape = paddle.to_tensor(data1)
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val = paddle.to_tensor(data2)
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value = paddle.to_tensor(data3)
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res1 = paddle.tensor.fill_constant(
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shape=[1, 2], dtype='float32', value=1.1
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)
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res2 = paddle.tensor.fill_constant(
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shape=shape, dtype='float32', value=1.1
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)
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res3 = paddle.tensor.fill_constant(
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shape=shape, dtype='float32', value=val
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)
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res4 = paddle.tensor.fill_constant(
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shape=shape, dtype='int32', value=value
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)
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np.testing.assert_array_equal(
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res1.numpy(), np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res2.numpy(), np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res3.numpy(), np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res4.numpy(), np.full([1, 2], 88, dtype="int32")
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)
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def test_nan(self):
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with base.dygraph.guard():
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res = paddle.tensor.fill_constant([1], 'float32', np.nan)
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self.assertTrue(np.isnan(res.numpy().item(0)))
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def test_inf(self):
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with base.dygraph.guard():
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res = paddle.tensor.fill_constant([1], 'float32', np.inf)
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self.assertTrue(np.isinf(res.numpy().item(0)))
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def test_ninf(self):
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with base.dygraph.guard():
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res = paddle.tensor.fill_constant([1], 'float32', -np.inf)
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self.assertTrue(np.isinf(res.numpy().item(0)))
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self.assertEqual(-np.inf, res.numpy().item(0))
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class TestFillConstantOpError(unittest.TestCase):
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def test_errors1(self):
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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# for ci coverage
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x1 = paddle.static.data(name='x1', shape=[-1, 1], dtype="int16")
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self.assertRaises(
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TypeError,
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paddle.tensor.fill_constant,
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shape=[1],
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value=5,
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dtype='uint4',
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)
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self.assertRaises(
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TypeError,
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paddle.tensor.fill_constant,
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shape=[1.1],
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value=5,
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dtype='float32',
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out=x1,
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)
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x3 = np.random.randn(100, 100).astype('int32')
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self.assertRaises(
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TypeError,
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paddle.tensor.fill_constant,
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shape=[100, 100],
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value=5,
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dtype='float64',
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out=x3,
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)
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def test_errors2(self):
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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# The argument dtype of fill_constant_op must be one of bool, float16,
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# float32, float64, uint8, int16, int32 or int64
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x2 = paddle.static.data(name='x2', shape=[-1, 1], dtype="int32")
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self.assertRaises(
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TypeError,
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paddle.tensor.fill_constant,
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shape=[1],
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value=5,
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dtype='float64',
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out=x2,
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)
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# The shape dtype of fill_constant_op must be int32 or int64.
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def test_shape_tensor_dtype():
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shape = paddle.static.data(
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name="shape_tensor", shape=[2], dtype="float32"
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)
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paddle.tensor.fill_constant(
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shape=shape, dtype="float32", value=1
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)
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self.assertRaises(TypeError, test_shape_tensor_dtype)
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def test_shape_tensor_list_dtype():
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shape = paddle.static.data(
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name="shape_tensor_list", shape=[1], dtype="bool"
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)
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paddle.tensor.fill_constant(
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shape=[shape, 2], dtype="float32", value=1
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)
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self.assertRaises(TypeError, test_shape_tensor_list_dtype)
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def test_pir_errors(self):
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def test_shape_type():
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# The shape dtype of fill_constant_op must be int32 or int64.
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# test_shape_tensor_dtype:
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shape = paddle.static.data(
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name="shape_tensor", shape=[2], dtype="int32"
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)
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out = paddle.tensor.fill_constant(
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shape=shape, dtype="float32", value=1
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)
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exe = base.Executor(place=base.CPUPlace())
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exe.run(
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feed={"shape_tensor": np.array([1, 2]).astype("float32")},
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fetch_list=[out],
|
|
)
|
|
|
|
# TODO(chenzhiyang): pir test_shape_dtype
|
|
|
|
|
|
class TestFillConstantOp_ValueTensorBf16(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified value'''
|
|
self.op_type = "fill_constant"
|
|
self.python_api = fill_wrapper
|
|
self.init_data()
|
|
|
|
self.inputs = {
|
|
"ShapeTensor": np.array(self.shape).astype("int32"),
|
|
'ValueTensor': convert_float_to_uint16(
|
|
np.array([self.value]).astype("float32")
|
|
),
|
|
}
|
|
self.attrs = {'value': self.value, 'dtype': core.VarDesc.VarType.BF16}
|
|
self.outputs = {'Out': np.full(self.shape, self.value)}
|
|
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.value = 3.0
|
|
self.dtype = np.uint16
|
|
self.onednn_data_type = "bfloat16"
|
|
|
|
def test_check_output(self):
|
|
# no dynamic graph test for onednn
|
|
self.check_output_with_place(
|
|
core.CPUPlace(), check_dygraph=False, check_pir=False
|
|
)
|
|
|
|
|
|
class TestFillConstantOp_ZeroSize(unittest.TestCase):
|
|
def test_shape(self):
|
|
out = paddle.full(
|
|
shape=[
|
|
paddle.to_tensor([1]),
|
|
paddle.to_tensor([1]),
|
|
paddle.to_tensor([]),
|
|
],
|
|
fill_value=1.0,
|
|
)
|
|
out_np = out.numpy()
|
|
np.testing.assert_allclose(out_np, np.ones([1, 1]).astype(out_np.dtype))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|