paddlepaddle--paddle
wehub-resource-sync
904 行
28 KiB
Python
904 行
28 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 unittest
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import gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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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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from utils import static_guard
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import paddle
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from paddle import base
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from paddle.base import Program, core, program_guard
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from paddle.framework import in_pir_mode
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# Situation 1: shape is a list(without tensor)
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class TestExpandV2OpRank1(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.init_data()
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
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self.attrs = {'shape': self.shape}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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self.if_enable_cinn()
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def init_data(self):
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self.ori_shape = [100]
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self.shape = [100]
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self.expand_times = [1]
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def if_enable_cinn(self):
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pass
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def test_check_output(self):
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self.check_output(check_cinn=True, check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestExpandV2OpRank1_ZeroDim1(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = [10]
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self.expand_times = [10]
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestExpandV2OpRank1_ZeroDim2(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = []
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self.expand_times = []
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def if_enable_cinn(self):
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pass
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def test_check_grad(self):
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if self.shape == [] or self.ori_shape == []:
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return
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super().test_check_grad()
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class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [120]
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self.shape = [2, 120]
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self.expand_times = [2, 1]
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class TestExpandV2OpRank2(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 140]
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self.shape = [12, 140]
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self.expand_times = [12, 1]
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class TestExpandV2OpRank3_Corner(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = (2, 10, 5)
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self.shape = (2, 10, 5)
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self.expand_times = (1, 1, 1)
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class TestExpandV2OpRank4(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = (2, 4, 5, 7)
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self.shape = (-1, -1, -1, -1)
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self.expand_times = (1, 1, 1, 1)
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class TestExpandV2OpRank5(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [5, 2, 1, 4, 5]
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self.shape = [5, 2, 3, 4, 5]
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self.expand_times = [1, 1, 3, 1, 1]
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class TestExpandV2OpRank5_Corner(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [5, 2, 3, 4, 5]
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self.shape = [5, 2, 3, 4, 5]
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self.expand_times = [1, 1, 1, 1, 1]
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class TestExpandV2OpRank5_ZeroDim(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = [5, 2, 3, 4, 5]
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self.expand_times = [5, 2, 3, 4, 5]
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestExpandV2OpRank6(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 2, 1, 4, 5, 6]
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self.shape = [1, 2, 3, 4, 5, 6]
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self.expand_times = [1, 1, 3, 1, 1, 1]
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class TestExpandV2OpRank6_Corner(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 2, 3, 4, 5, 6]
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self.shape = [1, 2, 3, 4, 5, 6]
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self.expand_times = [1, 1, 1, 1, 1, 1]
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class TestExpandV2OpRank6_ZeroDim(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = [1, 2, 3, 4, 5, 6]
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self.expand_times = [1, 2, 3, 4, 5, 6]
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestExpandV2OpRank7(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [5, 2, 1, 4, 5, 6, 7]
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self.shape = [5, 2, 3, 4, 5, 6, 7]
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self.expand_times = [1, 1, 3, 1, 1, 1, 1]
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class TestExpandV2OpRank7_Corner(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 2, 3, 4, 5, 2, 2]
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self.shape = [1, 2, 3, 4, 5, 2, 2]
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self.expand_times = [1, 1, 1, 1, 1, 1, 1]
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class TestExpandV2OpRank7_ZeroDim(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = [1, 2, 3, 4, 5, 6, 7]
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self.expand_times = [1, 2, 3, 4, 5, 6, 7]
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestExpandV2OpRank8(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 2, 1, 4, 5, 6, 7, 8]
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self.shape = [1, 2, 3, 4, 5, 6, 7, 8]
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self.expand_times = [1, 1, 3, 1, 1, 1, 1, 1]
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class TestExpandV2OpRank8_Corner(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = [1, 2, 3, 4, 5, 2, 2, 2]
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self.shape = [1, 2, 3, 4, 5, 2, 2, 2]
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self.expand_times = [1, 1, 1, 1, 1, 1, 1, 1]
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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numeric_grad_delta=1e-5,
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max_relative_error=2e-7, # need slightly larger than 1e-7.
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)
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class TestExpandV2OpRank8_ZeroDim(TestExpandV2OpRank1):
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def init_data(self):
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self.ori_shape = []
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self.shape = [1, 2, 3, 4, 5, 6, 7, 8]
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self.expand_times = [1, 2, 3, 4, 5, 6, 7, 8]
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# Situation 2: shape is a list(with tensor)
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class TestExpandV2OpRank1_tensor_attr(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.init_data()
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expand_shapes_tensor = []
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for index, ele in enumerate(self.expand_shape):
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expand_shapes_tensor.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {
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'X': np.random.random(self.ori_shape).astype("float64"),
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'expand_shapes_tensor': expand_shapes_tensor,
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}
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self.attrs = {"shape": self.infer_expand_shape}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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def init_data(self):
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self.ori_shape = [100]
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self.expand_times = [1]
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self.expand_shape = [100]
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self.infer_expand_shape = [-1]
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def test_check_output(self):
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self.check_output(
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check_cinn=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_cinn=True, check_pir=True)
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class TestExpandV2OpRank2_Corner_tensor_attr(TestExpandV2OpRank1_tensor_attr):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.expand_times = [1, 1]
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self.expand_shape = [12, 14]
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self.infer_expand_shape = [12, -1]
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# Situation 3: shape is a tensor
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class TestExpandV2OpRank1_tensor(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.init_data()
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self.inputs = {
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'X': np.random.random(self.ori_shape).astype("float64"),
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'Shape': np.array(self.expand_shape).astype("int32"),
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}
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self.attrs = {}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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def init_data(self):
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self.ori_shape = [100]
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self.expand_times = [2, 1]
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self.expand_shape = [2, 100]
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def test_check_output(self):
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self.check_output(
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check_cinn=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_cinn=True, check_pir=True)
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# Situation 4: input x is Integer
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class TestExpandV2OpInteger(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.inputs = {
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'X': np.random.randint(10, size=(2, 4, 5)).astype("int32")
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}
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self.attrs = {'shape': [2, 4, 5]}
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output = np.tile(self.inputs['X'], (1, 1, 1))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output(check_cinn=True, check_pir=True)
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# Situation 5: input x is Bool
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class TestExpandV2OpBoolean(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
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self.attrs = {'shape': [2, 4, 5]}
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output = np.tile(self.inputs['X'], (1, 1, 1))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output(check_cinn=True, check_pir=True)
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# Situation 6: input x is Integer
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class TestExpandV2OpInt64_t(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.inputs = {
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'X': np.random.randint(10, size=(2, 4, 5)).astype("int64")
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}
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self.attrs = {'shape': [2, 4, 5]}
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output = np.tile(self.inputs['X'], (1, 1, 1))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output(check_cinn=True, check_pir=True)
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# Situation 7: input x is Float16
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class TestExpandV2FP16Op(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.dtype = np.float16
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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self.inputs = {
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'X': np.random.randint(10, size=(8, 8, 5)).astype(self.dtype)
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}
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self.attrs = {'shape': [8, 8, 5]}
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output = np.tile(self.inputs['X'], (1, 1, 1))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output(check_cinn=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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# Situation 8: input x is BF16
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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 the bfloat16",
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)
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class TestExpandV2BF16Op(OpTest):
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def setUp(self):
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self.op_type = "expand_v2"
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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self.python_api = paddle.expand
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self.public_python_api = paddle.expand
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x = np.random.randint(10, size=(8, 8, 5)).astype(np.float32)
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {'shape': [8, 8, 5]}
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output = np.tile(x, (1, 1, 1)).astype(np.float32)
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self.outputs = {'Out': convert_float_to_uint16(output)}
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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_cinn=True, check_pir=True)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestExpandV2Error(unittest.TestCase):
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def test_errors(self):
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with (
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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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shape = [2, 2]
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if not in_pir_mode():
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x1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.tensor.expand, x1, shape)
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x2 = paddle.static.data(name='x2', shape=[-1, 4], dtype="bool")
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x2.stop_gradient = False
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self.assertRaises(ValueError, paddle.tensor.expand, x2, shape)
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x2.stop_gradient = True
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self.assertRaises(ValueError, paddle.tensor.expand, x2, 1)
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x3 = paddle.static.data(name='x3', shape=[1, 1, 1], dtype="int64")
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shape_empty = paddle.static.data(
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name='shape_empty', shape=[0], dtype="int32"
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)
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try:
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result = paddle.tensor.expand(x3, shape_empty)
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self.assertIsNotNone(result)
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except Exception as e:
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self.fail(f"Unexpected exception: {e}")
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# Test python API
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class TestExpandV2API(unittest.TestCase):
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def test_api(self):
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with paddle.static.program_guard(paddle.static.Program()):
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input = np.random.random([12, 14]).astype("float32")
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x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")
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positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
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expand_shape = paddle.static.data(
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name="expand_shape",
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shape=[2],
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dtype="int32",
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)
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out_1 = paddle.expand(x, shape=[12, 14])
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out_2 = paddle.expand(x, shape=[positive_2, 14])
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out_3 = paddle.expand(x, shape=expand_shape)
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exe = base.Executor(place=base.CPUPlace())
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res_1, res_2, res_3 = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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"expand_shape": np.array([12, 14]).astype("int32"),
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},
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fetch_list=[out_1, out_2, out_3],
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)
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np.testing.assert_array_equal(res_1, np.tile(input, (1, 1)))
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np.testing.assert_array_equal(res_2, np.tile(input, (1, 1)))
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np.testing.assert_array_equal(res_3, np.tile(input, (1, 1)))
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class TestExpandInferShape(unittest.TestCase):
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def test_shape_with_var(self):
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with program_guard(Program(), Program()):
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x = paddle.static.data(shape=[-1, 1, 3], name='x')
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fake_var = paddle.randn([2, 3])
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target_shape = [
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-1,
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paddle.shape(fake_var)[0],
|
|
paddle.shape(fake_var)[1],
|
|
]
|
|
out = paddle.expand(x, shape=target_shape)
|
|
self.assertListEqual(list(out.shape), [-1, -1, -1])
|
|
|
|
|
|
# Test python Dygraph API
|
|
class TestExpandV2DygraphAPI(unittest.TestCase):
|
|
def test_expand_times_is_tensor(self):
|
|
with paddle.base.dygraph.guard():
|
|
paddle.seed(1)
|
|
a = paddle.rand([2, 5])
|
|
expand_1 = paddle.expand(a, shape=[2, 5])
|
|
np_array = np.array([2, 5])
|
|
expand_2 = paddle.expand(a, shape=np_array)
|
|
np.testing.assert_array_equal(expand_1.numpy(), expand_2.numpy())
|
|
|
|
|
|
class TestExpandDoubleGradCheck(unittest.TestCase):
|
|
def expand_wrapper(self, x):
|
|
return paddle.expand(x[0], [2, 3])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 3], dtype)
|
|
data.persistable = True
|
|
out = paddle.expand(data, [2, 3])
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.expand_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestExpandTripleGradCheck(unittest.TestCase):
|
|
def expand_wrapper(self, x):
|
|
return paddle.expand(x[0], [2, 3])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 3], dtype)
|
|
data.persistable = True
|
|
out = paddle.expand(data, [2, 3])
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.triple_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.expand_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
# Situation 9: comp case, shape is a list(without tensor)
|
|
class TestExpandV2CompOpRank1(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.prim_op_type = "comp"
|
|
self.init_data()
|
|
self.python_api = paddle.expand
|
|
self.public_python_api = paddle.expand
|
|
self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
|
|
self.attrs = {'shape': self.shape}
|
|
output = np.tile(self.inputs['X'], self.expand_times)
|
|
self.outputs = {'Out': output}
|
|
self.enable_cinn = True
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [100]
|
|
self.shape = [100]
|
|
self.expand_times = [1]
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_prim=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], 'Out', check_prim=True, check_prim_pir=True)
|
|
|
|
|
|
class TestExpandV2OpCompRank2_DimExpanding(TestExpandV2CompOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = [120]
|
|
self.shape = [2, 120]
|
|
self.expand_times = [2, 1]
|
|
|
|
|
|
class TestExpandV2CompOpRank2(TestExpandV2CompOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = [1, 140]
|
|
self.shape = [12, 140]
|
|
self.expand_times = [12, 1]
|
|
|
|
|
|
class TestExpandV2CompOpRank3_Corner(TestExpandV2CompOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 10, 5)
|
|
self.shape = (2, 10, 5)
|
|
self.expand_times = (1, 1, 1)
|
|
|
|
|
|
class TestExpandV2CompOpRank4(TestExpandV2CompOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 4, 5, 7)
|
|
self.shape = (-1, -1, -1, -1)
|
|
self.expand_times = (1, 1, 1, 1)
|
|
|
|
|
|
# Situation 10: comp case, input x is Integer
|
|
class TestExpandV2CompOpInteger(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.prim_op_type = "comp"
|
|
self.python_api = paddle.expand
|
|
self.public_python_api = paddle.expand
|
|
self.inputs = {
|
|
'X': np.random.randint(10, size=(2, 4, 5)).astype("int32")
|
|
}
|
|
self.attrs = {'shape': [2, 4, 5]}
|
|
output = np.tile(self.inputs['X'], (1, 1, 1))
|
|
self.outputs = {'Out': output}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_prim=True)
|
|
|
|
|
|
# Situation 11: comp case, input x is Bool
|
|
class TestExpandV2CompOpBoolean(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.prim_op_type = "comp"
|
|
self.python_api = paddle.expand
|
|
self.public_python_api = paddle.expand
|
|
self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
|
|
self.attrs = {'shape': [2, 4, 5]}
|
|
output = np.tile(self.inputs['X'], (1, 1, 1))
|
|
self.outputs = {'Out': output}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_prim=True)
|
|
|
|
|
|
# Situation 12: comp case, input x is Integer
|
|
class TestExpandV2CompOpInt64_t(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.prim_op_type = "comp"
|
|
self.python_api = paddle.expand
|
|
self.public_python_api = paddle.expand
|
|
self.inputs = {
|
|
'X': np.random.randint(10, size=(2, 4, 5)).astype("int64")
|
|
}
|
|
self.attrs = {'shape': [2, 4, 5]}
|
|
output = np.tile(self.inputs['X'], (1, 1, 1))
|
|
self.outputs = {'Out': output}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_prim=True)
|
|
|
|
|
|
class TestExpandPirValueListShape(unittest.TestCase):
|
|
def test_value_list_shape1(self):
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(paddle.static.Program()),
|
|
):
|
|
x = paddle.static.data('x', [1, 1])
|
|
shape = [2, paddle.full([], 4)]
|
|
out = paddle.expand(x, shape)
|
|
np.testing.assert_array_equal(tuple(out.shape), (2, -1))
|
|
|
|
def test_value_list_shape2(self):
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(paddle.static.Program()),
|
|
):
|
|
x = paddle.static.data('x', [1, 1, -1, -1], 'float32')
|
|
shape1 = paddle.static.data('shape1', [], 'int32')
|
|
x = paddle.expand(x, shape=[shape1, 1, -1, -1])
|
|
np.testing.assert_equal(tuple(x.shape), (-1, 1, -1, -1))
|
|
|
|
|
|
class TestExpandV2ZeroSizeOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.init_data()
|
|
self.init_place()
|
|
self.python_api = paddle.expand
|
|
self.x = np.zeros(self.ori_shape).astype("float64")
|
|
self.attrs = {
|
|
'shape': self.shape,
|
|
}
|
|
self.set_inputs()
|
|
self.set_additional_inputs()
|
|
output = np.zeros(self.expect_shape).astype("float64")
|
|
self.outputs = {'Out': output}
|
|
|
|
def set_inputs(self):
|
|
self.inputs = {'X': self.x}
|
|
|
|
def set_additional_inputs(self):
|
|
pass
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [1, 0, 1, 140]
|
|
self.shape = [1, 0, 1, 140]
|
|
self.expect_shape = [1, 0, 1, 140]
|
|
|
|
def init_place(self):
|
|
self.place = core.CPUPlace()
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(self.place, check_dygraph=False)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad_with_place(
|
|
self.place,
|
|
["X"],
|
|
"Out",
|
|
check_dygraph=False,
|
|
)
|
|
|
|
|
|
class TestExpandV2CPUOp1(TestExpandV2ZeroSizeOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 1)
|
|
self.shape = (0, 8)
|
|
self.expect_shape = (0, 8)
|
|
|
|
|
|
class TestExpandV2CPUOp2(TestExpandV2ZeroSizeOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 130)
|
|
self.shape = (4, 0, 130)
|
|
self.expect_shape = (4, 0, 130)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestExpandV2ZeroSizeGPUOp(TestExpandV2ZeroSizeOp):
|
|
def init_place(self):
|
|
self.place = get_device_place()
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestExpandV2ZeroSizeGPUOp1(TestExpandV2ZeroSizeGPUOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 130)
|
|
self.shape = (4, 0, 130)
|
|
self.expect_shape = (4, 0, 130)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestExpandV2ZeroSizeGPUOp2(TestExpandV2ZeroSizeGPUOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 1)
|
|
self.shape = (0, 8)
|
|
self.expect_shape = (0, 8)
|
|
|
|
|
|
class TestExpandV2ZeroSizeOneDNNOp(TestExpandV2ZeroSizeOp):
|
|
def setUp(self):
|
|
self.op_type = "expand_v2"
|
|
self.init_data()
|
|
self.init_place()
|
|
self.python_api = paddle.expand
|
|
self.x = np.zeros(self.ori_shape).astype("float32")
|
|
self.attrs = {'shape': self.shape, 'use_onednn': True}
|
|
self.use_onednn = True
|
|
self.set_inputs()
|
|
self.set_additional_inputs()
|
|
output = np.zeros(self.expect_shape).astype("float32")
|
|
self.outputs = {'Out': output}
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [1, 0, 1, 140]
|
|
self.shape = [1, 0, 1, 140]
|
|
self.expect_shape = [1, 0, 1, 140]
|
|
|
|
def init_place(self):
|
|
self.place = core.CPUPlace()
|
|
|
|
def test_check_output(self):
|
|
flags_use_onednn = core.globals()["FLAGS_use_onednn"]
|
|
paddle.set_flags({'FLAGS_use_onednn': True})
|
|
self.check_output_with_place(
|
|
self.place,
|
|
check_dygraph=False,
|
|
check_pir=False,
|
|
check_pir_onednn=True,
|
|
)
|
|
paddle.set_flags({'FLAGS_use_onednn': flags_use_onednn})
|
|
|
|
def test_check_grad(self):
|
|
flags_use_onednn = core.globals()["FLAGS_use_onednn"]
|
|
paddle.set_flags({'FLAGS_use_onednn': True})
|
|
self.check_grad_with_place(
|
|
self.place,
|
|
["X"],
|
|
"Out",
|
|
check_dygraph=False,
|
|
check_pir=False,
|
|
check_pir_onednn=True,
|
|
)
|
|
paddle.set_flags({'FLAGS_use_onednn': flags_use_onednn})
|
|
|
|
|
|
class TestExpandV2ZeroSizeOneDNNOp1(TestExpandV2ZeroSizeOneDNNOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 130)
|
|
self.shape = (4, 0, 130)
|
|
self.expect_shape = (4, 0, 130)
|
|
|
|
|
|
class TestExpandV2ZeroSizeOneDNNOp2(TestExpandV2ZeroSizeOneDNNOp):
|
|
def init_data(self):
|
|
self.ori_shape = (0, 1, 8)
|
|
self.shape = (0, 8, 8)
|
|
self.expect_shape = (0, 8, 8)
|
|
|
|
|
|
class TestExpandV2API_Compatibility(unittest.TestCase):
|
|
def test_static_api(self):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
input = np.random.random([12, 14]).astype("float32")
|
|
x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")
|
|
|
|
positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
|
|
expand_shape = paddle.static.data(
|
|
name="expand_shape",
|
|
shape=[2],
|
|
dtype="int32",
|
|
)
|
|
|
|
out_1 = paddle.expand(input=x, shape=[12, 14])
|
|
out_2 = paddle.expand(x, size=[positive_2, 14])
|
|
out_3 = paddle.expand(input=x, shape=expand_shape)
|
|
out_4 = x.expand([12, 14])
|
|
out_5 = x.expand(size=[positive_2, 14])
|
|
out_6 = x.expand(shape=expand_shape)
|
|
out_7 = x.expand(12, 14)
|
|
|
|
exe = base.Executor(place=base.CPUPlace())
|
|
res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={
|
|
"x": input,
|
|
"expand_shape": np.array([12, 14]).astype("int32"),
|
|
},
|
|
fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7],
|
|
)
|
|
np.testing.assert_array_equal(res_1, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_2, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_3, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_4, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_5, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_6, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_7, np.tile(input, (1, 1)))
|
|
|
|
def test_dygraph_api(self):
|
|
paddle.disable_static()
|
|
|
|
input = np.random.random([1, 3]).astype("float32")
|
|
x = paddle.to_tensor(input)
|
|
|
|
expect_out = paddle.expand(x, shape=[2, 3])
|
|
out_1 = paddle.expand(input=x, shape=[2, 3])
|
|
out_2 = paddle.expand(x, size=[2, 3])
|
|
out_3 = paddle.expand(input=x, shape=[2, 3])
|
|
out_4 = x.expand([2, 3])
|
|
out_5 = x.expand(size=[2, 3])
|
|
out_6 = x.expand(shape=[2, 3])
|
|
out_7 = x.expand(2, 3)
|
|
|
|
np.testing.assert_array_equal(out_1, expect_out)
|
|
np.testing.assert_array_equal(out_2, expect_out)
|
|
np.testing.assert_array_equal(out_3, expect_out)
|
|
np.testing.assert_array_equal(out_4, expect_out)
|
|
np.testing.assert_array_equal(out_5, expect_out)
|
|
np.testing.assert_array_equal(out_6, expect_out)
|
|
np.testing.assert_array_equal(out_7, expect_out)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|