wehub-resource-sync
727 lines
29 KiB
Python
727 lines
29 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 numpy
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import numpy as np
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import op_test
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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.framework import in_pir_mode
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def create_test_class(op_type, typename, callback, check_pir=False):
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class Cls(op_test.OpTest):
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def setUp(self):
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a = numpy.random.random(size=(10, 7)).astype(typename)
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b = numpy.random.random(size=(10, 7)).astype(typename)
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c = callback(a, b)
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self.python_api = eval("paddle." + op_type)
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self.inputs = {'X': a, 'Y': b}
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self.outputs = {'Out': c}
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self.op_type = op_type
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def test_output(self):
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self.check_output(check_cinn=True, check_pir=check_pir)
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def test_int16_support(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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a = paddle.static.data(name='a', shape=[-1, 2], dtype='int16')
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b = paddle.static.data(name='b', shape=[-1, 2], dtype='int16')
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op = eval(f"paddle.{self.op_type}")
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try:
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result = op(x=a, y=b)
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except TypeError:
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self.fail("TypeError should not be raised for int16 inputs")
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cls_name = f"{op_type}_{typename}"
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Cls.__name__ = cls_name
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globals()[cls_name] = Cls
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for _type_name in {
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'float32',
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'float64',
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'uint8',
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'int8',
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'int16',
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'int32',
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'int64',
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'float16',
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}:
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if _type_name == 'float64' and core.is_compiled_with_rocm():
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_type_name = 'float32'
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if _type_name == 'float16' and (
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not (core.is_compiled_with_cuda() or is_custom_device())
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):
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continue
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create_test_class('less_than', _type_name, lambda _a, _b: _a < _b, True)
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create_test_class('less_equal', _type_name, lambda _a, _b: _a <= _b, True)
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create_test_class('greater_than', _type_name, lambda _a, _b: _a > _b, True)
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create_test_class(
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'greater_equal', _type_name, lambda _a, _b: _a >= _b, True
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)
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create_test_class('equal', _type_name, lambda _a, _b: _a == _b, True)
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create_test_class('not_equal', _type_name, lambda _a, _b: _a != _b, True)
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def create_paddle_case(op_type, callback):
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class PaddleCls(unittest.TestCase):
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def setUp(self):
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self.op_type = op_type
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self.input_x = np.array([1, 2, 3, 4]).astype(np.int64)
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self.input_y = np.array([1, 3, 2, 4]).astype(np.int64)
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self.real_result = callback(self.input_x, self.input_y)
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self.place = base.CPUPlace()
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if core.is_compiled_with_cuda() or is_custom_device():
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self.place = get_device_place()
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def test_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[4], dtype='int64')
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y = paddle.static.data(name='y', shape=[4], dtype='int64')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = base.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "y": self.input_y},
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fetch_list=[out],
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)
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self.assertEqual((res == self.real_result).all(), True)
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def test_api_float(self):
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if self.op_type == "equal":
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[4], dtype='int64')
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y = paddle.static.data(name='y', shape=[], dtype='int64')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = base.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "y": 1.0}, fetch_list=[out]
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)
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self.real_result = np.array([1, 0, 0, 0]).astype(np.int64)
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self.assertEqual((res == self.real_result).all(), True)
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def test_dynamic_api(self):
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x)
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y = paddle.to_tensor(self.input_y)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.assertEqual((out.numpy() == self.real_result).all(), True)
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def test_dynamic_api_int(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, 1)
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self.real_result = np.array([1, 0, 0, 0]).astype(np.int64)
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self.assertEqual(
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(out.numpy() == self.real_result).all(), True
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)
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def test_dynamic_api_float(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, 1.0)
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self.real_result = np.array([1, 0, 0, 0]).astype(np.int64)
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self.assertEqual(
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(out.numpy() == self.real_result).all(), True
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)
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def test_dynamic_api_float16(self):
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x, dtype="float16")
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y = paddle.to_tensor(self.input_y, dtype="float16")
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.assertEqual((out.numpy() == self.real_result).all(), True)
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def test_dynamic_api_inf_1(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('inf'), float('inf')]).astype(
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np.int64
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, float('-inf'), float('inf')]).astype(
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np.int64
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)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_dynamic_api_inf_2(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('inf'), float('inf')]).astype(
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np.float32
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, float('-inf'), float('inf')]).astype(
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np.float32
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)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_dynamic_api_inf_3(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('inf'), float('-inf')]).astype(
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np.float32
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, 2, 3]).astype(np.float32)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_dynamic_api_nan_1(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('nan'), float('nan')]).astype(
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np.int64
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, float('-nan'), float('nan')]).astype(
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np.int64
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)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_dynamic_api_nan_2(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('nan'), float('nan')]).astype(
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np.float32
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, float('-nan'), float('nan')]).astype(
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np.float32
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)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_dynamic_api_nan_3(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x1 = np.array([1, float('-nan'), float('nan')]).astype(
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np.float32
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)
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x = paddle.to_tensor(x1)
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y1 = np.array([1, 2, 1]).astype(np.float32)
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y = paddle.to_tensor(y1)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = (x1 == y1).astype(np.int64)
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self.assertEqual(
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(
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out.numpy().astype(np.int64) == self.real_result
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).all(),
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True,
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)
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def test_not_equal(self):
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if self.op_type == "not_equal":
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(
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np.array([1.2e-15, 2, 2, 1]), dtype="float32"
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)
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y = paddle.to_tensor(
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np.array([1.1e-15, 2, 2, 1]), dtype="float32"
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)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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self.real_result = np.array([0, 0, 0, 0]).astype(np.int64)
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self.assertEqual(
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(out.numpy() == self.real_result).all(), True
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)
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def test_assert(self):
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def test_dynamic_api_string(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, "1.0")
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self.assertRaises(TypeError, test_dynamic_api_string)
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def test_dynamic_api_bool(self):
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if self.op_type == "equal":
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.input_x)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, True)
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self.real_result = np.array([1, 0, 0, 0]).astype(np.int64)
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self.assertEqual(
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(out.numpy() == self.real_result).all(), True
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)
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def test_broadcast_api_1(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(
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name='x', shape=[1, 2, 1, 3], dtype='int32'
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)
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y = paddle.static.data(name='y', shape=[1, 2, 3], dtype='int32')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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input_x = np.arange(1, 7).reshape((1, 2, 1, 3)).astype(np.int32)
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input_y = np.arange(0, 6).reshape((1, 2, 3)).astype(np.int32)
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real_result = callback(input_x, input_y)
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(res,) = exe.run(
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feed={"x": input_x, "y": input_y}, fetch_list=[out]
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)
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self.assertEqual((res == real_result).all(), True)
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def test_broadcast_api_2(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[1, 2, 3], dtype='int32')
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y = paddle.static.data(
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name='y', shape=[1, 2, 1, 3], dtype='int32'
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)
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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input_x = np.arange(0, 6).reshape((1, 2, 3)).astype(np.int32)
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input_y = np.arange(1, 7).reshape((1, 2, 1, 3)).astype(np.int32)
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real_result = callback(input_x, input_y)
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(res,) = exe.run(
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feed={"x": input_x, "y": input_y}, fetch_list=[out]
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)
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self.assertEqual((res == real_result).all(), True)
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def test_broadcast_api_3(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[5], dtype='int32')
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y = paddle.static.data(name='y', shape=[3, 1], dtype='int32')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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input_x = np.arange(0, 5).reshape(5).astype(np.int32)
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input_y = np.array([5, 3, 2]).reshape((3, 1)).astype(np.int32)
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real_result = callback(input_x, input_y)
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(res,) = exe.run(
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feed={"x": input_x, "y": input_y}, fetch_list=[out]
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)
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self.assertEqual((res == real_result).all(), True)
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def test_zero_dim_api_1(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.randint(-3, 3, shape=[], dtype='int32')
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y = paddle.randint(-3, 3, shape=[], dtype='int32')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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(
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x_np,
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y_np,
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res,
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) = exe.run(fetch_list=[x, y, out])
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real_result = callback(x_np, y_np)
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self.assertEqual((res == real_result).all(), True)
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def test_zero_dim_api_2(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.randint(-3, 3, shape=[2, 3, 4], dtype='int32')
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y = paddle.randint(-3, 3, shape=[], dtype='int32')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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(
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x_np,
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y_np,
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res,
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) = exe.run(fetch_list=[x, y, out])
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real_result = callback(x_np, y_np)
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self.assertEqual((res == real_result).all(), True)
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def test_zero_dim_api_3(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.randint(-3, 3, shape=[], dtype='int32')
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y = paddle.randint(-3, 3, shape=[2, 3, 4], dtype='int32')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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(
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x_np,
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y_np,
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res,
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) = exe.run(fetch_list=[x, y, out])
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real_result = callback(x_np, y_np)
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self.assertEqual((res == real_result).all(), True)
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def test_bool_api_4(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[3, 1], dtype='bool')
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y = paddle.static.data(name='y', shape=[3, 1], dtype='bool')
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op = eval(f"paddle.{self.op_type}")
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out = op(x, y)
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exe = paddle.static.Executor(self.place)
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input_x = np.array([True, False, True]).astype(np.bool_)
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input_y = np.array([True, True, False]).astype(np.bool_)
|
|
real_result = callback(input_x, input_y)
|
|
(res,) = exe.run(
|
|
feed={"x": input_x, "y": input_y}, fetch_list=[out]
|
|
)
|
|
self.assertEqual((res == real_result).all(), True)
|
|
|
|
def test_bool_broadcast_api_4(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[3, 1], dtype='bool')
|
|
y = paddle.static.data(name='y', shape=[1], dtype='bool')
|
|
op = eval(f"paddle.{self.op_type}")
|
|
out = op(x, y)
|
|
exe = paddle.static.Executor(self.place)
|
|
input_x = np.array([True, False, True]).astype(np.bool_)
|
|
input_y = np.array([True]).astype(np.bool_)
|
|
real_result = callback(input_x, input_y)
|
|
(res,) = exe.run(
|
|
feed={"x": input_x, "y": input_y}, fetch_list=[out]
|
|
)
|
|
self.assertEqual((res == real_result).all(), True)
|
|
|
|
def test_attr_name(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[-1, 4], dtype='int32')
|
|
y = paddle.static.data(name='y', shape=[-1, 4], dtype='int32')
|
|
op = eval(f"paddle.{self.op_type}")
|
|
out = op(x=x, y=y, name=f"name_{self.op_type}")
|
|
if not in_pir_mode():
|
|
self.assertEqual(f"name_{self.op_type}" in out.name, True)
|
|
|
|
cls_name = f"TestCase_{op_type}"
|
|
PaddleCls.__name__ = cls_name
|
|
globals()[cls_name] = PaddleCls
|
|
|
|
|
|
create_paddle_case('less_than', lambda _a, _b: _a < _b)
|
|
create_paddle_case('less_equal', lambda _a, _b: _a <= _b)
|
|
create_paddle_case('greater_than', lambda _a, _b: _a > _b)
|
|
create_paddle_case('greater_equal', lambda _a, _b: _a >= _b)
|
|
create_paddle_case('equal', lambda _a, _b: _a == _b)
|
|
create_paddle_case('not_equal', lambda _a, _b: _a != _b)
|
|
|
|
|
|
# add bf16 tests
|
|
def create_bf16_case(op_type, callback, check_pir=False):
|
|
class TestCompareOpBF16Op(op_test.OpTest):
|
|
def setUp(self):
|
|
self.op_type = op_type
|
|
self.dtype = np.uint16
|
|
self.python_api = eval("paddle." + op_type)
|
|
|
|
x = np.random.uniform(0, 1, [5, 5]).astype(np.float32)
|
|
y = np.random.uniform(0, 1, [5, 5]).astype(np.float32)
|
|
real_result = callback(x, y)
|
|
self.inputs = {
|
|
'X': op_test.convert_float_to_uint16(x),
|
|
'Y': op_test.convert_float_to_uint16(y),
|
|
}
|
|
self.outputs = {'Out': real_result}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_cinn=True, check_pir=check_pir)
|
|
|
|
cls_name = f"BF16TestCase_{op_type}"
|
|
TestCompareOpBF16Op.__name__ = cls_name
|
|
globals()[cls_name] = TestCompareOpBF16Op
|
|
|
|
|
|
create_bf16_case('less_than', lambda _a, _b: _a < _b, True)
|
|
create_bf16_case('less_equal', lambda _a, _b: _a <= _b, True)
|
|
create_bf16_case('greater_than', lambda _a, _b: _a > _b, True)
|
|
create_bf16_case('greater_equal', lambda _a, _b: _a >= _b, True)
|
|
create_bf16_case('equal', lambda _a, _b: _a == _b, True)
|
|
create_bf16_case('not_equal', lambda _a, _b: _a != _b, True)
|
|
|
|
|
|
class TestCompareOpError(unittest.TestCase):
|
|
def test_int16_support(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
# The input x and y of compare_op must be Variable.
|
|
x = paddle.static.data(name='x', shape=[-1, 1], dtype="float32")
|
|
y = base.create_lod_tensor(
|
|
numpy.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
self.assertRaises(TypeError, paddle.greater_equal, x, y)
|
|
|
|
|
|
class API_TestElementwise_Equal(unittest.TestCase):
|
|
def test_api(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
label = paddle.assign(np.array([3, 3], dtype="int32"))
|
|
limit = paddle.assign(np.array([3, 2], dtype="int32"))
|
|
out = paddle.equal(x=label, y=limit)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
(res,) = exe.run(fetch_list=[out])
|
|
self.assertEqual((res == np.array([True, False])).all(), True)
|
|
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
label = paddle.assign(np.array([3, 3], dtype="int32"))
|
|
limit = paddle.assign(np.array([3, 3], dtype="int32"))
|
|
out = paddle.equal(x=label, y=limit)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
(res,) = exe.run(fetch_list=[out])
|
|
self.assertEqual((res == np.array([True, True])).all(), True)
|
|
|
|
def test_api_fp16(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
label = paddle.to_tensor([3, 3], dtype="float16")
|
|
limit = paddle.to_tensor([3, 2], dtype="float16")
|
|
out = paddle.equal(x=label, y=limit)
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
exe = base.Executor(place)
|
|
(res,) = exe.run(fetch_list=[out])
|
|
self.assertEqual((res == np.array([True, False])).all(), True)
|
|
|
|
|
|
class API_TestElementwise_Greater_Than(unittest.TestCase):
|
|
def test_api_fp16(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
label = paddle.to_tensor([3, 3], dtype="float16")
|
|
limit = paddle.to_tensor([3, 2], dtype="float16")
|
|
out = paddle.greater_than(x=label, y=limit)
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
(res,) = exe.run(fetch_list=[out])
|
|
self.assertEqual((res == np.array([False, True])).all(), True)
|
|
|
|
|
|
class TestCompareOpPlace(unittest.TestCase):
|
|
def test_place_1(self):
|
|
paddle.enable_static()
|
|
place = paddle.CPUPlace()
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
label = paddle.assign(np.array([3, 3], dtype="int32"))
|
|
limit = paddle.assign(np.array([3, 2], dtype="int32"))
|
|
out = paddle.less_than(label, limit)
|
|
exe = base.Executor(place)
|
|
(res,) = exe.run(fetch_list=[out])
|
|
self.assertEqual((res == np.array([False, False])).all(), True)
|
|
|
|
def test_place_2(self):
|
|
place = paddle.CPUPlace()
|
|
data_place = place
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
data_place = paddle.CUDAPinnedPlace()
|
|
paddle.disable_static(place)
|
|
data = np.array([9], dtype="int64")
|
|
data_tensor = paddle.to_tensor(data, place=data_place)
|
|
result = data_tensor == 0
|
|
self.assertEqual((result.numpy() == np.array([False])).all(), True)
|
|
|
|
|
|
class TestCompareOutAndParamAlias(unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
self.shape = [2, 3, 4, 5]
|
|
self.api_names = [
|
|
"equal", # eq
|
|
"equal",
|
|
"not_equal", # ne
|
|
"not_equal",
|
|
"less_than", # lt
|
|
"less_than", # less
|
|
"less_equal", # le
|
|
"less_equal",
|
|
"greater_than", # gt
|
|
"greater_than", # greater
|
|
"greater_equal", # ge
|
|
"greater_equal",
|
|
]
|
|
self.apis = [getattr(paddle, name) for name in self.api_names]
|
|
|
|
self.np_apis = [
|
|
np.equal,
|
|
np.equal,
|
|
np.not_equal,
|
|
np.not_equal,
|
|
np.less,
|
|
np.less,
|
|
np.less_equal,
|
|
np.less_equal,
|
|
np.greater,
|
|
np.greater,
|
|
np.greater_equal,
|
|
np.greater_equal,
|
|
]
|
|
self.input = np.random.rand(*self.shape).astype(np.float32)
|
|
self.other = np.random.rand(*self.shape).astype(np.float32)
|
|
self.other[0, 0, 3, 0] = self.input[0, 0, 3, 0]
|
|
|
|
def test_dygraph_out(self):
|
|
paddle.disable_static()
|
|
for api, np_api in zip(self.apis, self.np_apis):
|
|
x = paddle.to_tensor(self.input)
|
|
y = paddle.to_tensor(self.other)
|
|
out_holder = paddle.zeros_like(x)
|
|
out = api(x, y)
|
|
out_holder[:] = out
|
|
np.testing.assert_allclose(
|
|
out_holder.numpy(), np_api(self.input, self.other)
|
|
)
|
|
|
|
def test_dygraph_param_alias(self):
|
|
paddle.disable_static()
|
|
for api, np_api in zip(self.apis, self.np_apis):
|
|
x = paddle.to_tensor(self.input)
|
|
y = paddle.to_tensor(self.other)
|
|
out1 = api(x, y)
|
|
out2 = api(x, y)
|
|
out3 = api(x, y)
|
|
out4 = api(x, y)
|
|
for out in [out1, out2, out3, out4]:
|
|
np.testing.assert_allclose(
|
|
out.numpy(), np_api(self.input, self.other)
|
|
)
|
|
|
|
def test_dygraph_param_alias_out(self):
|
|
paddle.disable_static()
|
|
for api, np_api in zip(self.apis, self.np_apis):
|
|
x = paddle.to_tensor(self.input)
|
|
y = paddle.to_tensor(self.other)
|
|
out_holders = [paddle.zeros_like(x) for _ in range(4)]
|
|
out_holders[0][:] = api(x, y)
|
|
out_holders[1][:] = api(x, y)
|
|
out_holders[2][:] = api(x, y)
|
|
out_holders[3][:] = api(x, y)
|
|
for out in out_holders:
|
|
np.testing.assert_allclose(
|
|
out.numpy(), np_api(self.input, self.other)
|
|
)
|
|
|
|
def test_tensor_api_dygraph_param_alias(self):
|
|
paddle.disable_static()
|
|
for api, np_api in zip(self.api_names, self.np_apis):
|
|
x = paddle.to_tensor(self.input)
|
|
y = paddle.to_tensor(self.other)
|
|
api = getattr(x, api)
|
|
out1 = api(y)
|
|
out2 = api(y)
|
|
for out in [out1, out2]:
|
|
np.testing.assert_allclose(
|
|
out.numpy(), np_api(self.input, self.other)
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|