wehub-resource-sync
456 lines
16 KiB
Python
456 lines
16 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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class ApiFMaxTest(unittest.TestCase):
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"""ApiFMaxTest"""
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def setUp(self):
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"""setUp"""
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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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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(10, 15).astype("float32")
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self.input_y = np.random.rand(10, 15).astype("float32")
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self.input_z = np.random.rand(15).astype("float32")
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self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
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self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
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self.input_c = np.array([4, 1, 3]).astype('int64')
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self.np_expected1 = np.fmax(self.input_x, self.input_y)
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self.np_expected2 = np.fmax(self.input_x, self.input_z)
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self.np_expected3 = np.fmax(self.input_a, self.input_c)
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self.np_expected4 = np.fmax(self.input_b, self.input_c)
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def test_static_api(self):
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"""test_static_api"""
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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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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
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result_fmax = paddle.fmax(data_x, data_y)
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exe = paddle.static.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=[result_fmax],
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)
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np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
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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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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_z = paddle.static.data("z", shape=[15], dtype="float32")
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result_fmax = paddle.fmax(data_x, data_z)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "z": self.input_z},
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fetch_list=[result_fmax],
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)
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np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
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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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data_a = paddle.static.data("a", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_fmax = paddle.fmax(data_a, data_c)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"a": self.input_a, "c": self.input_c},
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fetch_list=[result_fmax],
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)
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np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
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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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data_b = paddle.static.data("b", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_fmax = paddle.fmax(data_b, data_c)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"b": self.input_b, "c": self.input_c},
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fetch_list=[result_fmax],
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)
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np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
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def test_dynamic_api(self):
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"""test_dynamic_api"""
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paddle.disable_static()
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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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z = paddle.to_tensor(self.input_z)
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a = paddle.to_tensor(self.input_a)
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b = paddle.to_tensor(self.input_b)
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c = paddle.to_tensor(self.input_c)
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res = paddle.fmax(x, y)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
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# test broadcast
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res = paddle.fmax(x, z)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
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res = paddle.fmax(a, c)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
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res = paddle.fmax(b, c)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
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class TestElementwiseFmaxOp(OpTest):
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"""TestElementwiseFmaxOp"""
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def setUp(self):
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"""setUp"""
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self.op_type = "elementwise_fmax"
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self.prim_op_type = "prim"
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self.python_api = paddle.fmax
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self.public_python_api = paddle.fmax
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# If x and y have the same value, the max() is not differentiable.
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# So we generate test data by the following method
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# to avoid them being too close to each other.
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self.init_shape()
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x = np.random.uniform(0.1, 1, self.shape).astype("float64")
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sgn = np.random.choice([-1, 1], self.shape).astype("float64")
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y = x + sgn * np.random.uniform(0.1, 1, self.shape).astype("float64")
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.fmax(self.inputs['X'], self.inputs['Y'])}
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def init_shape(self):
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self.shape = [13, 17]
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def test_check_output(self):
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"""test_check_output"""
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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"""test_check_grad_normal"""
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self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
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def test_check_grad_ignore_x(self):
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"""test_check_grad_ignore_x"""
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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set("X"),
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check_pir=True,
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)
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def test_check_grad_ignore_y(self):
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"""test_check_grad_ignore_y"""
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set('Y'),
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check_pir=True,
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)
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class TestElementwiseFmax2Op(OpTest):
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"""TestElementwiseFmax2Op"""
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def setUp(self):
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"""setUp"""
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self.op_type = "elementwise_fmax"
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self.prim_op_type = "prim"
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self.python_api = paddle.fmax
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self.public_python_api = paddle.fmax
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# If x and y have the same value, the max() is not differentiable.
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# So we generate test data by the following method
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# to avoid them being too close to each other.
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x = np.random.uniform(0.1, 1, [13, 17]).astype("float64")
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sgn = np.random.choice([-1, 1], [13, 17]).astype("float64")
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y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float64")
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y[2, 10:] = np.nan
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.fmax(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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"""test_check_output"""
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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"""test_check_grad_normal"""
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self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
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def test_check_grad_ignore_x(self):
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"""test_check_grad_ignore_x"""
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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set("X"),
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check_pir=True,
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)
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def test_check_grad_ignore_y(self):
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"""test_check_grad_ignore_y"""
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set('Y'),
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check_pir=True,
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)
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class TestElementwiseFmax3Op(OpTest):
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"""TestElementwiseFmax3Op"""
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def setUp(self):
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"""setUp"""
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self.op_type = "elementwise_fmax"
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self.prim_op_type = "prim"
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self.python_api = paddle.fmax
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self.public_python_api = paddle.fmax
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# If x and y have the same value, the max() is not differentiable.
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# So we generate test data by the following method
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# to avoid them being too close to each other.
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x = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
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sgn = np.random.choice([-1, 1], [13, 17]).astype("float16")
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y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float16")
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.fmax(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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"""test_check_output"""
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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"""test_check_grad_normal"""
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self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFmaxBF16OP(OpTest):
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def setUp(self):
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self.op_type = "elementwise_fmax"
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self.prim_op_type = "prim"
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self.python_api = paddle.fmax
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self.public_python_api = paddle.fmax
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self.dtype = np.uint16
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x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
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sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
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y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
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out = np.fmax(x, y)
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self.inputs = {
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'X': convert_float_to_uint16(x),
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'Y': convert_float_to_uint16(y),
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}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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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(
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place, check_pir=True, check_symbol_infer=False
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)
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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, ['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True
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)
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class TestElementwiseFmaxOpZeroSize(TestElementwiseFmaxOp):
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def init_shape(self):
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self.shape = [0, 15]
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class TestElementwiseFmaxOpZeroSize1(TestElementwiseFmaxOp):
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def init_shape(self):
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self.shape = [0, 15, 0]
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class ApiFMaxTestZeroSize(unittest.TestCase):
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"""ApiFMaxTest"""
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def setUp(self):
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"""setUp"""
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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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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(0, 15).astype("float32")
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self.input_y = np.random.rand(0, 15).astype("float32")
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self.input_z = np.random.rand(1, 15).astype("float32")
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self.input_a = np.random.rand(15, 0).astype('int64')
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self.input_b = np.random.rand(15, 0, 1).astype('int64')
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self.input_c = np.random.rand(15, 0, 2).astype('int64')
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self.np_expected1 = np.fmax(self.input_x, self.input_y)
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self.np_expected2 = np.fmax(self.input_x, self.input_z)
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self.np_expected3 = np.fmax(self.input_a, self.input_c)
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self.np_expected4 = np.fmax(self.input_b, self.input_c)
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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 TestElementwiseFmaxOp_Stride(OpTest):
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "elementwise_fmax"
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self.python_api = paddle.fmax
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self.public_python_api = paddle.fmax
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self.transpose_api = paddle.transpose
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self.as_stride_api = paddle.as_strided
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self.init_dtype()
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self.init_input_output()
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self.inputs_stride = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
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}
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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def init_dtype(self):
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self.dtype = np.float64
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self.val_dtype = np.float64
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def test_check_output(self):
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place = get_device_place()
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self.check_strided_forward = True
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self.check_output(
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place,
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)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def test_check_gradient(self):
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pass
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class TestElementwiseFmaxOp_Stride1(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFmaxOp_Stride2(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFmaxOp_Stride3(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFmaxOp_Stride4(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFmaxOp_Stride5(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = np.fmax(self.x, self.y)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseFmaxOp_Stride_ZeroDim1(TestElementwiseFmaxOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.fmax(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFmaxOp_Stride_ZeroSize1(TestElementwiseFmaxOp_Stride):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = np.fmax(self.x, self.y)
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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if __name__ == "__main__":
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unittest.main()
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