wehub-resource-sync
288 lines
9.4 KiB
Python
288 lines
9.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle import base, tensor
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from paddle.base import core
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class TestTraceOp(OpTest):
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def setUp(self):
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self.op_type = "trace"
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self.python_api = paddle.trace
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self.init_config()
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self.outputs = {'Out': self.target}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['Input'], 'Out', check_pir=True)
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def init_config(self):
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self.case = np.random.randn(20, 6).astype('float64')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
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self.target = np.trace(self.inputs['Input'])
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class TestTraceOpCase1(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(2, 20, 2, 3).astype('float32')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 1, 'axis1': 0, 'axis2': 2}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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class TestTraceOpCase2(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(2, 20, 2, 3).astype('float32')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
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self.__class__.exist_check_grad = True
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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class TestTraceOpCase3(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(0, 3, 2).astype('float64')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -1, 'axis1': 2, 'axis2': -2}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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class TestTraceOpCase4(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(2, 30, 3).astype('float64')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -1, 'axis1': 2, 'axis2': -2}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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class TestTraceFP16Op1(TestTraceOp):
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def init_config(self):
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self.dtype = np.float16
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self.case = np.random.randn(20, 6).astype(self.dtype)
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
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self.target = np.trace(self.inputs['Input'])
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class TestTraceFP16Op2(TestTraceOp):
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def init_config(self):
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self.dtype = np.float16
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self.case = np.random.randn(2, 20, 2, 3).astype(self.dtype)
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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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 bfloat16",
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)
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class TestTraceBF16Op1(OpTest):
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def setUp(self):
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self.op_type = "trace"
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self.python_api = paddle.trace
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self.init_config()
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self.outputs = {'Out': self.target}
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self.inputs['Input'] = convert_float_to_uint16(self.inputs['Input'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(self.place, check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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['Input'],
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'Out',
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numeric_grad_delta=0.02,
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check_pir=True,
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)
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def init_config(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.case = np.random.randn(20, 6).astype(self.np_dtype)
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
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self.target = np.trace(self.inputs['Input'])
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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 bfloat16",
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)
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class TestTraceBF16Op2(TestTraceBF16Op1):
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def init_config(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.case = np.random.randn(2, 20, 2, 3).astype(self.np_dtype)
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'],
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)
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class TestTraceAPICase(unittest.TestCase):
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def test_case1(self):
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with paddle.static.program_guard(paddle.static.Program()):
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case = np.random.randn(2, 20, 2, 3).astype('float32')
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data1 = paddle.static.data(
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name='data1', shape=[2, 20, 2, 3], dtype='float32'
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)
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out1 = tensor.trace(data1)
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out2 = tensor.trace(data1, offset=-5, axis1=1, axis2=-1)
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place = core.CPUPlace()
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exe = base.Executor(place)
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results = exe.run(
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paddle.static.default_main_program(),
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feed={"data1": case},
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fetch_list=[out1, out2],
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return_numpy=True,
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)
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target1 = np.trace(case)
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target2 = np.trace(case, offset=-5, axis1=1, axis2=-1)
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np.testing.assert_allclose(results[0], target1, rtol=1e-05)
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np.testing.assert_allclose(results[1], target2, rtol=1e-05)
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class TestTraceAPIZerodimCase(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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self.x = np.random.random([5, 0, 0, 0]).astype('float32')
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def test_dygraph(self):
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paddle.disable_static()
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for place in self.places:
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x = paddle.to_tensor(self.x, place=place)
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params = [
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(0, 1, 2),
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(1, 0, 1),
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(-1, 2, 0),
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(2, 1, 2),
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(0, -1, -2),
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(5, 1, 2),
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(-5, 2, 0),
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]
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for offset, axis1, axis2 in params:
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paddle_res = paddle.trace(
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x, offset=offset, axis1=axis1, axis2=axis2
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)
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np_res = np.trace(
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self.x, offset=offset, axis1=axis1, axis2=axis2
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)
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self.assertEqual(tuple(paddle_res.shape), np_res.shape)
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np.testing.assert_allclose(paddle_res, np_res, rtol=1e-6)
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paddle.enable_static()
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def test_static(self):
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with paddle.static.program_guard(paddle.static.Program()):
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case = np.random.randn(2, 0, 0, 0).astype('float32')
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data1 = paddle.static.data(
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name='data1', shape=[2, 0, 0, 0], dtype='float32'
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)
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params = [
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(0, 1, 2),
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(-5, 1, -1),
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(2, 0, 1),
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(0, 2, 1),
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(1, 0, 2),
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(-1, 1, 0),
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(0, -2, -1),
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]
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for offset, axis1, axis2 in params:
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out = tensor.trace(
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data1, offset=offset, axis1=axis1, axis2=axis2
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)
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place = core.CPUPlace()
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exe = base.Executor(place)
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result = exe.run(
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paddle.static.default_main_program(),
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feed={"data1": case},
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fetch_list=[out],
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return_numpy=True,
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)[0]
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target = np.trace(case, offset=offset, axis1=axis1, axis2=axis2)
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self.assertEqual(tuple(result.shape), target.shape)
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np.testing.assert_allclose(result, target, rtol=1e-5)
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# Test alias for 'input'
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class TestTraceAlias(unittest.TestCase):
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def test_alias(self):
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with base.dygraph.guard():
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x_np = np.random.random((3, 3)).astype("float32")
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x = paddle.to_tensor(x_np)
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# 1. Standard call
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out_ref = paddle.trace(x)
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# 2. Test alias: input -> x
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out_alias = paddle.trace(input=x)
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np.testing.assert_array_equal(out_ref.numpy(), out_alias.numpy())
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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