forked from mindspore-Ecosystem/mindspore
359 lines
9.4 KiB
Python
359 lines
9.4 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations.array_ops as P
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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class TrilNet(nn.Cell):
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def __init__(self, nptype, diagonal):
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super(TrilNet, self).__init__()
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self.tril = P.Tril(diagonal=diagonal)
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self.x_np = np.random.randn(2, 3, 4).astype(nptype)
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self.x_ms = Tensor(self.x_np)
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@ms_function
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def construct(self):
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return self.tril(self.x_ms)
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class TriuNet(nn.Cell):
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def __init__(self, nptype, diagonal):
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super(TriuNet, self).__init__()
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self.triu = P.Triu(diagonal=diagonal)
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self.x_np = np.random.randn(2, 3, 4).astype(nptype)
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self.x_ms = Tensor(self.x_np)
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@ms_function
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def construct(self):
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return self.triu(self.x_ms)
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def tril_triu(nptype, diagonal):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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tril_ = TrilNet(nptype, diagonal)
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triu_ = TriuNet(nptype, diagonal)
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tril_output = tril_()
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triu_output = triu_()
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tril_expect = np.tril(tril_.x_np, diagonal).astype(nptype)
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triu_expect = np.triu(triu_.x_np, diagonal).astype(nptype)
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assert (tril_output.asnumpy() == tril_expect).all()
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assert (triu_output.asnumpy() == triu_expect).all()
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def tril_triu_pynative(nptype, diagonal):
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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tril_ = TrilNet(nptype, diagonal)
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triu_ = TriuNet(nptype, diagonal)
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tril_output = tril_()
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triu_output = triu_()
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tril_expect = np.tril(tril_.x_np, diagonal).astype(nptype)
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triu_expect = np.triu(triu_.x_np, diagonal).astype(nptype)
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assert (tril_output.asnumpy() == tril_expect).all()
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assert (triu_output.asnumpy() == triu_expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_uint8():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.uint8, -5)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_uint16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.uint16, -4)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_uint32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.uint32, -3)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_uint64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.uint64, -2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_int8():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.int8, -1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_int16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.int16, 0)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_int32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.int32, 1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_int64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.int64, 2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_float16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.float16, 3)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_float32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.float32, 4)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_float64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.float64, 5)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_graph_bool():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu(np.bool, 6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_uint8():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.uint8, -5)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_uint16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.uint16, -4)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_uint32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.uint32, -3)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_uint64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.uint64, -2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_int8():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.int8, -1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_int16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.int16, 0)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_int32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.int32, 1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_int64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.int64, 2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_float16():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.float16, 3)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_float32():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.float32, 4)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_float64():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.float64, 5)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tril_triu_pynative_bool():
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"""
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Feature: ALL To ALL
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Description: test cases for Tril and Triu
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Expectation: the result match to numpy
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"""
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tril_triu_pynative(np.bool, 6)
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