160 lines
6.0 KiB
Python
160 lines
6.0 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 os
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import sys
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import tempfile
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import glob
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import shutil
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import pytest
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import numpy as np
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import mindspore as ms
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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 as ops
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from mindspore import Tensor
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from dump_test_utils import generate_dump_json, check_dump_structure
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from tests.security_utils import security_off_wrap
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class ConvNet(nn.Cell):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.conv2 = ops.Conv2D(out_channel=3, kernel_size=1)
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def construct(self, x, weight):
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return self.conv2(x, weight)
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def run_trans_flag(test_name):
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if sys.platform != 'linux':
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return
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, test_name)
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dump_config_path = os.path.join(tmp_dir, '{}.json'.format(test_name))
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generate_dump_json(dump_path, dump_config_path, test_name)
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os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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net = ConvNet()
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tensor = Tensor(np.ones([1, 3, 3, 3]), ms.float32)
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weight = Tensor(np.ones([3, 3, 1, 1]), ms.float32)
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expect = net(tensor, weight)
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check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
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dump_data_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
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assert os.path.exists(dump_data_path)
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if test_name == "test_e2e_dump_trans_true":
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# tensor data in host format.
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output_name = "Conv2D.Conv2D-op*.0.0.*.output.0.DefaultFormat.npy"
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output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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output = np.load(real_path)
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assert output.shape == (1, 3, 3, 3)
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assert np.array_equal(output, expect)
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elif test_name == "test_e2e_dump_trans_false":
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# tensor data in device format.
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output_name = "Conv2D.Conv2D-op*.0.0.*.output.0.NC1HWC0.npy"
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output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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output = np.load(real_path)
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assert output.shape == (1, 1, 3, 3, 16)
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else:
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# tensor data in host format.
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output_name = "Conv2D.Conv2D-op*.*.*.*.output.0.NCHW.npy"
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output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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output = np.load(real_path)
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assert output.shape == (1, 3, 3, 3)
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assert np.array_equal(output, expect)
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del os.environ['MINDSPORE_DUMP_CONFIG']
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_ascend_e2e_trans_true():
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"""
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Feature: Ascend e2e dump.
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Description: Test e2e dump in Ascend with trans_flag is configured to true.
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Expectation: Dump files has tensor data in host format (4 dimensions).
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_trans_flag("test_e2e_dump_trans_true")
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_ascend_e2e_trans_false():
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"""
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Feature: Ascend e2e dump.
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Description: Test e2e dump in Ascend with trans_flag is configured to false.
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Expectation: Dump files has tensor data in device format.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_trans_flag("test_e2e_dump_trans_false")
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_ascend_kernel_by_kernel_trans_true():
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"""
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Feature: Ascend kernel by kernel dump.
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Description: Test kernel by kernel dump in Ascend with trans_flag is configured to true.
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Expectation: Dump files has tensor data in host format (4 dimensions).
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"""
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os.environ['GRAPH_OP_RUN'] = "1"
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_trans_flag("test_e2e_dump_trans_true")
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del os.environ['GRAPH_OP_RUN']
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_ascend_kernel_by_kernel_trans_false():
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"""
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Feature: Ascend kernel by kernel dump.
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Description: Test kernel by kernel dump in Ascend with trans_flag is configured to false.
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Expectation: Dump files has tensor data in device format.
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"""
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os.environ['GRAPH_OP_RUN'] = "1"
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_trans_flag("test_e2e_dump_trans_false")
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del os.environ['GRAPH_OP_RUN']
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_ascend_a_plus_m_conversion():
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"""
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Feature: Ascend A+M dump.
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Description: Test A+M dump in Ascend and check the format of the dump data.
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Expectation: Dump files has tensor data in host format (4 dimensions).
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_trans_flag("test_async_dump_npy")
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