120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
# Copyright 2021 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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"""Test ascend profiling."""
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import glob
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import tempfile
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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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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.dataset as ds
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from mindspore import Profiler
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from mindspore import Model
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from tests.security_utils import security_off_wrap
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.add = P.Add()
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def construct(self, x_, y_):
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return self.add(x_, y_)
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x = np.random.randn(1, 3, 3, 4).astype(np.float32)
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y = np.random.randn(1, 3, 3, 4).astype(np.float32)
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class NetWork(nn.Cell):
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def __init__(self):
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super(NetWork, self).__init__()
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self.unique = P.Unique()
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self.shape = P.TensorShape()
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self.reshape = P.Reshape()
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self.add = P.Add()
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def construct(self, a, b):
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val = self.add(a, b)
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size = self.shape(val)
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res = self.reshape(val, size)
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return res
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def dataset_generator():
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for i in range(1, 10):
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yield (np.ones((32, 2 * i), dtype=np.float32), np.ones((32, 2 * i), dtype=np.float32))
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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_profiling():
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"""Test ascend profiling"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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with tempfile.TemporaryDirectory() as tmpdir:
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profiler = Profiler(output_path=tmpdir)
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add = Net()
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add(Tensor(x), Tensor(y))
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profiler.analyse()
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assert len(glob.glob(f"{tmpdir}/profiler*/*PROF*/device_*/data/Framework*")) == 4
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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_pynative_profiling():
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"""
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Feature: Test the ascend pynative model profiling
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Description: Generate the Net op timeline
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Expectation: Timeline generated successfully
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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with tempfile.TemporaryDirectory() as tmpdir:
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profiler = Profiler(output_path=tmpdir)
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add = Net()
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add(Tensor(x), Tensor(y))
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profiler.analyse()
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assert len(glob.glob(f"{tmpdir}/profiler*/output_timeline_data_*.txt")) == 1
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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_shape():
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"""
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Feature: Test the ascend dynamic shape model profiling
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Description: Generate the Net dynamic shape data.
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Expectation: Dynamic shape data generated successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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with tempfile.TemporaryDirectory() as tmpdir:
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network = NetWork()
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profiler = Profiler(output_path=tmpdir)
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dataset = ds.GeneratorDataset(dataset_generator, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [32, None], "data2": [32, None]})
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model = Model(network)
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model.train(1, dataset, dataset_sink_mode=True)
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profiler.analyse()
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assert len(glob.glob(f"{tmpdir}/profiler*/dynamic_shape_*.json")) == 1
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