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add op analyser and minddata pipeline analyser st

tags/v0.5.0-beta
chenchao99 5 years ago
parent
fcf0df340f
commit
79a0187450
3 changed files with 404 additions and 0 deletions
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  1. +124
    -0
      tests/st/func/profiler/test_minddata_pipeline_analyser.py
  2. +225
    -0
      tests/st/func/profiler/test_op_analyser.py
  3. +55
    -0
      tests/utils/resource/profiler/pipeline_profiling_1.json

+ 124
- 0
tests/st/func/profiler/test_minddata_pipeline_analyser.py View File

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Fuction:
Test profiler to watch the performance of training.
Usage:
pytest tests/st/func/profiler
"""
import os
import shutil
from unittest import mock

import pytest

from mindinsight.profiler import Profiler
from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
from mindinsight.profiler.parser.framework_parser import FrameworkParser
from tests.st.func.profiler.conftest import BASE_SUMMARY_DIR
from tests.ut.profiler import RAW_DATA_BASE


@pytest.mark.usefixtures('create_summary_dir')
class TestMinddataPipelineAnalyser:
"""Test minddata pipeline analyser module."""
JOB_ID = 'JOB3'

@classmethod
def setup_class(cls):
"""Generate parsed files."""
cls.generate_parsed_files()

def setup_method(self):
"""Create analyser."""
self._analyser = AnalyserFactory.instance().get_analyser(
'minddata_pipeline', self.profiler, '1')

@classmethod
def generate_parsed_files(cls):
"""Test parse raw info about profiler."""
cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
cls.profiler = os.path.join(cls.summary_dir, 'profiler')
FrameworkParser._raw_data_dir = RAW_DATA_BASE
if not os.path.exists(cls.summary_dir):
os.makedirs(cls.summary_dir)
os.makedirs(cls.profiler, exist_ok=True)
pipeline_path = os.path.join(RAW_DATA_BASE, 'profiler', 'pipeline_profiling_1.json')
shutil.copy(pipeline_path, cls.profiler)
Profiler._base_profiling_container_path = os.path.join(RAW_DATA_BASE, 'container')
with mock.patch('mindinsight.profiler.profiling.PROFILING_LOG_BASE_PATH', RAW_DATA_BASE):
profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False,
output_path=cls.summary_dir, job_id=cls.JOB_ID)
profiler.analyse()

@pytest.mark.level0
@pytest.mark.env_single
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_ascend_training
def test_query(self):
"""Test the function of querying minddata pipeline infomation."""
expect_result = {
'col_name': [
'op_id', 'op_type', 'output_queue_average_size', 'output_queue_length',
'output_queue_usage_rate', 'sample_interval', 'parent_id', 'children_id'
],
'object': [
[1, 'Shuffle', 20.0, 64, 0.3125, 10, 0, [2, 3]],
[2, 'TFReader', 20.0, 64, 0.3125, 10, 1, None],
[3, 'TFReader', 20.0, 64, 0.3125, 10, 1, None],
[0, 'Batch', None, None, None, 10, None, [1]]
],
'size': 4
}
condition = {
'sort_condition': {
'name': 'output_queue_average_size',
'type': 'descending'
}
}
result = self._analyser.query(condition)
assert expect_result == result

@pytest.mark.level0
@pytest.mark.env_single
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_ascend_training
def test_get_op_and_parent_op_info(self):
"""Test the function of the target operator and queue infomation."""
expect_result = {
'current_op': {
'op_id': 1,
'op_type': 'Shuffle',
'num_workers': 1
},
'parent_op': {
'op_id': 0,
'op_type': 'Batch',
'num_workers': 4
},
'queue_info': {
'output_queue_size': [10, 20, 30],
'output_queue_average_size': 20.0,
'output_queue_length': 64,
'output_queue_usage_rate': 0.3125,
'sample_interval': 10
}
}
result = self._analyser.get_op_and_parent_op_info(1)
assert expect_result == result

+ 225
- 0
tests/st/func/profiler/test_op_analyser.py View File

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Fuction:
Test profiler to watch the performance of training.
Usage:
pytest tests/st/func/profiler
"""
import os
from unittest import mock

import pytest

from mindinsight.profiler import Profiler
from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
from mindinsight.profiler.parser.framework_parser import FrameworkParser
from tests.st.func.profiler.conftest import BASE_SUMMARY_DIR
from tests.ut.profiler import RAW_DATA_BASE


OP_GATHER_V2_INFO = {
'col_name': [
'op_name', 'op_type', 'execution_time', 'subgraph', 'full_op_name', 'op_info'
],
'object': [
[
'GatherV2-op55', 'GatherV2', 42.220212142857136, 'Default',
'Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/'
'_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op55',
{
'input_0': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_FLOAT32',
'shape': '184696,8'
},
'input_1': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_INT32',
'shape': '128000,39'
},
'output_0': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_FLOAT32',
'shape': '128000,39,8'
}
}
],
[
'GatherV2-op33', 'GatherV2', 0.9352293333333332, 'Default',
'Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/'
'_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op33',
{
'input_0': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_FLOAT32',
'shape': '184696,1'
},
'input_1': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_INT32',
'shape': '16000,39'
},
'output_0': {
'format': 'DefaultFormat',
'data_type': 'NUMBER_TYPE_FLOAT32',
'shape': '16000,39,1'
}
}
]
],
'size': 2
}


@pytest.mark.usefixtures('create_summary_dir')
class TestOpAnalyser:
"""Test AICORE and AICPU analyser module."""
JOB_ID = 'JOB3'

@classmethod
def setup_class(cls):
"""Generate parsed files."""
cls.generate_parsed_files()

def setup_method(self):
"""Create analyser."""
self._analyser_aicore_type = AnalyserFactory.instance().get_analyser(
'aicore_type', self.profiler, '1')
self._analyser_aicore_detail = AnalyserFactory.instance().get_analyser(
'aicore_detail', self.profiler, '1')

@classmethod
def generate_parsed_files(cls):
"""Test parse raw info about profiler."""
cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
cls.profiler = os.path.join(cls.summary_dir, 'profiler')
FrameworkParser._raw_data_dir = RAW_DATA_BASE
if not os.path.exists(cls.summary_dir):
os.makedirs(cls.summary_dir)
Profiler._base_profiling_container_path = os.path.join(RAW_DATA_BASE, 'container')
with mock.patch('mindinsight.profiler.profiling.PROFILING_LOG_BASE_PATH', RAW_DATA_BASE):
profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False,
output_path=cls.summary_dir, job_id=cls.JOB_ID)
profiler.analyse()

@pytest.mark.level0
@pytest.mark.env_single
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_ascend_training
def test_query_aicore_type_1(self):
"""Test the function of querying AICORE operator type infomation."""
expect_result = {
'col_name': ['op_type', 'execution_time', 'execution_frequency', 'percent'],
'object': [
['UnsortedSegmentSum', 44.60782642857142, 2, 35.28],
['GatherV2', 43.15544147619047, 2, 34.13],
['Slice', 20.376314999999998, 16, 16.12],
['Concat', 5.80845380952381, 4, 4.59],
['Split', 2.7142774761904764, 2, 2.15],
['MatMul', 1.9366814285714287, 15, 1.53],
['Mul', 1.9029486666666666, 32, 1.51],
['StridedSliceGrad', 1.5068342857142858, 2, 1.19],
['TransData', 1.1151575238095237, 30, 0.88],
['ReluGrad', 0.8540685714285714, 5, 0.68],
['Cast', 0.4846848571428572, 15, 0.38],
['ReLU', 0.48328214285714277, 5, 0.38],
['RealDiv', 0.4228071904761905, 15, 0.33],
['StridedSlice', 0.3455687619047618, 2, 0.27],
['Adam', 0.2859357142857143, 11, 0.23],
['BiasAdd', 0.18966285714285713, 5, 0.15],
['BiasAddGrad', 0.07168142857142856, 5, 0.06],
['Tile', 0.04415833333333334, 4, 0.03],
['ReduceSum', 0.030764857142857142, 5, 0.02],
['ApplyFtrl', 0.025453571428571426, 2, 0.02],
['AtomicAddrClean', 0.019368666666666666, 8, 0.02],
['AddN', 0.012836428571428572, 1, 0.01],
['Square', 0.009799333333333334, 1, 0.01],
['SigmoidCrossEntropyWithLogitsGrad', 0.009582142857142859, 2, 0.01],
['TensorAdd', 0.009218380952380952, 3, 0.01],
['SigmoidCrossEntropyWithLogits', 0.004808571428571428, 1, 0.0],
['ReduceMean', 0.004534999999999999, 1, 0.0],
['Assign', 0.0024766666666666665, 2, 0.0],
['AssignAdd', 0.001688, 1, 0.0]
],
'size': 29
}
condition = {
'sort_condition': {
'name': 'execution_time',
'type': 'descending'
}
}
result = self._analyser_aicore_type.query(condition)
assert expect_result == result

@pytest.mark.level0
@pytest.mark.env_single
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_ascend_training
def test_query_aicore_type_2(self):
"""Test the function of querying AICORE operator type infomation."""
expect_result = {
'col_name': ['op_type', 'execution_time', 'execution_frequency', 'percent'],
'object': [
['MatMul', 1.9366814285714287, 15, 1.53],
['Mul', 1.9029486666666666, 32, 1.51]
],
'size': 2
}
condition = {
'filter_condition': {
'op_type': {
'partial_match_str_in': ['Mul']
}
},
'sort_condition': {
'name': 'execution_time',
'type': 'descending'
}
}
result = self._analyser_aicore_type.query(condition)
assert expect_result == result

@pytest.mark.level0
@pytest.mark.env_single
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_ascend_training
def test_query_aicore_detail_1(self):
"""Test the function of querying AICORE operator detail infomation."""
expect_result = OP_GATHER_V2_INFO
condition = {
'filter_condition': {
'op_type': {
'in': ['GatherV2']
}
},
'sort_condition': {
'name': 'execution_time',
'type': 'descending'
},
'group_condition': {
'limit': 10,
'offset': 0
}
}
result = self._analyser_aicore_detail.query(condition)
assert expect_result == result

+ 55
- 0
tests/utils/resource/profiler/pipeline_profiling_1.json View File

@@ -0,0 +1,55 @@
{
"sampling_interval": 10,
"op_info": [
{
"op_id": 4,
"op_type": "TFReader",
"num_workers": 4,
"metrics": null,
"children": [3]
},
{
"op_id": 3,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 2,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 1,
"op_type": "Shuffle",
"num_workers": 1,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": [2, 4]
},
{
"op_id": 0,
"op_type": "Batch",
"num_workers": 4,
"metrics": null,
"children": [1]
}
]
}

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