diff --git a/cmake/package.cmake b/cmake/package.cmake index 99f6f7bd197..2981294bbf3 100644 --- a/cmake/package.cmake +++ b/cmake/package.cmake @@ -326,6 +326,7 @@ install( ${CMAKE_SOURCE_DIR}/mindspore/profiler ${CMAKE_SOURCE_DIR}/mindspore/explainer ${CMAKE_SOURCE_DIR}/mindspore/compression + ${CMAKE_SOURCE_DIR}/mindspore/run_check DESTINATION ${INSTALL_PY_DIR} COMPONENT mindspore ) diff --git a/mindspore/__init__.py b/mindspore/__init__.py index d66d5044e66..1e0c8978fe9 100755 --- a/mindspore/__init__.py +++ b/mindspore/__init__.py @@ -14,7 +14,7 @@ # ============================================================================ """.. MindSpore package.""" -from ._check_version import check_version_and_env_config +from .run_check import run_check from . import common, train, log from .common import * from .ops import _op_impl @@ -22,8 +22,10 @@ from .train import * from .log import * from .version import __version__ +all = ["run_check"] __all__ = [] __all__.extend(__version__) +__all__.extend(run_check.__all__) __all__.extend(common.__all__) __all__.extend(train.__all__) __all__.extend(log.__all__) diff --git a/mindspore/run_check/__init__.py b/mindspore/run_check/__init__.py new file mode 100644 index 00000000000..e447e82475d --- /dev/null +++ b/mindspore/run_check/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2021 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. +# ============================================================================ +""".. run_check package.""" + +from .run_check import run_check +from ._check_version import check_version_and_env_config + +__all__ = ['run_check'] diff --git a/mindspore/_check_deps_version.py b/mindspore/run_check/_check_deps_version.py similarity index 100% rename from mindspore/_check_deps_version.py rename to mindspore/run_check/_check_deps_version.py diff --git a/mindspore/_check_version.py b/mindspore/run_check/_check_version.py similarity index 98% rename from mindspore/_check_version.py rename to mindspore/run_check/_check_version.py index 2791036cb93..9523ee25c57 100644 --- a/mindspore/_check_version.py +++ b/mindspore/run_check/_check_version.py @@ -20,9 +20,9 @@ from pathlib import Path from abc import abstractmethod, ABCMeta import numpy as np from packaging import version -from . import log as logger -from .version import __version__ -from .default_config import __package_name__ +from mindspore import log as logger +from ..version import __version__ +from ..default_config import __package_name__ class EnvChecker(metaclass=ABCMeta): @@ -282,7 +282,8 @@ class AscendEnvChecker(EnvChecker): input_args = ["--mindspore_version=" + __version__] for v in self.version: input_args.append("--supported_version=" + v) - deps_version_checker = os.path.join(os.path.split(os.path.realpath(__file__))[0], "_check_deps_version.py") + deps_version_checker = os.path.join(os.path.split(os.path.realpath(__file__))[0], + "_check_deps_version.py") call_cmd = [sys.executable, deps_version_checker] + input_args try: process = subprocess.run(call_cmd, timeout=3, text=True, capture_output=True, check=False) @@ -390,7 +391,7 @@ def check_version_and_env_config(): try: # check version of ascend site or cuda env_checker.check_version() - from . import _c_expression + from .. import _c_expression env_checker.set_env() except ImportError as e: env_checker.check_env(e) diff --git a/mindspore/run_check/run_check.py b/mindspore/run_check/run_check.py new file mode 100644 index 00000000000..5c451706a82 --- /dev/null +++ b/mindspore/run_check/run_check.py @@ -0,0 +1,67 @@ +# Copyright 2021 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. +# ============================================================================ + +""" +mindspore.run_check + +The goal is to provide a convenient API to check if the installation is successful or failed. +""" + +import numpy as np +from importlib import import_module + + +try: + ms = import_module("mindspore") +except ModuleNotFoundError: + ms = None + + +def _check_mul(): + """ + Define the mul method. + """ + input_x = ms.Tensor(np.array([1.0, 2.0, 3.0]), ms.float32) + input_y = ms.Tensor(np.array([4.0, 5.0, 6.0]), ms.float32) + mul = ms.ops.Mul() + mul(input_x, input_y) + print(f"The result of multiplication calculation is correct, MindSpore has been installed successfully!") + + +def _check_install(): + """ + Define the check install method. + Print MindSpore version. + """ + print(f"MindSpore version:", ms.__version__) + + +def run_check(): + """ + Provide a convenient API to check if the installation is successful or failed. + + Examples: + >>> import mindspore + >>> mindspore.run_check() + MindSpore version: xxx + The result of multiplication calculation is correct, MindSpore has been installed successfully! + """ + try: + _check_install() + _check_mul() + # pylint: disable=broad-except + except Exception as e: + print("MindSpore installation failed!") + print("CheckFailed: ", str(e)) diff --git a/tests/st/profiler/test_profiler.py b/tests/st/profiler/test_profiler.py index d75068014e4..48115c5574f 100644 --- a/tests/st/profiler/test_profiler.py +++ b/tests/st/profiler/test_profiler.py @@ -1,187 +1,187 @@ -# 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. -# ============================================================================ -import os -import shutil - -import pytest - -from mindspore import dataset as ds -from mindspore import nn, Tensor, context -from mindspore.nn.metrics import Accuracy -from mindspore.nn.optim import Momentum -from mindspore.dataset.transforms import c_transforms as C -from mindspore.dataset.vision import c_transforms as CV -from mindspore.dataset.vision import Inter -from mindspore.common import dtype as mstype -from mindspore.common.initializer import TruncatedNormal -from mindspore.train import Model -from mindspore.profiler import Profiler - - -def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): - """weight initial for conv layer""" - weight = weight_variable() - return nn.Conv2d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, padding=padding, - weight_init=weight, has_bias=False, pad_mode="valid") - - -def fc_with_initialize(input_channels, out_channels): - """weight initial for fc layer""" - weight = weight_variable() - bias = weight_variable() - return nn.Dense(input_channels, out_channels, weight, bias) - - -def weight_variable(): - """weight initial""" - return TruncatedNormal(0.02) - - -class LeNet5(nn.Cell): - """Define LeNet5 network.""" - def __init__(self, num_class=10, channel=1): - super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = conv(channel, 6, 5) - self.conv2 = conv(6, 16, 5) - self.fc1 = fc_with_initialize(16 * 5 * 5, 120) - self.fc2 = fc_with_initialize(120, 84) - self.fc3 = fc_with_initialize(84, self.num_class) - self.relu = nn.ReLU() - self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) - self.flatten = nn.Flatten() - self.channel = Tensor(channel) - - def construct(self, data): - """define construct.""" - output = self.conv1(data) - output = self.relu(output) - output = self.max_pool2d(output) - output = self.conv2(output) - output = self.relu(output) - output = self.max_pool2d(output) - output = self.flatten(output) - output = self.fc1(output) - output = self.relu(output) - output = self.fc2(output) - output = self.relu(output) - output = self.fc3(output) - return output - - -def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): - """create dataset for train""" - # define dataset - mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100) - - resize_height, resize_width = 32, 32 - rescale = 1.0 / 255.0 - rescale_nml = 1 / 0.3081 - shift_nml = -1 * 0.1307 / 0.3081 - - # define map operations - resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode - rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) - rescale_op = CV.Rescale(rescale, shift=0.0) - hwc2chw_op = CV.HWC2CHW() - type_cast_op = C.TypeCast(mstype.int32) - - # apply map operations on images - mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) - - # apply DatasetOps - mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) - mnist_ds = mnist_ds.repeat(repeat_size) - - return mnist_ds - - -def cleanup(): - data_path = os.path.join(os.getcwd(), "data") - kernel_meta_path = os.path.join(os.getcwd(), "kernel_data") - cache_path = os.path.join(os.getcwd(), "__pycache__") - if os.path.exists(data_path): - shutil.rmtree(data_path) - if os.path.exists(kernel_meta_path): - shutil.rmtree(kernel_meta_path) - if os.path.exists(cache_path): - shutil.rmtree(cache_path) - - -class TestProfiler: - device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0 - mnist_path = '/home/workspace/mindspore_dataset/mnist' - - @classmethod - def teardown_class(cls): - """ Run after class end.""" - cleanup() - - @pytest.mark.level0 - @pytest.mark.platform_x86_gpu_training - @pytest.mark.env_onecard - def test_gpu_profiler(self): - context.set_context(mode=context.GRAPH_MODE, device_target="GPU") - profiler = Profiler(output_path='data') - profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0] - self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/') - ds_train = create_dataset(os.path.join(self.mnist_path, "train")) - if ds_train.get_dataset_size() == 0: - raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") - - lenet = LeNet5() - loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") - optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9) - model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()}) - - model.train(1, ds_train, dataset_sink_mode=True) - profiler.analyse() - - self._check_gpu_profiling_file() - - def _check_gpu_profiling_file(self): - op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv' - op_type_file = self.profiler_path + f'gpu_op_type_info_{self.device_id}.csv' - activity_file = self.profiler_path + f'gpu_activity_data_{self.device_id}.csv' - timeline_file = self.profiler_path + f'gpu_timeline_display_{self.device_id}.json' - getnext_file = self.profiler_path + f'minddata_getnext_profiling_{self.device_id}.txt' - pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv' - - assert os.path.exists(op_detail_file) - assert os.path.exists(op_type_file) - assert os.path.exists(activity_file) - assert os.path.exists(timeline_file) - assert os.path.exists(getnext_file) - assert os.path.exists(pipeline_file) - - def _check_d_profiling_file(self): - aicore_file = self.profiler_path + f'aicore_intermediate_{self.device_id}_detail.csv' - step_trace_file = self.profiler_path + f'step_trace_raw_{self.device_id}_detail_time.csv' - timeline_file = self.profiler_path + f'ascend_timeline_display_{self.device_id}.json' - aicpu_file = self.profiler_path + f'aicpu_intermediate_{self.device_id}.csv' - minddata_pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv' - queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.device_id}.txt' - - assert os.path.exists(aicore_file) - assert os.path.exists(step_trace_file) - assert os.path.exists(timeline_file) - assert os.path.exists(queue_profiling_file) - assert os.path.exists(minddata_pipeline_file) - assert os.path.exists(aicpu_file) +# 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. +# ============================================================================ +import os +import shutil + +import pytest + +from mindspore import dataset as ds +from mindspore import nn, Tensor, context +from mindspore.nn.metrics import Accuracy +from mindspore.nn.optim import Momentum +from mindspore.dataset.transforms import c_transforms as C +from mindspore.dataset.vision import c_transforms as CV +from mindspore.dataset.vision import Inter +from mindspore.common import dtype as mstype +from mindspore.common.initializer import TruncatedNormal +from mindspore.train import Model +from mindspore.profiler import Profiler + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +class LeNet5(nn.Cell): + """Define LeNet5 network.""" + def __init__(self, num_class=10, channel=1): + super(LeNet5, self).__init__() + self.num_class = num_class + self.conv1 = conv(channel, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16 * 5 * 5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + self.channel = Tensor(channel) + + def construct(self, data): + """define construct.""" + output = self.conv1(data) + output = self.relu(output) + output = self.max_pool2d(output) + output = self.conv2(output) + output = self.relu(output) + output = self.max_pool2d(output) + output = self.flatten(output) + output = self.fc1(output) + output = self.relu(output) + output = self.fc2(output) + output = self.relu(output) + output = self.fc3(output) + return output + + +def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): + """create dataset for train""" + # define dataset + mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift=0.0) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = C.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + + +def cleanup(): + data_path = os.path.join(os.getcwd(), "data") + kernel_meta_path = os.path.join(os.getcwd(), "kernel_data") + cache_path = os.path.join(os.getcwd(), "__pycache__") + if os.path.exists(data_path): + shutil.rmtree(data_path) + if os.path.exists(kernel_meta_path): + shutil.rmtree(kernel_meta_path) + if os.path.exists(cache_path): + shutil.rmtree(cache_path) + + +class TestProfiler: + device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0 + mnist_path = '/home/workspace/mindspore_dataset/mnist' + + @classmethod + def teardown_class(cls): + """ Run after class end.""" + cleanup() + + @pytest.mark.level1 + @pytest.mark.platform_x86_gpu_training + @pytest.mark.env_onecard + def test_gpu_profiler(self): + context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + profiler = Profiler(output_path='data') + profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0] + self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/') + ds_train = create_dataset(os.path.join(self.mnist_path, "train")) + if ds_train.get_dataset_size() == 0: + raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") + + lenet = LeNet5() + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9) + model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()}) + + model.train(1, ds_train, dataset_sink_mode=True) + profiler.analyse() + + self._check_gpu_profiling_file() + + def _check_gpu_profiling_file(self): + op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv' + op_type_file = self.profiler_path + f'gpu_op_type_info_{self.device_id}.csv' + activity_file = self.profiler_path + f'gpu_activity_data_{self.device_id}.csv' + timeline_file = self.profiler_path + f'gpu_timeline_display_{self.device_id}.json' + getnext_file = self.profiler_path + f'minddata_getnext_profiling_{self.device_id}.txt' + pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv' + + assert os.path.exists(op_detail_file) + assert os.path.exists(op_type_file) + assert os.path.exists(activity_file) + assert os.path.exists(timeline_file) + assert os.path.exists(getnext_file) + assert os.path.exists(pipeline_file) + + def _check_d_profiling_file(self): + aicore_file = self.profiler_path + f'aicore_intermediate_{self.device_id}_detail.csv' + step_trace_file = self.profiler_path + f'step_trace_raw_{self.device_id}_detail_time.csv' + timeline_file = self.profiler_path + f'ascend_timeline_display_{self.device_id}.json' + aicpu_file = self.profiler_path + f'aicpu_intermediate_{self.device_id}.csv' + minddata_pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv' + queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.device_id}.txt' + + assert os.path.exists(aicore_file) + assert os.path.exists(step_trace_file) + assert os.path.exists(timeline_file) + assert os.path.exists(queue_profiling_file) + assert os.path.exists(minddata_pipeline_file) + assert os.path.exists(aicpu_file)