forked from mindspore-Ecosystem/mindspore
!16747 offline gpu case of test_gpu_profiler
From: @xsmq Reviewed-by: @wmzheng2020,@zhoufeng54 Signed-off-by: @zhoufeng54
This commit is contained in:
Gitee
commit
3e0ada91ff
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@ -1,187 +1,187 @@
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# Copyright 2020 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 shutil
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import pytest
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from mindspore import dataset as ds
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from mindspore import nn, Tensor, context
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from mindspore.nn.metrics import Accuracy
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from mindspore.nn.optim import Momentum
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from mindspore.dataset.transforms import c_transforms as C
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from mindspore.dataset.vision import c_transforms as CV
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from mindspore.dataset.vision import Inter
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.train import Model
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from mindspore.profiler import Profiler
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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"""Define LeNet5 network."""
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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self.channel = Tensor(channel)
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def construct(self, data):
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"""define construct."""
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output = self.conv1(data)
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output = self.relu(output)
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output = self.max_pool2d(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.max_pool2d(output)
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output = self.flatten(output)
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output = self.fc1(output)
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output = self.relu(output)
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output = self.fc2(output)
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output = self.relu(output)
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output = self.fc3(output)
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return output
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def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
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"""create dataset for train"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift=0.0)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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def cleanup():
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data_path = os.path.join(os.getcwd(), "data")
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kernel_meta_path = os.path.join(os.getcwd(), "kernel_data")
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cache_path = os.path.join(os.getcwd(), "__pycache__")
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if os.path.exists(data_path):
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shutil.rmtree(data_path)
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if os.path.exists(kernel_meta_path):
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shutil.rmtree(kernel_meta_path)
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if os.path.exists(cache_path):
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shutil.rmtree(cache_path)
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class TestProfiler:
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device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
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mnist_path = '/home/workspace/mindspore_dataset/mnist'
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@classmethod
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def teardown_class(cls):
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""" Run after class end."""
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cleanup()
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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_gpu_profiler(self):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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profiler = Profiler(output_path='data')
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profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0]
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self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/')
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ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
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if ds_train.get_dataset_size() == 0:
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raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
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lenet = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
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model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()})
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model.train(1, ds_train, dataset_sink_mode=True)
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profiler.analyse()
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self._check_gpu_profiling_file()
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def _check_gpu_profiling_file(self):
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op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv'
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op_type_file = self.profiler_path + f'gpu_op_type_info_{self.device_id}.csv'
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activity_file = self.profiler_path + f'gpu_activity_data_{self.device_id}.csv'
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timeline_file = self.profiler_path + f'gpu_timeline_display_{self.device_id}.json'
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getnext_file = self.profiler_path + f'minddata_getnext_profiling_{self.device_id}.txt'
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pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv'
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assert os.path.exists(op_detail_file)
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assert os.path.exists(op_type_file)
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assert os.path.exists(activity_file)
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assert os.path.exists(timeline_file)
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assert os.path.exists(getnext_file)
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assert os.path.exists(pipeline_file)
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def _check_d_profiling_file(self):
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aicore_file = self.profiler_path + f'aicore_intermediate_{self.device_id}_detail.csv'
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step_trace_file = self.profiler_path + f'step_trace_raw_{self.device_id}_detail_time.csv'
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timeline_file = self.profiler_path + f'ascend_timeline_display_{self.device_id}.json'
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aicpu_file = self.profiler_path + f'aicpu_intermediate_{self.device_id}.csv'
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minddata_pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv'
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queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.device_id}.txt'
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assert os.path.exists(aicore_file)
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assert os.path.exists(step_trace_file)
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assert os.path.exists(timeline_file)
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assert os.path.exists(queue_profiling_file)
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assert os.path.exists(minddata_pipeline_file)
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assert os.path.exists(aicpu_file)
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# Copyright 2020 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 shutil
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import pytest
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from mindspore import dataset as ds
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from mindspore import nn, Tensor, context
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from mindspore.nn.metrics import Accuracy
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from mindspore.nn.optim import Momentum
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from mindspore.dataset.transforms import c_transforms as C
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from mindspore.dataset.vision import c_transforms as CV
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from mindspore.dataset.vision import Inter
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.train import Model
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from mindspore.profiler import Profiler
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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"""Define LeNet5 network."""
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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self.channel = Tensor(channel)
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def construct(self, data):
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"""define construct."""
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output = self.conv1(data)
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output = self.relu(output)
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output = self.max_pool2d(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.max_pool2d(output)
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output = self.flatten(output)
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output = self.fc1(output)
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output = self.relu(output)
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output = self.fc2(output)
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output = self.relu(output)
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output = self.fc3(output)
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return output
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def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
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"""create dataset for train"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift=0.0)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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def cleanup():
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data_path = os.path.join(os.getcwd(), "data")
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kernel_meta_path = os.path.join(os.getcwd(), "kernel_data")
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cache_path = os.path.join(os.getcwd(), "__pycache__")
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if os.path.exists(data_path):
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shutil.rmtree(data_path)
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if os.path.exists(kernel_meta_path):
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shutil.rmtree(kernel_meta_path)
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if os.path.exists(cache_path):
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shutil.rmtree(cache_path)
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class TestProfiler:
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device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
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mnist_path = '/home/workspace/mindspore_dataset/mnist'
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@classmethod
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def teardown_class(cls):
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""" Run after class end."""
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cleanup()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_gpu_profiler(self):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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profiler = Profiler(output_path='data')
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profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0]
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self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/')
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ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
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if ds_train.get_dataset_size() == 0:
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raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
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lenet = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
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model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()})
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model.train(1, ds_train, dataset_sink_mode=True)
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profiler.analyse()
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self._check_gpu_profiling_file()
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def _check_gpu_profiling_file(self):
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op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv'
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op_type_file = self.profiler_path + f'gpu_op_type_info_{self.device_id}.csv'
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activity_file = self.profiler_path + f'gpu_activity_data_{self.device_id}.csv'
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timeline_file = self.profiler_path + f'gpu_timeline_display_{self.device_id}.json'
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getnext_file = self.profiler_path + f'minddata_getnext_profiling_{self.device_id}.txt'
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pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv'
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assert os.path.exists(op_detail_file)
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assert os.path.exists(op_type_file)
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assert os.path.exists(activity_file)
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assert os.path.exists(timeline_file)
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assert os.path.exists(getnext_file)
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assert os.path.exists(pipeline_file)
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def _check_d_profiling_file(self):
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aicore_file = self.profiler_path + f'aicore_intermediate_{self.device_id}_detail.csv'
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step_trace_file = self.profiler_path + f'step_trace_raw_{self.device_id}_detail_time.csv'
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timeline_file = self.profiler_path + f'ascend_timeline_display_{self.device_id}.json'
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aicpu_file = self.profiler_path + f'aicpu_intermediate_{self.device_id}.csv'
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minddata_pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv'
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queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.device_id}.txt'
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assert os.path.exists(aicore_file)
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assert os.path.exists(step_trace_file)
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assert os.path.exists(timeline_file)
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assert os.path.exists(queue_profiling_file)
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assert os.path.exists(minddata_pipeline_file)
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assert os.path.exists(aicpu_file)
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