diff --git a/tests/st/profiler/test_profiler.py b/tests/st/profiler/test_profiler.py new file mode 100644 index 00000000000..97440337360 --- /dev/null +++ b/tests/st/profiler/test_profiler.py @@ -0,0 +1,186 @@ +# 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' + profiler_path = os.path.join(os.getcwd(), 'data/profiler/') + + @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() + 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)