add profiler st

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)