diff --git a/mindspore/ops/composite/multitype_ops/add_impl.py b/mindspore/ops/composite/multitype_ops/add_impl.py index a7a1f0f659e..f12ad70470d 100644 --- a/mindspore/ops/composite/multitype_ops/add_impl.py +++ b/mindspore/ops/composite/multitype_ops/add_impl.py @@ -179,6 +179,23 @@ def _tensor_add_list(x, y): return F.tensor_add(x, y) +@add.register("List", "List") +def _list_add_list(x, y): + """ + list is added to list. + + Args: + x (list): x + y (list): y. + + Returns: + list, has the same dtype as x. + """ + for i in y: + x.append(i) + return x + + @add.register("Tensor", "Tensor") def _tensor_add_tensor(x, y): """ 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) diff --git a/tests/ut/python/pipeline/parse/test_list_add_list.py b/tests/ut/python/pipeline/parse/test_list_add_list.py new file mode 100644 index 00000000000..d0e53c09177 --- /dev/null +++ b/tests/ut/python/pipeline/parse/test_list_add_list.py @@ -0,0 +1,37 @@ +# 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. +# ============================================================================ +""" test list add list """ + +import numpy as np +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import context + + +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.value1 = [Tensor([1, 2, 3]), Tensor([4, 5, 6])] + self.value2 = [Tensor([7, 8, 9]), Tensor([10, 11, 12])] + + def construct(self): + return self.value1 + self.value2 + +def test_list_add_list(): + context.set_context(mode=context.GRAPH_MODE) + net = Net() + expect_ret = (Tensor([1, 2, 3]), Tensor([4, 5, 6]), Tensor([7, 8, 9]), Tensor([10, 11, 12])) + for i in range(len(net())): + assert (np.array_equal(net()[i].asnumpy(), expect_ret[i].asnumpy()))