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fenglovebei 2021-05-22 15:11:22 +08:00 committed by wangfengwfwf
parent 90991518bc c34c36effb
commit 76b884dea9
3 changed files with 241 additions and 187 deletions
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17
mindspore/ops/composite/multitype_ops/add_impl.py
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@ -179,6 +179,23 @@ def _tensor_add_list(x, y):
return F.tensor_add(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") @add.register("Tensor", "Tensor")
def _tensor_add_tensor(x, y): def _tensor_add_tensor(x, y):
""" """

374
tests/st/profiler/test_profiler.py
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@ -1,187 +1,187 @@
# Copyright 2020 Huawei Technologies Co., Ltd # Copyright 2020 Huawei Technologies Co., Ltd
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
# You may obtain a copy of the License at # You may obtain a copy of the License at
# #
# http://www.apache.org/licenses/LICENSE-2.0 # http://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software # Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, # distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# ============================================================================ # ============================================================================
import os import os
import shutil import shutil
import pytest import pytest
from mindspore import dataset as ds from mindspore import dataset as ds
from mindspore import nn, Tensor, context from mindspore import nn, Tensor, context
from mindspore.nn.metrics import Accuracy from mindspore.nn.metrics import Accuracy
from mindspore.nn.optim import Momentum from mindspore.nn.optim import Momentum
from mindspore.dataset.transforms import c_transforms as C from mindspore.dataset.transforms import c_transforms as C
from mindspore.dataset.vision import c_transforms as CV from mindspore.dataset.vision import c_transforms as CV
from mindspore.dataset.vision import Inter from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore.common.initializer import TruncatedNormal from mindspore.common.initializer import TruncatedNormal
from mindspore.train import Model from mindspore.train import Model
from mindspore.profiler import Profiler from mindspore.profiler import Profiler
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
"""weight initial for conv layer""" """weight initial for conv layer"""
weight = weight_variable() weight = weight_variable()
return nn.Conv2d(in_channels, out_channels, return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding, kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode="valid") weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels): def fc_with_initialize(input_channels, out_channels):
"""weight initial for fc layer""" """weight initial for fc layer"""
weight = weight_variable() weight = weight_variable()
bias = weight_variable() bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias) return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable(): def weight_variable():
"""weight initial""" """weight initial"""
return TruncatedNormal(0.02) return TruncatedNormal(0.02)
class LeNet5(nn.Cell): class LeNet5(nn.Cell):
"""Define LeNet5 network.""" """Define LeNet5 network."""
def __init__(self, num_class=10, channel=1): def __init__(self, num_class=10, channel=1):
super(LeNet5, self).__init__() super(LeNet5, self).__init__()
self.num_class = num_class self.num_class = num_class
self.conv1 = conv(channel, 6, 5) self.conv1 = conv(channel, 6, 5)
self.conv2 = conv(6, 16, 5) self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120) self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84) self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, self.num_class) self.fc3 = fc_with_initialize(84, self.num_class)
self.relu = nn.ReLU() self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten() self.flatten = nn.Flatten()
self.channel = Tensor(channel) self.channel = Tensor(channel)
def construct(self, data): def construct(self, data):
"""define construct.""" """define construct."""
output = self.conv1(data) output = self.conv1(data)
output = self.relu(output) output = self.relu(output)
output = self.max_pool2d(output) output = self.max_pool2d(output)
output = self.conv2(output) output = self.conv2(output)
output = self.relu(output) output = self.relu(output)
output = self.max_pool2d(output) output = self.max_pool2d(output)
output = self.flatten(output) output = self.flatten(output)
output = self.fc1(output) output = self.fc1(output)
output = self.relu(output) output = self.relu(output)
output = self.fc2(output) output = self.fc2(output)
output = self.relu(output) output = self.relu(output)
output = self.fc3(output) output = self.fc3(output)
return output return output
def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
"""create dataset for train""" """create dataset for train"""
# define dataset # define dataset
mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100) mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size*100)
resize_height, resize_width = 32, 32 resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0 rescale = 1.0 / 255.0
rescale_nml = 1 / 0.3081 rescale_nml = 1 / 0.3081
shift_nml = -1 * 0.1307 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081
# define map operations # define map operations
resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
rescale_op = CV.Rescale(rescale, shift=0.0) rescale_op = CV.Rescale(rescale, shift=0.0)
hwc2chw_op = CV.HWC2CHW() hwc2chw_op = CV.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32) type_cast_op = C.TypeCast(mstype.int32)
# apply map operations on images # 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=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=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_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=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) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
# apply DatasetOps # apply DatasetOps
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
mnist_ds = mnist_ds.repeat(repeat_size) mnist_ds = mnist_ds.repeat(repeat_size)
return mnist_ds return mnist_ds
def cleanup(): def cleanup():
data_path = os.path.join(os.getcwd(), "data") data_path = os.path.join(os.getcwd(), "data")
kernel_meta_path = os.path.join(os.getcwd(), "kernel_data") kernel_meta_path = os.path.join(os.getcwd(), "kernel_data")
cache_path = os.path.join(os.getcwd(), "__pycache__") cache_path = os.path.join(os.getcwd(), "__pycache__")
if os.path.exists(data_path): if os.path.exists(data_path):
shutil.rmtree(data_path) shutil.rmtree(data_path)
if os.path.exists(kernel_meta_path): if os.path.exists(kernel_meta_path):
shutil.rmtree(kernel_meta_path) shutil.rmtree(kernel_meta_path)
if os.path.exists(cache_path): if os.path.exists(cache_path):
shutil.rmtree(cache_path) shutil.rmtree(cache_path)
class TestProfiler: class TestProfiler:
device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0 device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
mnist_path = '/home/workspace/mindspore_dataset/mnist' mnist_path = '/home/workspace/mindspore_dataset/mnist'
@classmethod @classmethod
def teardown_class(cls): def teardown_class(cls):
""" Run after class end.""" """ Run after class end."""
cleanup() cleanup()
@pytest.mark.level0 @pytest.mark.level1
@pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_gpu_profiler(self): def test_gpu_profiler(self):
context.set_context(mode=context.GRAPH_MODE, device_target="GPU") context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
profiler = Profiler(output_path='data') profiler = Profiler(output_path='data')
profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0] profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0]
self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/') self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/')
ds_train = create_dataset(os.path.join(self.mnist_path, "train")) ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
if ds_train.get_dataset_size() == 0: if ds_train.get_dataset_size() == 0:
raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
lenet = LeNet5() lenet = LeNet5()
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9) optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()}) model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()})
model.train(1, ds_train, dataset_sink_mode=True) model.train(1, ds_train, dataset_sink_mode=True)
profiler.analyse() profiler.analyse()
self._check_gpu_profiling_file() self._check_gpu_profiling_file()
def _check_gpu_profiling_file(self): def _check_gpu_profiling_file(self):
op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv' 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' 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' 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' 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' 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' 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_detail_file)
assert os.path.exists(op_type_file) assert os.path.exists(op_type_file)
assert os.path.exists(activity_file) assert os.path.exists(activity_file)
assert os.path.exists(timeline_file) assert os.path.exists(timeline_file)
assert os.path.exists(getnext_file) assert os.path.exists(getnext_file)
assert os.path.exists(pipeline_file) assert os.path.exists(pipeline_file)
def _check_d_profiling_file(self): def _check_d_profiling_file(self):
aicore_file = self.profiler_path + f'aicore_intermediate_{self.device_id}_detail.csv' 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' 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' 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' 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' 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' queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.device_id}.txt'
assert os.path.exists(aicore_file) assert os.path.exists(aicore_file)
assert os.path.exists(step_trace_file) assert os.path.exists(step_trace_file)
assert os.path.exists(timeline_file) assert os.path.exists(timeline_file)
assert os.path.exists(queue_profiling_file) assert os.path.exists(queue_profiling_file)
assert os.path.exists(minddata_pipeline_file) assert os.path.exists(minddata_pipeline_file)
assert os.path.exists(aicpu_file) assert os.path.exists(aicpu_file)

37
tests/ut/python/pipeline/parse/test_list_add_list.py Normal file
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@ -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()))