forked from mindspore-Ecosystem/mindspore
268 lines
10 KiB
Python
268 lines
10 KiB
Python
# 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.
|
|
# ============================================================================
|
|
import os
|
|
import sys
|
|
import tempfile
|
|
import time
|
|
import shutil
|
|
import glob
|
|
|
|
from enum import Enum
|
|
import numpy as np
|
|
import pytest
|
|
from mindspore import Tensor, set_dump, ops, context
|
|
from mindspore.ops import operations as P
|
|
from mindspore.nn import Cell
|
|
from mindspore.nn import Dense
|
|
from mindspore.nn import SoftmaxCrossEntropyWithLogits
|
|
from mindspore.nn import Momentum
|
|
from mindspore.nn import TrainOneStepCell
|
|
from mindspore.nn import WithLossCell
|
|
from dump_test_utils import generate_cell_dump_json, check_dump_structure
|
|
from tests.security_utils import security_off_wrap
|
|
|
|
|
|
class IsDump(Enum):
|
|
SET_DUMP_TRUE = 1
|
|
SET_DUMP_FALSE = 2
|
|
SET_NONE = 3
|
|
|
|
|
|
class ReluReduceMeanDenseRelu(Cell):
|
|
def __init__(self, kernel, bias, in_channel, num_class):
|
|
super().__init__()
|
|
self.relu = P.ReLU()
|
|
self.mean = P.ReduceMean(keep_dims=False)
|
|
self.dense = Dense(in_channel, num_class, kernel, bias)
|
|
|
|
def construct(self, x_):
|
|
x_ = self.relu(x_)
|
|
x_ = self.mean(x_, (2, 3))
|
|
x_ = self.dense(x_)
|
|
x_ = self.relu(x_)
|
|
return x_
|
|
|
|
|
|
def run_multi_layer_train(is_set_dump):
|
|
weight = Tensor(np.ones((1000, 2048)).astype(np.float32))
|
|
bias = Tensor(np.ones((1000,)).astype(np.float32))
|
|
net = ReluReduceMeanDenseRelu(weight, bias, 2048, 1000)
|
|
if is_set_dump is IsDump.SET_DUMP_TRUE:
|
|
set_dump(net.relu)
|
|
elif is_set_dump is IsDump.SET_DUMP_FALSE:
|
|
set_dump(net.relu, enabled=False)
|
|
set_dump(net.mean)
|
|
criterion = SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
optimizer = Momentum(learning_rate=0.1, momentum=0.1,
|
|
params=filter(lambda x: x.requires_grad, net.get_parameters()))
|
|
net_with_criterion = WithLossCell(net, criterion)
|
|
train_network = TrainOneStepCell(net_with_criterion, optimizer)
|
|
train_network.set_train()
|
|
inputs = Tensor(np.random.randn(32, 2048, 7, 7).astype(np.float32))
|
|
label = Tensor(np.zeros(shape=(32, 1000)).astype(np.float32))
|
|
train_network(inputs, label)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_cell_dump():
|
|
"""
|
|
Feature: Cell Dump
|
|
Description: Test cell dump
|
|
Expectation: Only dump cell set by set_dump when dump_mode = 2
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'cell_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'cell_dump.json')
|
|
generate_cell_dump_json(dump_path, dump_config_path, 'test_async_dump', 2)
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
run_multi_layer_train(IsDump.SET_DUMP_TRUE)
|
|
dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
|
|
for _ in range(5):
|
|
if not os.path.exists(dump_file_path):
|
|
time.sleep(2)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
|
|
# make sure 2 relu dump files are generated with correct name prefix
|
|
time.sleep(5)
|
|
assert len(os.listdir(dump_file_path)) == 3
|
|
relu_file_name = "Relu.Default_network-WithLossCell__backbone-ReluReduceMeanDenseRelu_Relu-op*.*.*.*"
|
|
relu_file1 = glob.glob(os.path.join(dump_file_path, relu_file_name))[0]
|
|
relu_file2 = glob.glob(os.path.join(dump_file_path, relu_file_name))[1]
|
|
assert relu_file1
|
|
assert relu_file2
|
|
|
|
# make sure 1 ReluGrad dump files are generated with correct name prefix
|
|
relu_grad_file_name = "ReluGrad.Gradients_Default_network-WithLossCell__backbone" \
|
|
"-ReluReduceMeanDenseRelu_gradReLU-meta_ReluGrad-op*.*.*.*"
|
|
relu_grad_file1 = glob.glob(os.path.join(dump_file_path, relu_grad_file_name))[0]
|
|
assert relu_grad_file1
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_not_cell_dump():
|
|
"""
|
|
Feature: Cell Dump
|
|
Description: Test cell dump
|
|
Expectation: Should ignore set_dump when dump_mode != 2
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'cell_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'cell_dump.json')
|
|
generate_cell_dump_json(dump_path, dump_config_path, 'test_async_dump', 0)
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
run_multi_layer_train(IsDump.SET_DUMP_TRUE)
|
|
dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
|
|
for _ in range(5):
|
|
if not os.path.exists(dump_file_path):
|
|
time.sleep(2)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
|
|
# make sure set_dump is ignored and all cell layer are dumped
|
|
assert len(os.listdir(dump_file_path)) == 11
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_cell_empty_dump():
|
|
"""
|
|
Feature: Cell Dump
|
|
Description: Test cell dump
|
|
Expectation: Should dump nothing when set_dump is not set and dump_mode = 2
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'cell_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'cell_dump.json')
|
|
generate_cell_dump_json(dump_path, dump_config_path, 'test_async_dump', 2)
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
run_multi_layer_train(IsDump.SET_NONE)
|
|
dump_file_path = os.path.join(dump_path, 'rank_0', 'Net')
|
|
time.sleep(5)
|
|
|
|
# make sure no files are dumped
|
|
assert not os.path.exists(dump_file_path)
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_cell_dump_set_enable_false():
|
|
"""
|
|
Feature: Cell Dump
|
|
Description: Test cell dump
|
|
Expectation: Should ignore set_dump when enabled=False
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'cell_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'cell_dump.json')
|
|
generate_cell_dump_json(dump_path, dump_config_path, 'test_async_dump', 2)
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
run_multi_layer_train(IsDump.SET_DUMP_FALSE)
|
|
dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
|
|
for _ in range(5):
|
|
if not os.path.exists(dump_file_path):
|
|
time.sleep(1)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
|
|
# make sure directory has dumped files with enabled=True
|
|
assert len(os.listdir(dump_file_path)) == 1
|
|
mean_file_name = "ReduceMeanD.Default_network-WithLossCell__backbone" \
|
|
"-ReluReduceMeanDenseRelu_ReduceMeanD-*.*.*.*"
|
|
mean_file = glob.glob(os.path.join(dump_file_path, mean_file_name))[0]
|
|
assert mean_file
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
class OperateSymbolNet(Cell):
|
|
def construct(self, x):
|
|
x = ops.Add()(x, 1)
|
|
x = x - 1
|
|
x = x / 1
|
|
return x
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_cell_dump_with_operate_symbol():
|
|
"""
|
|
Feature: Cell Dump
|
|
Description: Test cell dump
|
|
Expectation: Operators which is expressed by symbol will be dumped
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'cell_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'cell_dump.json')
|
|
generate_cell_dump_json(dump_path, dump_config_path, 'test_async_dump', 2)
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
|
|
net = OperateSymbolNet()
|
|
x = Tensor(np.ones((1000,)).astype(np.float32))
|
|
set_dump(net)
|
|
net(x)
|
|
|
|
dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
|
|
for _ in range(5):
|
|
if not os.path.exists(dump_file_path):
|
|
time.sleep(1)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
|
|
# make sure directory has dumped files with enabled=True
|
|
time.sleep(2)
|
|
assert len(os.listdir(dump_file_path)) == 3
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|