forked from mindspore-Ecosystem/mindspore
!9071 add testcase for ps
From: @chopin_syp Reviewed-by: @cristoval,@limingqi107 Signed-off-by: @limingqi107
This commit is contained in:
Gitee
commit
32aa790f43
|
|
@ -0,0 +1,56 @@
|
|||
#!/bin/bash
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
execute_path=$(pwd)
|
||||
self_path=$(dirname "${script_self}")
|
||||
export MS_COMM_TYPE=zmq
|
||||
export MS_SCHED_NUM=1
|
||||
DEVICE_TARGET=$1
|
||||
DATASET_PATH=$2
|
||||
export MS_WORKER_NUM=$3
|
||||
export MS_SERVER_NUM=$4
|
||||
export MS_SCHED_HOST=$5
|
||||
export MS_SCHED_PORT=$6
|
||||
|
||||
export MS_ROLE=MS_SCHED
|
||||
for((i=0;i<1;i++));
|
||||
do
|
||||
rm -rf ${execute_path}/sched_$i/
|
||||
mkdir ${execute_path}/sched_$i/
|
||||
cd ${execute_path}/sched_$i/ || exit
|
||||
python ${self_path}/../test_ps_embedding_heterogeneous_conv2d_adam.py --device_target=$DEVICE_TARGET --dataset_path=$DATASET_PATH &
|
||||
done
|
||||
|
||||
export MS_ROLE=MS_PSERVER
|
||||
for((i=0;i<$MS_SERVER_NUM;i++));
|
||||
do
|
||||
rm -rf ${execute_path}/server_$i/
|
||||
mkdir ${execute_path}/server_$i/
|
||||
cd ${execute_path}/server_$i/ || exit
|
||||
python ${self_path}/../test_ps_embedding_heterogeneous_conv2d_adam.py --device_target=$DEVICE_TARGET --dataset_path=$DATASET_PATH &
|
||||
done
|
||||
|
||||
export MS_ROLE=MS_WORKER
|
||||
for((i=0;i<$MS_WORKER_NUM;i++));
|
||||
do
|
||||
rm -rf ${execute_path}/worker_$i/
|
||||
mkdir ${execute_path}/worker_$i/
|
||||
cd ${execute_path}/worker_$i/ || exit
|
||||
python ${self_path}/../test_ps_embedding_heterogeneous_conv2d_adam.py --device_target=$DEVICE_TARGET --dataset_path=$DATASET_PATH &
|
||||
done
|
||||
|
||||
wait $!
|
||||
exit $?
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
# 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 pytest
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ps_embedding_heterogeneous_conv2d_adam():
|
||||
return_code = os.system(
|
||||
"bash shell_run_test.sh Ascend /home/workspace/mindspore_dataset/mnist 1 1 127.0.0.1 8085"
|
||||
)
|
||||
assert return_code == 0
|
||||
|
|
@ -0,0 +1,182 @@
|
|||
# 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 argparse
|
||||
import numpy as np
|
||||
import mindspore.context as context
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.c_transforms as C
|
||||
import mindspore.dataset.vision.c_transforms as CV
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.dataset.vision import Inter
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.nn import Cell
|
||||
from mindspore.nn import Flatten
|
||||
from mindspore.nn import Conv2d
|
||||
from mindspore.nn import BatchNorm2d
|
||||
from mindspore.nn import SoftmaxCrossEntropyWithLogits
|
||||
from mindspore.nn import Adam
|
||||
from mindspore.nn import EmbeddingLookup
|
||||
from mindspore.nn import ReLU
|
||||
import mindspore
|
||||
import mindspore.ops.operations as op
|
||||
from mindspore.common.parameter import Parameter
|
||||
from mindspore.train import Model
|
||||
from mindspore.common import set_seed
|
||||
|
||||
parser = argparse.ArgumentParser(description='test_ps_lenet')
|
||||
parser.add_argument("--device_target", type=str, default="Ascend")
|
||||
parser.add_argument("--dataset_path", type=str, default="/home/workspace/mindspore_dataset/mnist")
|
||||
args, _ = parser.parse_known_args()
|
||||
device_target = args.device_target
|
||||
dataset_path = args.dataset_path
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=device_target, enable_sparse=True)
|
||||
context.set_ps_context(enable_ps=True)
|
||||
|
||||
|
||||
class Menet(Cell):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, vocab_size, embedding_size,
|
||||
output_channels, target, sparse):
|
||||
super().__init__()
|
||||
set_seed(5)
|
||||
self.relu = ReLU()
|
||||
self.conv = Conv2d(in_channels=in_channels, out_channels=out_channels,
|
||||
kernel_size=kernel_size, has_bias=True, weight_init='normal')
|
||||
self.batchnorm = BatchNorm2d(num_features=out_channels)
|
||||
self.embedding_lookup = EmbeddingLookup(vocab_size=vocab_size,
|
||||
embedding_size=embedding_size,
|
||||
param_init='normal', target=target, sparse=sparse)
|
||||
self.flatten = Flatten()
|
||||
self.cast = op.Cast()
|
||||
self.bias = Parameter(Tensor(np.ones([output_channels]).astype(np.float32)), name='bias')
|
||||
self.biasadd = op.BiasAdd()
|
||||
self.type = mindspore.int32
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv(x)
|
||||
x = self.batchnorm(x)
|
||||
x = self.flatten(x)
|
||||
x = self.relu(x)
|
||||
x = self.cast(x, self.type)
|
||||
x = self.embedding_lookup(x)
|
||||
x = self.flatten(x)
|
||||
x = self.biasadd(x, self.bias)
|
||||
x = self.biasadd(x, self.bias)
|
||||
return x
|
||||
|
||||
|
||||
def create_dataset(data_path, batch_size=32, repeat_size=1,
|
||||
num_parallel_workers=1):
|
||||
"""
|
||||
create dataset for train or test
|
||||
"""
|
||||
# define dataset
|
||||
mnist_ds = ds.MnistDataset(data_path)
|
||||
|
||||
resize_height, resize_width = 32, 32
|
||||
rescale = 1.0 / 255.0
|
||||
shift = 0.0
|
||||
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)
|
||||
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
|
||||
buffer_size = 10000
|
||||
mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
|
||||
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
|
||||
mnist_ds = mnist_ds.repeat(repeat_size)
|
||||
|
||||
return mnist_ds
|
||||
|
||||
|
||||
class NetFactory:
|
||||
def __init__(self, dataset, input_shape=(2, 1, 32, 32), in_channels=1, out_channels=3,
|
||||
kernel_size=5, vocab_size=5, embedding_size=1, output_channels=3072,
|
||||
epoch_size=1, target='CPU', sparse=True):
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.vocab_size = vocab_size
|
||||
self.embedding_size = embedding_size
|
||||
self.output_channels = output_channels
|
||||
self.dataset = dataset
|
||||
self.epoch_size = epoch_size
|
||||
self.target = target
|
||||
self.sparse = sparse
|
||||
self.input_np = np.random.randn(*input_shape).astype(np.float32)
|
||||
|
||||
def no_ps_impl(self):
|
||||
context.set_ps_context(enable_ps=False)
|
||||
net = Menet(self.in_channels, self.out_channels, self.kernel_size, self.vocab_size,
|
||||
self.embedding_size, self.output_channels, self.target, self.sparse)
|
||||
net.conv.conv2d.add_prim_attr('primitive_target', 'CPU')
|
||||
net.conv.bias_add.add_prim_attr('primitive_target', 'CPU')
|
||||
net.set_train()
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
opt = Adam(params=filter(lambda x: x.requires_grad, net.get_parameters()))
|
||||
opt.target = 'CPU'
|
||||
model = Model(net, loss, opt)
|
||||
model.train(self.epoch_size, self.dataset, dataset_sink_mode=False)
|
||||
input_me = Tensor(self.input_np)
|
||||
out_me = model.predict(input_me)
|
||||
context.set_ps_context(enable_ps=True)
|
||||
return out_me.asnumpy()
|
||||
|
||||
def part_ps_impl(self):
|
||||
net = Menet(self.in_channels, self.out_channels, self.kernel_size, self.vocab_size,
|
||||
self.embedding_size, self.output_channels, self.target, self.sparse)
|
||||
net.embedding_lookup.set_param_ps()
|
||||
net.conv.conv2d.add_prim_attr('primitive_target', 'CPU')
|
||||
net.conv.bias_add.add_prim_attr('primitive_target', 'CPU')
|
||||
net.set_train()
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
opt = Adam(params=filter(lambda x: x.requires_grad, net.get_parameters()))
|
||||
opt.target = 'CPU'
|
||||
model = Model(net, loss, opt)
|
||||
model.train(self.epoch_size, self.dataset, dataset_sink_mode=False)
|
||||
input_me = Tensor(self.input_np)
|
||||
out_me = model.predict(input_me)
|
||||
return out_me.asnumpy()
|
||||
|
||||
def part_cmp(self):
|
||||
part_ps = self.part_ps_impl()
|
||||
no_ps = self.no_ps_impl()
|
||||
assert np.allclose(no_ps, part_ps, rtol=1.0e-5, atol=1.0e-5)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
datasets = create_dataset(os.path.join(dataset_path, "train"), 32, 1)
|
||||
fact = NetFactory(dataset=datasets)
|
||||
fact.part_cmp()
|
||||
Loading…
Reference in New Issue