hexia
5 years ago
parent
12102ae3e2
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import random
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import grpc
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import numpy as np
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import ms_service_pb2
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import ms_service_pb2_grpc
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import mindspore.dataset as de
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from mindspore import Tensor, context
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from mindspore import log as logger
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from tests.st.networks.models.bert.src.bert_model import BertModel
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from .generate_model import AddNet, bert_net_cfg
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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def test_add():
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channel = grpc.insecure_channel('localhost:5500')
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stub = ms_service_pb2_grpc.MSServiceStub(channel)
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request = ms_service_pb2.PredictRequest()
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x = request.data.add()
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x.tensor_shape.dims.extend([4])
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x.tensor_type = ms_service_pb2.MS_FLOAT32
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x.data = (np.ones([4]).astype(np.float32)).tobytes()
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y = request.data.add()
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y.tensor_shape.dims.extend([4])
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y.tensor_type = ms_service_pb2.MS_FLOAT32
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y.data = (np.ones([4]).astype(np.float32)).tobytes()
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result = stub.Predict(request)
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result_np = np.frombuffer(result.result[0].data, dtype=np.float32).reshape(result.result[0].tensor_shape.dims)
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print("ms client received: ")
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print(result_np)
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net = AddNet()
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net_out = net(Tensor(np.ones([4]).astype(np.float32)), Tensor(np.ones([4]).astype(np.float32)))
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print("add net out: ")
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print(net_out)
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assert np.allclose(net_out.asnumpy(), result_np, 0.001, 0.001, equal_nan=True)
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def test_bert():
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MAX_MESSAGE_LENGTH = 0x7fffffff
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input_ids = np.random.randint(0, 1000, size=(2, 32), dtype=np.int32)
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segment_ids = np.zeros((2, 32), dtype=np.int32)
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input_mask = np.zeros((2, 32), dtype=np.int32)
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channel = grpc.insecure_channel('localhost:5500', options=[('grpc.max_send_message_length', MAX_MESSAGE_LENGTH),
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('grpc.max_receive_message_length', MAX_MESSAGE_LENGTH)])
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stub = ms_service_pb2_grpc.MSServiceStub(channel)
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request = ms_service_pb2.PredictRequest()
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x = request.data.add()
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x.tensor_shape.dims.extend([2, 32])
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x.tensor_type = ms_service_pb2.MS_INT32
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x.data = input_ids.tobytes()
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y = request.data.add()
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y.tensor_shape.dims.extend([2, 32])
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y.tensor_type = ms_service_pb2.MS_INT32
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y.data = segment_ids.tobytes()
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z = request.data.add()
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z.tensor_shape.dims.extend([2, 32])
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z.tensor_type = ms_service_pb2.MS_INT32
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z.data = input_mask.tobytes()
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result = stub.Predict(request)
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result_np = np.frombuffer(result.result[0].data, dtype=np.float32).reshape(result.result[0].tensor_shape.dims)
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print("ms client received: ")
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print(result_np)
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net = BertModel(bert_net_cfg, False)
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bert_out = net(Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask))
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print("bert out: ")
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print(bert_out)
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bert_out_size = len(bert_out)
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for i in range(bert_out_size):
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result_np = np.frombuffer(result.result[i].data, dtype=np.float32).reshape(result.result[i].tensor_shape.dims)
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logger.info("i:{}, result_np:{}, bert_out:{}".
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format(i, result.result[i].tensor_shape.dims, bert_out[i].asnumpy().shape))
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assert np.allclose(bert_out[i].asnumpy(), result_np, 0.001, 0.001, equal_nan=True)
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import random
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import numpy as np
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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import mindspore.dataset as de
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from mindspore import Tensor, context
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from mindspore.ops import operations as P
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from mindspore.train.serialization import export
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from tests.st.networks.models.bert.src.bert_model import BertModel, BertConfig
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bert_net_cfg = BertConfig(
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batch_size=2,
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seq_length=32,
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vocab_size=21128,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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use_relative_positions=False,
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input_mask_from_dataset=True,
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token_type_ids_from_dataset=True,
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dtype=mstype.float32,
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compute_type=mstype.float16
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)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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class AddNet(nn.Cell):
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def __init__(self):
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super(AddNet, self).__init__()
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self.add = P.TensorAdd()
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def construct(self, x_, y_):
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return self.add(x_, y_)
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def export_add_model():
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net = AddNet()
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x = np.ones(4).astype(np.float32)
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y = np.ones(4).astype(np.float32)
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export(net, Tensor(x), Tensor(y), file_name='add.pb', file_format='BINARY')
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def export_bert_model():
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net = BertModel(bert_net_cfg, False)
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input_ids = np.random.randint(0, 1000, size=(2, 32), dtype=np.int32)
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segment_ids = np.zeros((2, 32), dtype=np.int32)
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input_mask = np.zeros((2, 32), dtype=np.int32)
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export(net, Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask), file_name='bert.pb', file_format='BINARY')
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if __name__ == '__main__':
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export_add_model()
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export_bert_model()
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#!/bin/bash
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export GLOG_v=1
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export DEVICE_ID=1
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MINDSPORE_INSTALL_PATH=$1
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CURRPATH=$(cd $(dirname $0); pwd)
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CURRUSER=$(whoami)
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PROJECT_PATH=${CURRPATH}/../../../
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ENV_DEVICE_ID=$DEVICE_ID
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echo "MINDSPORE_INSTALL_PATH:" ${MINDSPORE_INSTALL_PATH}
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echo "CURRPATH:" ${CURRPATH}
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echo "CURRUSER:" ${CURRUSER}
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echo "PROJECT_PATH:" ${PROJECT_PATH}
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echo "ENV_DEVICE_ID:" ${ENV_DEVICE_ID}
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MODEL_PATH=${CURRPATH}/model
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export LD_LIBRARY_PATH=${MINDSPORE_INSTALL_PATH}/lib:/usr/local/python/python375/lib/:${LD_LIBRARY_PATH}
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export PYTHONPATH=${MINDSPORE_INSTALL_PATH}/../:${PYTHONPATH}
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echo "LD_LIBRARY_PATH: " ${LD_LIBRARY_PATH}
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echo "PYTHONPATH: " ${PYTHONPATH}
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echo "-------------show MINDSPORE_INSTALL_PATH----------------"
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ls -l ${MINDSPORE_INSTALL_PATH}
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echo "------------------show /usr/lib64/----------------------"
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ls -l /usr/local/python/python375/lib/
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clean_pid()
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{
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ps aux | grep 'ms_serving' | grep ${CURRUSER} | grep -v grep | awk '{print $2}' | xargs kill -15
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if [ $? -ne 0 ]
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then
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echo "clean pip failed"
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fi
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sleep 6
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}
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prepare_model()
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{
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echo "### begin to generate mode for serving test ###"
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python3 generate_model.py &> generate_model_serving.log
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echo "### end to generate mode for serving test ###"
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result=`ls -l | grep -E '*pb' | grep -v ".log" | wc -l`
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if [ ${result} -ne 2 ]
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then
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cat generate_model_serving.log
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echo "### generate model for serving test failed ###" && exit 1
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clean_pid
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fi
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rm -rf model
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mkdir model
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mv *.pb ${CURRPATH}/model
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cp ${MINDSPORE_INSTALL_PATH}/ms_serving ./
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}
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start_service()
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{
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${CURRPATH}/ms_serving --port=$1 --model_path=${MODEL_PATH} --model_name=$2 --device_id=$3 > $2_service.log 2>&1 &
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if [ $? -ne 0 ]
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then
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echo "$2 faile to start."
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fi
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result=`grep -E 'MS Serving listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
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count=0
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while [[ ${result} -ne 1 && ${count} -lt 150 ]]
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do
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sleep 1
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count=$(($count+1))
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result=`grep -E 'MS Serving listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
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done
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if [ ${count} -eq 150 ]
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then
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clean_pid
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cat $2_service.log
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echo "start serving service failed!" && exit 1
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fi
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echo "### start serving service end ###"
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}
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pytest_serving()
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{
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unset http_proxy https_proxy
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CLIENT_DEVICE_ID=$((${ENV_DEVICE_ID}+1))
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export DEVICE_ID=${CLIENT_DEVICE_ID}
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local test_client_name=$1
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echo "### $1 client start ###"
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python3 -m pytest -v -s client_example.py::${test_client_name} > ${test_client_name}_client.log 2>&1
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if [ $? -ne 0 ]
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then
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clean_pid
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cat ${test_client_name}_client.log
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echo "client $1 faile to start."
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fi
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echo "### $1 client end ###"
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}
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test_add_model()
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{
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start_service 5500 add.pb ${ENV_DEVICE_ID}
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pytest_serving test_add
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clean_pid
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}
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test_bert_model()
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{
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start_service 5500 bert.pb ${ENV_DEVICE_ID}
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pytest_serving test_bert
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clean_pid
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}
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echo "-----serving start-----"
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rm -rf ms_serving *.log *.pb *.dat ${CURRPATH}/model ${CURRPATH}/kernel_meta
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prepare_model
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test_add_model
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test_bert_model
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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import sys
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import pytest
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import numpy as np
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_single
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def test_serving():
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"""test_serving"""
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sh_path = os.path.split(os.path.realpath(__file__))[0]
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python_path_folders = []
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for python_path in sys.path:
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if os.path.isdir(python_path):
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python_path_folders += [python_path]
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folders = []
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for folder in python_path_folders:
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folders += [os.path.join(folder, x) for x in os.listdir(folder) \
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if os.path.isdir(os.path.join(folder, x)) and '/site-packages/mindspore' in os.path.join(folder, x)]
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ret = os.system(f"sh {sh_path}/serving.sh {folders[0].split('mindspore', 1)[0] + 'mindspore'}")
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assert np.allclose(ret, 0, 0.0001, 0.0001)
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if __name__ == '__main__':
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test_serving()
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