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99 lines
3.8 KiB
99 lines
3.8 KiB
# 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 json
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import grpc
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import numpy as np
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import requests
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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 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_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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# grpc visit
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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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grpc_result = np.frombuffer(result.result[0].data, dtype=np.float32).reshape(result.result[0].tensor_shape.dims)
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print("ms grpc client received: ")
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print(grpc_result)
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# ms result
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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[0])
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bert_out_size = len(bert_out)
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# compare grpc result
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for i in range(bert_out_size):
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grpc_result = np.frombuffer(result.result[i].data, dtype=np.float32).reshape(result.result[i].tensor_shape.dims)
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logger.info("i:{}, grpc_result:{}, 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(), grpc_result, 0.001, 0.001, equal_nan=True)
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# http visit
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data = {"tensor": [input_ids.tolist(), segment_ids.tolist(), input_mask.tolist()]}
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url = "http://127.0.0.1:5501"
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input_json = json.dumps(data)
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headers = {'Content-type': 'application/json'}
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response = requests.post(url, data=input_json, headers=headers)
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result = response.text
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result = result.replace('\r', '\\r').replace('\n', '\\n')
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result_json = json.loads(result, strict=False)
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http_result = np.array(result_json['tensor'])
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print("ms http client received: ")
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print(http_result[0][:200])
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# compare http result
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for i in range(bert_out_size):
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logger.info("i:{}, http_result:{}, bert_out:{}".
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format(i, np.shape(http_result[i]), bert_out[i].asnumpy().shape))
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assert np.allclose(bert_out[i].asnumpy(), http_result[i], 0.001, 0.001, equal_nan=True)
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