add vgg19, bert, ernie example for xpu deployment

python/examples/xpu/bert/README.md

+
+## Prepare
+### convert model
+```
+python -m paddle_serving_client.convert --dirname infer_bert-base-chinese_ft_model_4000.pdparams
+```
+
+## RPC Service
+
+### Start Service
+
+```
+pytyon bert_web_service.py serving_server 7703
+```
+
+### Client Prediction
+
+```
+python bert_client.py
+```

python/examples/xpu/bert/bert_client.py

+# coding:utf-8
+# pylint: disable=doc-string-missing
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 sys
+from paddle_serving_client import Client
+from paddle_serving_client.utils import benchmark_args
+from chinese_bert_reader import ChineseBertReader
+import numpy as np
+args = benchmark_args()
+
+reader = ChineseBertReader({"max_seq_len": 128})
+fetch = ["save_infer_model/scale_0.tmp_1"]
+endpoint_list = ['127.0.0.1:7703']
+client = Client()
+client.load_client_config(args.model)
+client.connect(endpoint_list)
+
+for line in sys.stdin:
+    feed_dict = reader.process(line)
+    for key in feed_dict.keys():
+        feed_dict[key] = np.array(feed_dict[key]).reshape((128, 1))
+    #print(feed_dict)
+    result = client.predict(feed=feed_dict, fetch=fetch, batch=False)
+print(result)

python/examples/xpu/bert/bert_web_service.py

+# coding=utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+# pylint: disable=doc-string-missing
+from paddle_serving_server.web_service import WebService
+from paddle_serving_app.reader import ChineseBertReader
+import sys
+import os
+import numpy as np
+
+
+class BertService(WebService):
+    def load(self):
+        self.reader = ChineseBertReader({
+            "vocab_file": "vocab.txt",
+            "max_seq_len": 128
+        })
+
+    def preprocess(self, feed=[], fetch=[]):
+        feed_res = []
+        is_batch = False
+        for ins in feed:
+            feed_dict = self.reader.process(ins["words"].encode("utf-8"))
+            for key in feed_dict.keys():
+                feed_dict[key] = np.array(feed_dict[key]).reshape(
+                    (len(feed_dict[key]), 1))
+            feed_res.append(feed_dict)
+        return feed_res, fetch, is_batch
+
+
+bert_service = BertService(name="bert")
+bert_service.load()
+bert_service.load_model_config(sys.argv[1])
+bert_service.prepare_server(
+    workdir="workdir", port=int(sys.argv[2]), use_lite=True, use_xpu=True, ir_optim=True)
+bert_service.run_rpc_service()
+bert_service.run_web_service()

python/examples/xpu/bert/chinese_bert_reader.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+# coding=utf-8
+from paddle_serving_app.reader.bert_base_reader import BertBaseReader
+from paddle_serving_app.reader.batching import pad_batch_data
+from paddle_serving_app.reader.tokenization import FullTokenizer, convert_to_unicode
+
+
+class ChineseBertReader(BertBaseReader):
+    """
+    ChineseBertReader handles the most traditional Chinese Bert
+    preprocessing, a user can define the vocab file through initialization
+
+    Examples:
+    from paddle_serving_app import ChineseBertReader
+
+    line = ["this is China"]
+    reader = ChineseBertReader()
+    reader.process(line[0])
+
+    """
+
+    def __init__(self, args={}):
+        super(ChineseBertReader, self).__init__()
+        vocab_file = ""
+        if "vocab_file" in args:
+            vocab_file = args["vocab_file"]
+        else:
+            vocab_file = self._download_or_not()
+
+        self.tokenizer = FullTokenizer(vocab_file=vocab_file)
+        if "max_seq_len" in args:
+            self.max_seq_len = args["max_seq_len"]
+        else:
+            self.max_seq_len = 20
+        self.vocab = self.tokenizer.vocab
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+        self.feed_keys = [
+            "input_ids", "token_type_ids"
+        ]
+
+    """
+    inner function
+    """
+
+    def _download_or_not(self):
+        import os
+        import paddle_serving_app
+        module_path = os.path.dirname(paddle_serving_app.__file__)
+        full_path = "{}/tmp/chinese_bert".format(module_path)
+        os.system("mkdir -p {}".format(full_path))
+        if os.path.exists("{}/vocab.txt".format(full_path)):
+            pass
+        else:
+            url = "https://paddle-serving.bj.bcebos.com/reader/chinese_bert/vocab.txt"
+            r = os.system("wget --no-check-certificate " + url)
+            os.system("mv vocab.txt {}".format(full_path))
+            if r != 0:
+                raise SystemExit('Download failed, please check your network')
+        return "{}/vocab.txt".format(full_path)
+
+    """
+    inner function
+    """
+
+    def _pad_batch(self, token_ids, text_type_ids):
+        batch_token_ids = [token_ids]
+        batch_text_type_ids = [text_type_ids]
+
+        padded_token_ids, input_mask = pad_batch_data(
+            batch_token_ids,
+            max_seq_len=self.max_seq_len,
+            pad_idx=self.pad_id,
+            return_input_mask=True)
+        padded_text_type_ids = pad_batch_data(
+            batch_text_type_ids,
+            max_seq_len=self.max_seq_len,
+            pad_idx=self.pad_id)
+        return padded_token_ids,  padded_text_type_ids
+
+    """
+    process function deals with a raw Chinese string as a sentence
+    this funtion returns a feed_dict
+    default key of the returned feed_dict: input_ids, position_ids, segment_ids, input_mask
+    """
+
+    def process(self, line):
+        text_a = convert_to_unicode(line)
+        tokens_a = self.tokenizer.tokenize(text_a)
+        if len(tokens_a) > self.max_seq_len - 2:
+            tokens_a = tokens_a[0:(self.max_seq_len - 2)]
+        tokens = []
+        text_type_ids = []
+        tokens.append("[CLS]")
+        text_type_ids.append(0)
+        for token in tokens_a:
+            tokens.append(token)
+            text_type_ids.append(0)
+        token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
+        #position_ids = list(range(len(token_ids)))
+        p_token_ids, p_text_type_ids= \
+            self._pad_batch(token_ids, text_type_ids)
+        feed_result = {
+            self.feed_keys[0]: p_token_ids.reshape(-1).tolist(),
+            #self.feed_keys[1]: p_pos_ids.reshape(-1).tolist(),
+            self.feed_keys[1]: p_text_type_ids.reshape(-1).tolist(),
+            #self.feed_keys[3]: input_mask.reshape(-1).tolist()
+        }
+        return feed_result

python/examples/xpu/ernie/README.md

+
+## Prepare
+### convert model
+```
+python3 -m paddle_serving_client.convert --dirname erine
+```
+
+## RPC Service
+
+### Start Service
+
+```
+python3 -m paddle_serving_server.serve --model serving_server --port 7704 --use_lite --use_xpu --ir_optim
+```
+
+### Client Prediction
+
+```
+head data-c.txt | python3 ernie_client.py --model serving_client/serving_client_conf.prototxt
+```

python/examples/xpu/ernie/chinese_ernie_reader.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+# coding=utf-8
+from paddle_serving_app.reader.bert_base_reader import BertBaseReader
+from paddle_serving_app.reader.batching import pad_batch_data
+from paddle_serving_app.reader.tokenization import FullTokenizer, convert_to_unicode
+
+
+class ChineseErnieReader(BertBaseReader):
+    """
+    ChineseErnieReader handles the most traditional Chinese Bert
+    preprocessing, a user can define the vocab file through initialization
+
+    Examples:
+    from paddle_serving_app import ChineseErnieReader
+
+    line = ["this is China"]
+    reader = ChineseErnieReader()
+    reader.process(line[0])
+
+    """
+
+    def __init__(self, args={}):
+        super(ChineseErnieReader, self).__init__()
+        vocab_file = ""
+        if "vocab_file" in args:
+            vocab_file = args["vocab_file"]
+            print("vocab")
+        else:
+            vocab_file = self._download_or_not()
+
+        self.tokenizer = FullTokenizer(vocab_file=vocab_file)
+        print(self.tokenizer)
+        if "max_seq_len" in args:
+            self.max_seq_len = args["max_seq_len"]
+        else:
+            self.max_seq_len = 20
+        self.vocab = self.tokenizer.vocab
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+        self.feed_keys = [
+            "placeholder_0", "placeholder_1", "placeholder_2", "placeholder_3"
+        ]
+
+    """
+    inner function
+    """
+
+    def _download_or_not(self):
+        import os
+        import paddle_serving_app
+        module_path = os.path.dirname(paddle_serving_app.__file__)
+        full_path = "{}/tmp/chinese_bert".format(module_path)
+        os.system("mkdir -p {}".format(full_path))
+        if os.path.exists("{}/vocab.txt".format(full_path)):
+            pass
+        else:
+            url = "https://paddle-serving.bj.bcebos.com/reader/chinese_bert/vocab.txt"
+            r = os.system("wget --no-check-certificate " + url)
+            os.system("mv vocab.txt {}".format(full_path))
+            if r != 0:
+                raise SystemExit('Download failed, please check your network')
+        return "{}/vocab.txt".format(full_path)
+
+    """
+    inner function
+    """
+
+    def _pad_batch(self, token_ids, text_type_ids, position_ids):
+        batch_token_ids = [token_ids]
+        batch_text_type_ids = [text_type_ids]
+        batch_position_ids = [position_ids]
+
+        padded_token_ids, input_mask = pad_batch_data(
+            batch_token_ids,
+            max_seq_len=self.max_seq_len,
+            pad_idx=self.pad_id,
+            return_input_mask=True)
+        padded_text_type_ids = pad_batch_data(
+            batch_text_type_ids,
+            max_seq_len=self.max_seq_len,
+            pad_idx=self.pad_id)
+        padded_position_ids = pad_batch_data(
+            batch_position_ids,
+            max_seq_len=self.max_seq_len,
+            pad_idx=self.pad_id)
+        return padded_token_ids, padded_position_ids, padded_text_type_ids, input_mask
+
+    """
+    process function deals with a raw Chinese string as a sentence
+    this funtion returns a feed_dict
+    default key of the returned feed_dict: input_ids, position_ids, segment_ids, input_mask
+    """
+
+    def process(self, line):
+        text_a = convert_to_unicode(line)
+        tokens_a = self.tokenizer.tokenize(text_a)
+        if len(tokens_a) > self.max_seq_len - 2:
+            tokens_a = tokens_a[0:(self.max_seq_len - 2)]
+        tokens = []
+        text_type_ids = []
+        tokens.append("[CLS]")
+        text_type_ids.append(0)
+        for token in tokens_a:
+            tokens.append(token)
+            text_type_ids.append(0)
+        token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
+        position_ids = list(range(len(token_ids)))
+        p_token_ids, p_pos_ids, p_text_type_ids, input_mask = \
+            self._pad_batch(token_ids, text_type_ids, position_ids)
+        feed_result = {
+            self.feed_keys[0]: p_token_ids.reshape(-1).tolist(),
+            self.feed_keys[1]: p_pos_ids.reshape(-1).tolist(),
+            self.feed_keys[2]: p_text_type_ids.reshape(-1).tolist(),
+            self.feed_keys[3]: input_mask.reshape(-1).tolist()
+        }
+        return feed_result

python/examples/xpu/ernie/erine_web_service.py

+# coding=utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+# pylint: disable=doc-string-missing
+from paddle_serving_server.web_service import WebService
+from paddle_serving_app.reader import ChineseBertReader
+import sys
+import os
+import numpy as np
+
+
+class BertService(WebService):
+    def load(self):
+        self.reader = ChineseBertReader({
+            "vocab_file": "vocab.txt",
+            "max_seq_len": 128
+        })
+
+    def preprocess(self, feed=[], fetch=[]):
+        feed_res = []
+        is_batch = False
+        for ins in feed:
+            feed_dict = self.reader.process(ins["words"].encode("utf-8"))
+            for key in feed_dict.keys():
+                feed_dict[key] = np.array(feed_dict[key]).reshape(
+                    (len(feed_dict[key]), 1))
+            feed_res.append(feed_dict)
+        return feed_res, fetch, is_batch
+
+
+bert_service = BertService(name="bert")
+bert_service.load()
+bert_service.load_model_config(sys.argv[1])
+bert_service.prepare_server(
+    workdir="workdir", port=int(sys.argv[2]), use_lite=True, use_xpu=True, ir_optim=True)
+bert_service.run_rpc_service()
+bert_service.run_web_service()

python/examples/xpu/ernie/ernie_client.py

+# coding:utf-8
+# pylint: disable=doc-string-missing
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 sys
+from paddle_serving_client import Client
+from paddle_serving_client.utils import benchmark_args
+from chinese_ernie_reader import ChineseErnieReader
+import numpy as np
+args = benchmark_args()
+
+reader = ChineseErnieReader({"max_seq_len": 128})
+fetch = ["save_infer_model/scale_0"]
+endpoint_list = ['127.0.0.1:7704']
+client = Client()
+client.load_client_config(args.model)
+client.connect(endpoint_list)
+
+for line in sys.stdin:
+    feed_dict = reader.process(line)
+    for key in feed_dict.keys():
+        feed_dict[key] = np.array(feed_dict[key]).reshape((128, 1))
+   # print(feed_dict)
+    result = client.predict(feed=feed_dict, fetch=fetch, batch=False)
+    print(result)

python/examples/xpu/vgg19/README.md

+
+## Prepare
+### convert model
+```
+python -m paddle_serving_client.convert --dirname VGG19
+```
+
+## RPC Service
+
+### Start Service
+
+```
+python -m paddle_serving_server.serve --model serving_server --port 7702 --use_lite --use_xpu --ir_optim
+```
+
+### Client Prediction
+
+```
+python vgg19_client.py
+```

python/examples/xpu/vgg19/vgg19_client.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
+from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
+
+client = Client()
+client.load_client_config(
+    "serving_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:7702"])
+
+seq = Sequential([
+    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
+])
+
+image_file = "daisy.jpg"
+img = seq(image_file)
+fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0"])
+#print(fetch_map)
+print(fetch_map["save_infer_model/scale_0"].reshape(-1))