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Author
Last Commit
Dec. 16, 2018
Created
Nov. 12, 2018

bert-as-service

Using BERT model as a sentence encoding service, i.e. mapping a variable-length sentence to a fixed-length vector.

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HighlightsWhat is itInstallUsageFAQBenchmarkTutorial

Made by Han Xiao • 🌐 https://hanxiao.github.io

What is it

BERT is a NLP model developed by Google for pre-training language representations. It leverages an enormous amount of plain text data publicly available on the web and is trained in an unsupervised manner. Pre-training a BERT model is a fairly expensive yet one-time procedure for each language. Fortunately, Google released several pre-trained models where you can download from here.

Sentence Encoding/Embedding is a upstream task required in many NLP applications, e.g. sentiment analysis, text classification. The goal is to represent a variable length sentence into a fixed length vector, e.g. hello world to [0.1, 0.3, 0.9]. Each element of the vector should "encode" some semantics of the original sentence.

Finally, bert-as-service uses BERT as a sentence encoder and hosts it as a service via ZeroMQ, allowing you to map sentences into fixed-length representations in just two lines of code.

Highlights

  • 🔭 State-of-the-art: build on pretrained 12/24-layer BERT models released by Google AI, which is considered as a milestone in the NLP community.
  • 🐣 Easy-to-use: require only two lines of code to get sentence encodes.
  • ⚡️ Fast: 900 sentences/s on a single Tesla M40 24GB with. See benchmark.
  • 🐙 Scalable: scale nicely and smoothly on multiple GPUs and multiple clients without worrying about concurrency. See benchmark.

Install

You can install the server and client via pip either separately or even on different machines:

pip install bert-serving-server  # server
pip install bert-serving-client  # client, independent of `bert-serving-server`

Note that the server MUST be run on Python >= 3.5 and Tensorflow >= 1.10 (one-point-ten). Again, the server does not support Python 2!

☝️ The client can be run on both Python 2 and 3 for the following consideration.

Usage

1. Download a Pre-trained BERT Model

Download a model listed below, then uncompress the zip file into some folder, say /tmp/english_L-12_H-768_A-12/

List of released pretrained BERT models (click to expand...)
BERT-Base, Uncased12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Large, Uncased24-layer, 1024-hidden, 16-heads, 340M parameters
BERT-Base, Cased12-layer, 768-hidden, 12-heads , 110M parameters
BERT-Large, Cased24-layer, 1024-hidden, 16-heads, 340M parameters
BERT-Base, Multilingual Cased (New)104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Base, Multilingual Cased (Old)102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Base, ChineseChinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters

Optional: fine-tuning the model on your downstream task. Why is it optional?

2. Start the BERT service

After installing the server, you should be able to use bert-serving-start CLI as follows:

bert-serving-start -model_dir /tmp/english_L-12_H-768_A-12/ -num_worker=4 

This will start a service with four workers, meaning that it can handle up to four concurrent requests. More concurrent requests will be queued in a load balancer. Details can be found in our FAQ and the benchmark on number of clients.

Below shows what the server looks like when starting correctly:

Alternatively, one can start the BERT Service in a Docker Container (click to expand...)
docker build -t bert-as-service -f ./docker/Dockerfile .
NUM_WORKER=1
PATH_MODEL=/PATH_TO/_YOUR_MODEL/
docker run --runtime nvidia -dit -p 5555:5555 -p 5556:5556 -v $PATH_MODEL:/model -t bert-as-service $NUM_WORKER

3. Use Client to Get Sentence Encodes

Now you can encode sentences simply as follows:

from bert_serving.client import BertClient
bc = BertClient()
bc.encode(['First do it', 'then do it right', 'then do it better'])

It will return a ndarray, in which each row is the fixed representation of a sentence. You can also let it return a pure python object with type List[List[float]].

As a feature of BERT, you may get encodes of a pair of sentences by concatenating them with |||, e.g.

bc.encode(['First do it ||| then do it right'])

Below shows what the server looks like while encoding:

Use BERT Service Remotely

One may also start the service on one (GPU) machine and call it from another (CPU) machine as follows:

# on another CPU machine
from bert_serving.client import BertClient
bc = BertClient(ip='xx.xx.xx.xx')  # ip address of the GPU machine
bc.encode(['First do it', 'then do it right', 'then do it better'])

Note that you only need pip install -U bert-serving-client in this case, the server side is not required.

💡 Want to learn more? Checkout our tutorial:

Server and Client Configurations

Server-side configs

Server-side is a CLI bert-serving-start, you can specify its arguments via:

bert-serving-start -model_dir [-max_seq_len] [-num_worker] [-max_batch_size] [-port] [-port_out] [-pooling_strategy] [-pooling_layer]
Argument Type Default Description
model_dir str folder path of the pre-trained BERT model.
max_seq_len int 25 maximum length of sequence, longer sequence will be trimmed on the right side.
num_worker int 1 number of (GPU/CPU) worker runs BERT model, each works in a separate process.
max_batch_size int 256 maximum number of sequences handled by each worker, larger batch will be partitioned into small batches.
port int 5555 port for pushing data from client to server
port_out int 5556 port for publishing results from server to client
pooling_strategy str REDUCE_MEAN the pooling strategy for generating encoding vectors, valid values are NONE, REDUCE_MEAN, REDUCE_MAX, REDUCE_MEAN_MAX, CLS_TOKEN, FIRST_TOKEN, SEP_TOKEN, LAST_TOKEN. Explanation of these strategies can be found here. To get encoding for each token in the sequence, please set this to NONE.
pooling_layer int -2 the encoding layer that pooling operates on, where -1 means the last layer, -2 means the second-to-last, etc.
gpu_memory_fraction float 0.5 the fraction of the overall amount of memory that each GPU should be allocated per worker
cpu bool False run on CPU instead of GPU
xla bool False enable XLA compiler for graph optimization (experimental!)

Client-side configs

Client-side is Python class BertClient, which accepts arguments as follows:

Argument Type Default Description
ip str localhost IP address of the server
port int 5555 port for pushing data from client to server, must be consistent with the server side config
port_out int 5556 port for publishing results from server to client, must be consistent with the server side config
output_fmt str ndarray the output format of the sentence encodes, either in numpy array or python List[List[float]] (ndarray/list)
show_server_config bool False whether to show server configs when first connected
check_version bool True whether to force client and server to have the same version
identity str None a UUID that identifies the client, useful in multi-casting

A BertClient implements the following methods and properties:

Method Description
.encode() Encode a list of strings to a list of vectors
.encode_async() Asynchronous encode batches from a generator
.fetch() Fetch all encoded vectors from server and return them in a generator, use it with .encode_async() or .encode(blocking=False). Sending order is NOT preserved.
.fetch_all() Fetch all encoded vectors from server and return them in a list, use it with .encode_async() or .encode(blocking=False). Sending order is preserved.
.close() Gracefully close the connection between the client and the server
.status Get the client status in JSON format
.server_status Get the server status in JSON format

FAQ

Q: Where is the BERT code come from?

A: BERT code of this repo is forked from the original BERT repo with necessary modification, especially in extract_features.py.

Q: How large is a sentence vector?

In general, each sentence is translated to a 768-dimensional vector. Depending on the pretrained BERT you are using, pooling_strategy and pooling_layer the dimensions of the output vector could be different.

Q: How do you get the fixed representation? Did you do pooling or something?

A: Yes, pooling is required to get a fixed representation of a sentence. In the default strategy REDUCE_MEAN, I take the second-to-last hidden layer of all of the tokens in the sentence and do average pooling.

Q: Are you suggesting using BERT without fine-tuning?

A: Yes and no. On the one hand, Google pretrained BERT on Wikipedia data, thus should encode enough prior knowledge of the language into the model. Having such feature is not a bad idea. On the other hand, these prior knowledge is not specific to any particular domain. It should be totally reasonable if the performance is not ideal if you are using it on, for example, classifying legal cases. Nonetheless, you can always first fine-tune your own BERT on the downstream task and then use bert-as-service to extract the feature vectors efficiently. Keep in mind that bert-as-service is just a feature extraction service based on BERT. Nothing stops you from using a fine-tuned BERT.

Q: Can I get a concatenation of several layers instead of a single layer ?

A: Sure! Just use a list of the layer you want to concatenate when calling the server. Example:

bert_serving_start -pooling_layer -4 -3 -2 -1 -model_dir /tmp/english_L-12_H-768_A-12/
Q: What are the available pooling strategies?

A: Here is a table summarizes all pooling strategies I implemented. Choose your favorite one by specifying bert_serving_start -pooling_strategy.

Strategy Description
NONE no pooling at all, useful when you want to use word embedding instead of sentence embedding. This will results in a [max_seq_len, 768] encode matrix for a sequence.
REDUCE_MEAN take the average of the hidden state of encoding layer on the time axis
REDUCE_MAX take the maximum of the hidden state of encoding layer on the time axis
REDUCE_MEAN_MAX do REDUCE_MEAN and REDUCE_MAX separately and then concat them together on the last axis, resulting in 1536-dim sentence encodes
CLS_TOKEN or FIRST_TOKEN get the hidden state corresponding to [CLS], i.e. the first token
SEP_TOKEN or LAST_TOKEN get the hidden state corresponding to [SEP], i.e. the last token
Q: Why not use the hidden state of the first token as default strategy, i.e. the [CLS]?

A: Because a pre-trained model is not fine-tuned on any downstream tasks yet. In this case, the hidden state of [CLS] is not a good sentence representation. If later you fine-tune the model, you may use [CLS] as well.

Q: BERT has 12/24 layers, so which layer are you talking about?

A: By default this service works on the second last layer, i.e. pooling_layer=-2. You can change it by setting pooling_layer to other negative values, e.g. -1 corresponds to the last layer.

Q: Why not the last hidden layer? Why second-to-last?

A: The last layer is too closed to the target functions (i.e. masked language model and next sentence prediction) during pre-training, therefore may be biased to those targets. If you question about this argument and want to use the last hidden layer anyway, please feel free to set pooling_layer=-1.

Q: So which layer and which pooling strategy is the best?

A: It depends. Keep in mind that different BERT layers capture different information. To see that more clearly, here is a visualization on UCI-News Aggregator Dataset, where I randomly sample 20K news titles; get sentence encodes from different layers and with different pooling strategies, finally reduce it to 2D via PCA (one can of course do t-SNE as well, but that's not my point). There are only four classes of the data, illustrated in red, blue, yellow and green. To reproduce the result, please run example7.py.

Intuitively, pooling_layer=-1 is close to the training output, so it may be biased to the training targets. If you don't fine tune the model, then this could lead to a bad representation. pooling_layer=-12 is close to the word embedding, may preserve the very original word information (with no fancy self-attention etc.). On the other hand, you may achieve the very same performance by simply using a word-embedding only. That said, anything in-between [-1, -12] is then a trade-off.

Q: Could I use other pooling techniques?

A: For sure. But if you introduce new tf.variables to the graph, then you need to train those variables before using the model. You may also want to check some pooling techniques I mentioned in my blog post.

Q: Can I start multiple clients and send requests to one server simultaneously?

A: Yes! That's the purpose of this repo. In fact you can start as many clients as you want. One server can handle all of them (given enough time).

Q: How many requests can one service handle concurrently?

A: The maximum number of concurrent requests is determined by num_worker in bert_serving_start. If you a sending more than num_worker requests concurrently, the new requests will be temporally stored in a queue until a free worker becomes available.

Q: So one request means one sentence?

A: No. One request means a list of sentences sent from a client. Think the size of a request as the batch size. A request may contain 256, 512 or 1024 sentences. The optimal size of a request is often determined empirically. One large request can certainly improve the GPU utilization, yet it also increases the overhead of transmission. You may run python example/example1.py for a simple benchmark.

Q: How about the speed? Is it fast enough for production?

A: It highly depends on the max_seq_len and the size of a request. On a single Tesla M40 24GB with max_seq_len=40, you should get about 470 samples per second using a 12-layer BERT. In general, I'd suggest smaller max_seq_len (25) and larger request size (512/1024).

Q: Did you benchmark the efficiency?

A: Yes. See Benchmark.

To reproduce the results, please run python benchmark.py.

Q: What is backend based on?

A: ZeroMQ.

Q: What is the parallel processing model behind the scene?

Q: Why does the server need two ports?

One port is for pushing text data into the server, the other port is for publishing the encoded result to the client(s). In this way, we get rid of back-chatter, meaning that at every level recipients never talk back to senders. The overall message flow is strictly one-way, as depicted in the above figure. Killing back-chatter is essential to real scalability, allowing us to use BertClient in an asynchronous way.

Q: Do I need Tensorflow on the client side?

A: No. Think of BertClient as a general feature extractor, whose output can be fed to any ML models, e.g. scikit-learn, pytorch, tensorflow. The only file that client need is client.py. Copy this file to your project and import it, then you are ready to go.

Q: Can I use multilingual BERT model provided by Google?

A: Yes.

Q: Can I use my own fine-tuned BERT model?

A: Yes. In fact, this is suggested. Make sure you have the following three items in model_dir:

  • A TensorFlow checkpoint (bert_model.ckpt) containing the pre-trained weights (which is actually 3 files).
  • A vocab file (vocab.txt) to map WordPiece to word id.
  • A config file (bert_config.json) which specifies the hyperparameters of the model.
Q: Can I run it in python 2?

A: Server side no, client side yes. This is based on the consideration that python 2.x might still be a major piece in some tech stack. Migrating the whole downstream stack to python 3 for supporting bert-as-service can take quite some effort. On the other hand, setting up BertServer is just a one-time thing, which can be even run in a docker container. To ease the integration, we support python 2 on the client side so that you can directly use BertClient as a part of your python 2 project, whereas the server side should always be hosted with python 3.

Q: Do I need to do segmentation for Chinese?

No, if you are using the pretrained Chinese BERT released by Google you don't need word segmentation. As this Chinese BERT is character-based model. It won't recognize word/phrase even if you intentionally add space in-between. To see that more clearly, this is what the BERT model actually receives after tokenization:

bc.encode(['hey you', 'whats up?', '你好么?', '我 还 可以'])
tokens: [CLS] hey you [SEP]
input_ids: 101 13153 8357 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
input_mask: 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

tokens: [CLS] what ##s up ? [SEP]
input_ids: 101 9100 8118 8644 136 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
input_mask: 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

tokens: [CLS] 你 好 么 ? [SEP]
input_ids: 101 872 1962 720 8043 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
input_mask: 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

tokens: [CLS] 我 还 可 以 [SEP]
input_ids: 101 2769 6820 1377 809 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
input_mask: 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

That means the word embedding is actually the character embedding for Chinese-BERT.

Q: Why my (English) word is tokenized to ##something?

Because your word is out-of-vocabulary (OOV). The tokenizer from Google uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary.

For example:

input = "unaffable"
tokenizer_output = ["un", "##aff", "##able"]
Q: Can I use my own tokenizer?

Yes. If you already tokenize the sentence on your own, simply send use encode with List[List[Str]] as input and turn on is_tokenized, i.e. bc.encode(texts, is_tokenized=True).

Q: I encounter zmq.error.ZMQError: Operation cannot be accomplished in current state when using BertClient, what should I do?

A: This is often due to the misuse of BertClient in multi-thread/process environment. Note that you can’t reuse one BertClient among multiple threads/processes, you have to make a separate instance for each thread/process. For example, the following won't work at all:

# BAD example
bc = BertClient()

# in Proc1/Thread1 scope:
bc.encode(lst_str)

# in Proc2/Thread2 scope:
bc.encode(lst_str)

Instead, please do:

# in Proc1/Thread1 scope:
bc1 = BertClient()
bc1.encode(lst_str)

# in Proc2/Thread2 scope:
bc2 = BertClient()
bc2.encode(lst_str)
Q: After running the server, I have several garbage tmpXXXX folders. How can I change this behavior ?

A: These folders are used by ZeroMQ to store sockets. You can choose a different location by setting the environment variable ZEROMQ_SOCK_TMP_DIR : export ZEROMQ_SOCK_TMP_DIR=/tmp/

Q: The cosine similarity of two sentence vectors is unreasonably high (e.g. always > 0.8), what's wrong?

A: A decent representation for a downstream task doesn't mean that it will be meaningful in terms of cosine distance. Since cosine distance is a linear space where all dimensions are weighted equally. if you want to use cosine distance anyway, then please focus on the rank not the absolute value. Namely, do not use:

if cosine(A, B) > 0.9, then A and B are similar

Please consider the following instead:

if cosine(A, B) > cosine(A, C), then A is more similar to B than C.
Q: I'm getting bad performance, what should I do?

A: This often suggests that the pretrained BERT could not generate a descent representation of your downstream task. Thus, you can fine-tune the model on the downstream task and then use bert-as-service to serve the fine-tuned BERT. Note that, bert-as-service is just a feature extraction service based on BERT. Nothing stops you from using a fine-tuned BERT.

Q: Can I run the server side on CPU-only machine?

A: Yes, please run bert-serving-start -cpu -max_batch_size 16. Note that, CPU does not scale as good as GPU on large batches, therefore the max_batch_size on the server side needs to be smaller, e.g. 16 or 32.

Q: How can I choose num_worker?

A: Generally, the number of workers should be less than or equal to the number of GPU/CPU you have. Otherwise, multiple workers will be allocated to one GPU/CPU, which may not scale well (and may cause out-of-memory on GPU).

Benchmark

The primary goal of benchmarking is to test the scalability and the speed of this service, which is crucial for using it in a dev/prod environment. Benchmark was done on Tesla M40 24GB, experiments were repeated 10 times and the average value is reported.

To reproduce the results, please run

python benchmark.py

Common arguments across all experiments are:

Parameter Value
num_worker 1,2,4
max_seq_len 40
client_batch_size 2048
max_batch_size 256
num_client 1

Speed wrt. max_seq_len

max_seq_len is a parameter on the server side, which controls the maximum length of a sequence that a BERT model can handle. Sequences larger than max_seq_len will be truncated on the left side. Thus, if your client want to send long sequences to the model, please make sure the server can handle them correctly.

Performance-wise, longer sequences means slower speed and more chance of OOM, as the multi-head self-attention (the core unit of BERT) needs to do dot products and matrix multiplications between every two symbols in the sequence.

max_seq_len 1 GPU 2 GPU 4 GPU
20 903 1774 3254
40 473 919 1687
80 231 435 768
160 119 237 464
320 54 108 212

Speed wrt. client_batch_size

client_batch_size is the number of sequences from a client when invoking encode(). For performance reason, please consider encoding sequences in batch rather than encoding them one by one.

For example, do:

# prepare your sent in advance
bc = BertClient()
my_sentences = [s for s in my_corpus.iter()]
# doing encoding in one-shot
vec = bc.encode(my_sentences)

DON'T:

bc = BertClient()
vec = []
for s in my_corpus.iter():
    vec.append(bc.encode(s))

It's even worse if you put BertClient() inside the loop. Don't do that.

client_batch_size 1 GPU 2 GPU 4 GPU
1 75 74 72
4 206 205 201
8 274 270 267
16 332 329 330
64 365 365 365
256 382 383 383
512 432 766 762
1024 459 862 1517
2048 473 917 1681
4096 481 943 1809

Speed wrt. num_client

num_client represents the number of concurrent clients connected to the server at the same time.

num_client 1 GPU 2 GPU 4 GPU
1 473 919 1759
2 261 512 1028
4 133 267 533
8 67 136 270
16 34 68 136
32 17 34 68

As one can observe, 1 clients 1 GPU = 381 seqs/s, 2 clients 2 GPU 402 seqs/s, 4 clients 4 GPU 413 seqs/s. This shows the efficiency of our parallel pipeline and job scheduling, as the service can leverage the GPU time more exhaustively as concurrent requests increase.

Speed wrt. max_batch_size

max_batch_size is a parameter on the server side, which controls the maximum number of samples per batch per worker. If a incoming batch from client is larger than max_batch_size, the server will split it into small batches so that each of them is less or equal than max_batch_size before sending it to workers.

max_batch_size 1 GPU 2 GPU 4 GPU
32 450 887 1726
64 459 897 1759
128 473 931 1816
256 473 919 1688
512 464 866 1483

Speed wrt. pooling_layer

pooling_layer determines the encoding layer that pooling operates on. For example, in a 12-layer BERT model, -1 represents the layer closed to the output, -12 represents the layer closed to the embedding layer. As one can observe below, the depth of the pooling layer affects the speed.

pooling_layer 1 GPU 2 GPU 4 GPU
[-1] 438 844 1568
[-2] 475 916 1686
[-3] 516 995 1823
[-4] 569 1076 1986
[-5] 633 1193 2184
[-6] 711 1340 2430
[-7] 820 1528 2729
[-8] 945 1772 3104
[-9] 1128 2047 3622
[-10] 1392 2542 4241
[-11] 1523 2737 4752
[-12] 1568 2985 5303

Tutorial

The full list of examples can be found in example/. You can run each via python example/example-k.py. Note that they are only tested on Python 3.

Getting ELMo-like contextual word embedding

Start the server with pooling_strategy set to NONE.

bert-serving-start -pooling_strategy NONE -model_dir /tmp/english_L-12_H-768_A-12/

To get the word embedding corresponds to every token, you can simply use slice index as follows:

# max_seq_len = 25
# pooling_strategy = NONE

bc = BertClient()
vec = bc.encode(['hey you', 'whats up?'])

vec  # [2, 25, 768]
vec[0]  # [1, 25, 768], sentence embeddings for `hey you`
vec[0][0]  # [1, 1, 768], word embedding for `[CLS]`
vec[0][1]  # [1, 1, 768], word embedding for `hey`
vec[0][2]  # [1, 1, 768], word embedding for `you`
vec[0][3]  # [1, 1, 768], word embedding for `[SEP]`
vec[0][4]  # [1, 1, 768], word embedding for padding symbol
vec[0][25]  # error, out of index!

Note that no matter how long your original sequence is, the service will always return a [max_seq_len, 768] matrix for every sequence. When using slice index to get the word embedding, beware of the special tokens padded to the sequence, i.e. [CLS], [SEP], 0_PAD.

Using your own tokenizer

Often you want to use your own tokenizer to segment sentences instead of the default one from BERT. Simply call encode(is_tokenized=True) on the client slide as follows:

texts = ['hello world!', 'good day']

# a naive whitespace tokenizer
texts2 = [s.split() for s in texts]

vecs = bc.encode(texts2, is_tokenized=True)

This gives [2, 25, 768] tensor where the first [1, 25, 768] corresponds to the token-level encoding of "hello world!". If you look into its values, you will find that only the first four elements, i.e. [1, 0:3, 768] have values, all the others are zeros. This is due to the fact that BERT considers "hello world!" as four tokens: [CLS] hello world! [SEP], the rest are padding symbols and are masked out before output.

Note that there is no need to start a separate server for handling tokenized/untokenized sentences. The server can tell and handle both cases automatically.

Beware that the pretrained BERT Chinese from Google is character-based, i.e. its vocabulary is made of single Chinese characters. Therefore it makes no sense if you use word-level segmentation algorithm to pre-process the data and feed to such model.

Using BertClient with tf.data API

The tf.data API enables you to build complex input pipelines from simple, reusable pieces. One can also use BertClient to encode sentences on-the-fly and use the vectors in a downstream model. Here is an example:

batch_size = 256
num_parallel_calls = 4
num_clients = num_parallel_calls * 2  # should be at least greater than `num_parallel_calls`

# start a pool of clients
bc_clients = [BertClient(show_server_config=False) for _ in range(num_clients)]


def get_encodes(x):
    # x is `batch_size` of lines, each of which is a json object
    samples = [json.loads(l) for l in x]
    text = [s['raw_text'] for s in samples]  # List[List[str]]
    labels = [s['label'] for s in samples]  # List[str]
    # get a client from available clients
    bc_client = bc_clients.pop()
    features = bc_client.encode(text)
    # after use, put it back
    bc_clients.append(bc_client)
    return features, labels


ds = (tf.data.TextLineDataset(train_fp).batch(batch_size)
        .map(lambda x: tf.py_func(get_encodes, [x], [tf.float32, tf.string]),  num_parallel_calls=num_parallel_calls)
        .map(lambda x, y: {'feature': x, 'label': y})
        .make_one_shot_iterator().get_next())

The trick here is to start a pool of BertClient and reuse them one by one. In this way, we can fully harness the power of num_parallel_calls of Dataset.map() API.

The complete example can be found example4.py. There is also an example in Keras.

Training a text classifier using BERT features and tf.estimator API

Following the last example, we can easily extend it to a full classifier using tf.estimator API. One only need minor change on the input function as follows:

estimator = DNNClassifier(
    hidden_units=[512],
    feature_columns=[tf.feature_column.numeric_column('feature', shape=(768,))],
    n_classes=len(laws),
    config=run_config,
    label_vocabulary=laws_str,
    dropout=0.1)

input_fn = lambda fp: (tf.data.TextLineDataset(fp)
                       .apply(tf.contrib.data.shuffle_and_repeat(buffer_size=10000))
                       .batch(batch_size)
                       .map(lambda x: tf.py_func(get_encodes, [x], [tf.float32, tf.string]), num_parallel_calls=num_parallel_calls)
                       .map(lambda x, y: ({'feature': x}, y))
                       .prefetch(20))

train_spec = TrainSpec(input_fn=lambda: input_fn(train_fp))
eval_spec = EvalSpec(input_fn=lambda: input_fn(eval_fp), throttle_secs=0)
train_and_evaluate(estimator, train_spec, eval_spec)

The complete example can be found example5.py, in which a simple MLP is built on BERT features for predicting the relevant articles according to the fact description in the law documents. The problem is a part of the Chinese AI and Law Challenge Competition.

Saving and loading with TFRecord data

The TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use for training data. You can also pre-encode all your sequences and store their encodings to a TFRecord file, then later load it to build a tf.Dataset. For example, to write encoding into a TFRecord file:

bc = BertClient()
list_vec = bc.encode(lst_str)
list_label = [0 for _ in lst_str]  # a dummy list of all-zero labels

# write to tfrecord
with tf.python_io.TFRecordWriter('tmp.tfrecord') as writer:
    def create_float_feature(values):
        return tf.train.Feature(float_list=tf.train.FloatList(value=values))

    def create_int_feature(values):
        return tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))

    for (vec, label) in zip(list_vec, list_label):
        features = {'features': create_float_feature(vec), 'labels': create_int_feature([label])}
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())

Now we can load from it and build a tf.Dataset:

def _decode_record(record):
    """Decodes a record to a TensorFlow example."""
    return tf.parse_single_example(record, {
        'features': tf.FixedLenFeature([768], tf.float32),
        'labels': tf.FixedLenFeature([], tf.int64),
    })

ds = (tf.data.TFRecordDataset('tmp.tfrecord').repeat().shuffle(buffer_size=100).apply(
    tf.contrib.data.map_and_batch(lambda record: _decode_record(record), batch_size=64))
      .make_one_shot_iterator().get_next())

The complete example can be found example6.py.

To save word/token-level embedding to TFRecord, one needs to first flatten [max_seq_len, num_hidden] tensor into an 1D array as follows:

def create_float_feature(values):
    return tf.train.Feature(float_list=tf.train.FloatList(value=values.reshape(-1)))

And later reconstruct the shape when loading it:

name_to_features = {
    "feature": tf.FixedLenFeature([max_seq_length * num_hidden], tf.float32),
    "label_ids": tf.FixedLenFeature([], tf.int64),
}
    
def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)
    example['feature'] = tf.reshape(example['feature'], [max_seq_length, -1])
    return example

Be careful, this will generate a huge TFRecord file.

Asynchronous encoding

BertClient.encode() offers a nice synchronous way to get sentence encodes. However, sometimes we want to do it in an asynchronous manner by feeding all textual data to the server first, fetching the encoded results later. This can be easily done by:

# an endless data stream, generating data in an extremely fast speed
def text_gen():
    while True:
        yield lst_str  # yield a batch of text lines

bc = BertClient()

# get encoded vectors
for j in bc.encode_async(text_gen(), max_num_batch=10):
    print('received %d x %d' % (j.shape[0], j.shape[1]))

The complete example can be found example2.py.

Broadcasting to multiple clients

The encoded result is routed to the client according to its identity. If you have multiple clients with same identity, then they all receive the results! You can use this multicast feature to do some cool things, e.g. training multiple different models (some using scikit-learn some using tensorflow) in multiple separated processes while only call BertServer once. In the example below, bc and its two clones will all receive encoded vector.

# clone a client by reusing the identity 
def client_clone(id, idx):
    bc = BertClient(identity=id)
    for j in bc.listen():
        print('clone-client-%d: received %d x %d' % (idx, j.shape[0], j.shape[1]))

bc = BertClient()
# start two cloned clients sharing the same identity as bc
for j in range(2):
    threading.Thread(target=client_clone, args=(bc.identity, j)).start()

for _ in range(3):
    bc.encode(lst_str)

The complete example can be found in example3.py.

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