bert-multi-gpu

by HaoyuHu

Feel free to fine tune large BERT models with Multi-GPU and FP16 support.

155 Stars 41 Forks Last release: over 1 year ago (v1.1.0) Apache License 2.0 46 Commits 2 Releases

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bert-multi-gpu

Feel free to fine tune large BERT models with large batch size easily. Multi-GPU and FP16 are supported.

Dependencies

Features

  • CPU/GPU/TPU Support
  • Multi-GPU Support:
    tf.distribute.MirroredStrategy
    is used to achieve Multi-GPU support for this project, which mirrors vars to distribute across multiple devices and machines. The maximum batchsize for each GPU is almost the same as bert. So **global batchsize** depends on how many GPUs there are.
    • Assume: numtrainexamples = 32000
    • Situation 1 (multi-gpu): trainbatchsize = 8, numgpucores = 4, numtrainepochs = 1
      • globalbatchsize = trainbatchsize * numgpucores = 32
      • iterationsteps = numtrainexamples * numtrainepochs / trainbatch_size = 4000
    • Situation 2 (single-gpu): trainbatchsize = 32, numgpucores = 1, numtrainepochs = 4
      • globalbatchsize = trainbatchsize * numgpucores = 32
      • iterationsteps = numtrainexamples * numtrainepochs / trainbatch_size = 4000
    • Result after training is equivalent between situation 1 and 2 when synchronous update on gradients is applied.
  • FP16 Support: FP16 allows you to use a larger batch_size. And training speed will increase by 70~100% on Volta GPUs, but may be slower on Pascal GPUs(REF1, REF2).
  • SavedModel Export

Usage

Run Classifier

List some optional parameters below:

  • task_name
    : The name of task which you want to fine tune, you can define your own task by implementing
    DataProcessor
    class.
  • do_lower_case
    : Whether to lower case the input text. Should be True for uncased models and False for cased models. Default value is
    true
    .
  • do_train
    : Fine tune classifier or not. Default value is
    false
    .
  • do_eval
    : Evaluate classifier or not. Default value is
    false
    .
  • do_predict
    : Predict by classifier recovered from checkpoint or not. Default value is
    false
    .
  • save_for_serving
    : Output SavedModel for tensorflow serving. Default value is
    false
    .
  • data_dir
    : Your original input data directory.
  • vocab_file
    ,
    bert_config_file
    ,
    init_checkpoint
    : Files in BERT model directory.
  • max_seq_length
    : The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded. Default value is
    128
    .
  • train_batch_size
    : Batch size for each GPU. For example, if
    train_batch_size
    is 16, and
    num_gpu_cores
    is 4, your GLOBAL batch size is 16 * 4 = 64.
  • learning_rate
    : Learning rate for Adam optimizer initialization.
  • num_train_epochs
    : Train epoch number.
  • use_gpu
    : Use GPU or not.
  • num_gpu_cores
    : Total number of GPU cores to use, only used if
    use_gpu
    is True.
  • use_fp16
    : Use
    FP16
    or not.
  • output_dir
    : Checkpoints and SavedModel(.pb) files will be saved in this directory.
python run_custom_classifier.py \
  --task_name=QQP \
  --do_lower_case=true \
  --do_train=true \
  --do_eval=true \
  --do_predict=true \
  --save_for_serving=true \
  --data_dir=/cfs/data/glue/QQP \
  --vocab_file=/cfs/models/bert-large-uncased/vocab.txt \
  --bert_config_file=/cfs/models/bert-large-uncased/bert_config.json \
  --init_checkpoint=/cfs/models/bert-large-uncased/bert_model.ckpt \
  --max_seq_length=128 \
  --train_batch_size=32 \
  --learning_rate=2e-5 \
  --num_train_epochs=3.0 \
  --use_gpu=true \
  --num_gpu_cores=4 \
  --use_fp16=false \
  --output_dir=/cfs/outputs/bert-large-uncased-qqp

Shell script is available also (see runcustomclassifier.sh) - Optional params could be passed flexibly through command line. - CUDAVISIBLEDEVICES could be set and export as environmental variables when multi-gpus are used. ```shell

refer to the variables acronym

bash runcustomclassifier.sh -h

output

current params setting: -s maxseqlength, default val is: 128 -g numgpucores, default val is: 4 -b trainbatchsize, default val is: 32 -l learningrate, default val is: 2e-5 -e numtrainepochs, default val is: 3.0 -c CUDAVISIBLE_DEVICES, default val is: 0,1,2,3

example to pass params

bash runcustomclassifier.sh -s 512 -b 8 -l 3e-5 -e 1 -g 2 -c 2,3 ```

Run Multi-label Classification

Use case: In some situations, one example could be assigned to different groups, e.g. one movie could be tagged as romantic, commercial, boring with different aspects. As a result, multi-label classification should be applied rather than multi-class classification as labels are not exclusive (e.g. [1, 1, 0]).

One additional parameter 'num_labels' are required and other parameters keep similar to basic classifier.

python run_custom_classifier_mlabel.py \
  --num_labels=10 \
  --task_name=Mlabel \
  --do_lower_case=true \
  --do_train=true \
  --do_eval=true \
  --do_predict=true \
  --save_for_serving=true \
  --data_dir=/cfs/data/Mlabel \
  --vocab_file=/cfs/models/bert-large-uncased/vocab.txt \
  --bert_config_file=/cfs/models/bert-large-uncased/bert_config.json \
  --init_checkpoint=/cfs/models/bert-large-uncased/bert_model.ckpt \
  --max_seq_length=128 \
  --train_batch_size=32 \
  --learning_rate=2e-5 \
  --num_train_epochs=3.0 \
  --use_gpu=true \
  --num_gpu_cores=4 \
  --use_fp16=false \
  --output_dir=/cfs/outputs/bert-large-uncased-mlabel

Run Sequence Labeling

List some optional parameters below:

  • task_name
    : The name of task which you want to fine tune, you can define your own task by implementing
    DataProcessor
    class.
  • do_lower_case
    : Whether to lower case the input text. Should be True for uncased models and False for cased models. Default value is
    true
    .
  • do_train
    : Fine tune model or not. Default value is
    false
    .
  • do_eval
    : Evaluate model or not. Default value is
    false
    .
  • do_predict
    : Predict by model recovered from checkpoint or not. Default value is
    false
    .
  • save_for_serving
    : Output SavedModel for tensorflow serving. Default value is
    false
    .
  • data_dir
    : Your original input data directory.
  • vocab_file
    ,
    bert_config_file
    ,
    init_checkpoint
    : Files in BERT model directory.
  • max_seq_length
    : The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded. Default value is
    128
    .
  • train_batch_size
    : Batch size for each GPU. For example, if
    train_batch_size
    is 16, and
    num_gpu_cores
    is 4, your GLOBAL batch size is 16 * 4 = 64.
  • learning_rate
    : Learning rate for Adam optimizer initialization.
  • num_train_epochs
    : Train epoch number.
  • use_gpu
    : Use GPU or not.
  • num_gpu_cores
    : Total number of GPU cores to use, only used if
    use_gpu
    is True.
  • use_fp16
    : Use
    FP16
    or not.
  • output_dir
    : Checkpoints and SavedModel(.pb) files will be saved in this directory.
python run_seq_labeling.py \
  --task_name=PUNCT \
  --do_lower_case=true \
  --do_train=true \
  --do_eval=true \
  --do_predict=true \
  --save_for_serving=true \
  --data_dir=/cfs/data/PUNCT \
  --vocab_file=/cfs/models/bert-large-uncased/vocab.txt \
  --bert_config_file=/cfs/models/bert-large-uncased/bert_config.json \
  --init_checkpoint=/cfs/models/bert-large-uncased/bert_model.ckpt \
  --max_seq_length=128 \
  --train_batch_size=32 \
  --learning_rate=5e-5 \
  --num_train_epochs=10.0 \
  --use_gpu=true \
  --num_gpu_cores=4 \
  --use_fp16=false \
  --output_dir=/cfs/outputs/bert-large-uncased-punct

What's More

Add custom task

You can define your own task data processor by implementing

DataProcessor
class.

Then, add your

CustomProcessor
to processors.

Finally, you can pass

--task=your_task_name
to python script.
# Create custom task data processor in run_custom_classifier.py
class CustomProcessor(DataProcessor):
    """Processor for the Custom data set."""

def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(read_custom_train_lines(data_dir), 'train')

def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(read_custom_dev_lines(data_dir), 'dev')

def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(read_custom_test_lines(data_dir), 'test')

def get_labels(self):
    """See base class."""
    return your_label_list # ["label-1", "label-2", "label-3", ..., "label-k"]

def _create_examples(self, lines, set_type):
    """Creates examples for the training/evaluation/testing sets."""
    examples = []
    for (i, line) in enumerate(lines):
        # text_b can be None
        (guid, text_a, text_b, label) = parse_your_data_line(line)
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples

Add CustomProcessor to processors in run_custom_classifier.py

def main(_): # ... # Register 'custom' processor name to processors, and you can pass --task_name=custom to this script processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "xnli": XnliProcessor, "qqp": QqpProcessor, "custom": CustomProcessor, } # ...

Tensorflow serving

If

--save_for_serving=true
is passed to
run_custom_classifier.py
or
run_seq_labeling.py
, python script will export SavedModel file to
output_dir
. Now you are good to go.
  • Install the SavedModel CLI by installing a pre-built Tensorflow binary(usually already installed on your system at pathname

    bin\saved_model_cli
    ) or building TensorFlow from source code.
  • Check your SavedModel file:

  saved_model_cli show --dir / --all

For example:

saved_model_cli show --dir tf_serving/bert_base_uncased_multi_gpu_qqp/1557722227/ --all

Output:

signature_def['serving_default']:

The given SavedModel SignatureDef contains the following input(s):

inputs['input_ids'] tensor_info:

dtype: DT_INT32

shape: (-1, 128)

name: input_ids:0

inputs['input_mask'] tensor_info:

dtype: DT_INT32

shape: (-1, 128)

name: input_mask:0

inputs['label_ids'] tensor_info:

dtype: DT_INT32

shape: (-1)

name: label_ids:0

inputs['segment_ids'] tensor_info:

dtype: DT_INT32

shape: (-1, 128)

name: segment_ids:0

The given SavedModel SignatureDef contains the following output(s):

outputs['probabilities'] tensor_info:

dtype: DT_FLOAT

shape: (-1, 2)

name: loss/Softmax:0

Method name is: tensorflow/serving/predict

  • Install Bazel and compile tensorflowmodelserver.
  cd /your/path/to/tensorflow/serving
  bazel build -c opt //tensorflow_serving/model_servers:tensorflow_model_server
  • Start tensorflow serving to listen on port for HTTP/REST API or gRPC API,
    tensorflow_model_server
    will initialize the models in
    .
  # HTTP/REST API
  bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --rest_api_port= --model_name= --model_base_path=

For example:

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --rest_api_port=9000 --model_name=bert_base_uncased_qqp --model_base_path=/root/tf_serving/bert_base_uncased_multi_gpu_qqp --enable_batching=true

Output:

2019-05-14 23:26:38.135575: I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: bert_base_uncased_qqp version: 1557722227}

2019-05-14 23:26:38.158674: I tensorflow_serving/model_servers/server.cc:324] Running gRPC ModelServer at 0.0.0.0:8500 ...

2019-05-14 23:26:38.179164: I tensorflow_serving/model_servers/server.cc:344] Exporting HTTP/REST API at:localhost:9000 ...

  • Make a request to test your latest serving model.
  curl -H "Content-type: application/json" -X POST -d '{"instances": [{"input_ids": [101,2054,2064,2028,2079,2044,16914,5910,1029,102,2054,2079,1045,2079,2044,2026,16914,5910,1029,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], "input_mask": [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], "segment_ids": [0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], "label_ids":[0]}]}'  "http://localhost:9000/v1/models/bert_base_uncased_qqp:predict"

Output:

{"predictions": [[0.608512461, 0.391487628]]}

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License

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