bert-multi-gpu

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

Dependencies

• Tensorflow
  • tensorflow >= 1.11.0 # CPU Version of TensorFlow.
  • tensorflow-gpu >= 1.11.0 # GPU version of TensorFlow. (Upgrade to 1.14.0 when meets ImportError: No module named 'tensorflow.python.distribute.cross_device_ops' )
• NVIDIA Collective Communications Library (NCCL)

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 batch_size for each GPU is almost the same as bert. So global batch_size depends on how many GPUs there are.
  • Assume: num_train_examples = 32000
  • Situation 1 (multi-gpu): train_batch_size = 8, num_gpu_cores = 4, num_train_epochs = 1
    • global_batch_size = train_batch_size * num_gpu_cores = 32
    • iteration_steps = num_train_examples * num_train_epochs / train_batch_size = 4000
  • Situation 2 (single-gpu): train_batch_size = 32, num_gpu_cores = 1, num_train_epochs = 4
    • global_batch_size = train_batch_size * num_gpu_cores = 32
    • iteration_steps = num_train_examples * num_train_epochs / train_batch_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 run_custom_classifier.sh)

• Optional params could be passed flexibly through command line.
• CUDA_VISIBLE_DEVICES could be set and export as environmental variables when multi-gpus are used.

# refer to the variables acronym
bash run_custom_classifier.sh -h
# output
current params setting:
-s max_seq_length,        default val is: 128
-g num_gpu_cores,         default val is: 4
-b train_batch_size,      default val is: 32
-l learning_rate,         default val is: 2e-5
-e num_train_epochs,      default val is: 3.0
-c CUDA_VISIBLE_DEVICES,  default val is: 0,1,2,3

# example to pass params
bash run_custom_classifier.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 <bert_savedmodel_output_path>/<timestamp> --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 tensorflow_model_server.

  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 <bert_savedmodel_output_path>.

  # HTTP/REST API
  bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --rest_api_port=<rest_api_port> --model_name=<model_name> --model_base_path=<bert_savedmodel_output_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]]}

Stargazers over time

License

Apache License, please click to check more details.

                             Apache License
                       Version 2.0, January 2004
                    http://www.apache.org/licenses/

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

1. Definitions.

   "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.

   "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.

   "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.

   "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.

   "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.

   "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.

   "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).

   "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.

   "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."

   "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.

2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.

3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.

4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:

   (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and

   (b) You must cause any modified files to carry prominent notices stating that You changed the files; and

   (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and

   (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.

   You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.

5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.

6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.

7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.

8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.

9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.

END OF TERMS AND CONDITIONS

APPENDIX: How to apply the Apache License to your work.

  To apply the Apache License to your work, attach the following
  boilerplate notice, with the fields enclosed by brackets "[]"
  replaced with your own identifying information. (Don't include
  the brackets!)  The text should be enclosed in the appropriate
  comment syntax for the file format. We also recommend that a
  file or class name and description of purpose be included on the
  same "printed page" as the copyright notice for easier
  identification within third-party archives.

Copyright [yyyy] [name of copyright owner]

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.