Benchmarks

examples/benchmarking/run_benchmark.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
+# Copyright 2020 The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/benchmarking/run_benchmark_tf.py

@@ -0,0 +1,29 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+# Copyright (c) 2020, NVIDIA CORPORATION.  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.
+""" Benchmarking the library on inference and training in Tensorflow"""
+
+from transformers import HfArgumentParser, TensorflowBenchmark, TensorflowBenchmarkArguments
+
+
+def main():
+    parser = HfArgumentParser(TensorflowBenchmarkArguments)
+    benchmark_args = parser.parse_args_into_dataclasses()[0]
+    benchmark = TensorflowBenchmark(args=benchmark_args)
+    benchmark.run()
+
+
+if __name__ == "__main__":
+    main()

examples/longform-qa/eli5_app.py

@@ -1,11 +1,11 @@
+import faiss
+import nlp
 import numpy as np
 import torch
+from elasticsearch import Elasticsearch
 
-import faiss
-import nlp
 import streamlit as st
 import transformers
-from elasticsearch import Elasticsearch
 from eli5_utils import (
     embed_questions_for_retrieval,
     make_qa_s2s_model,

examples/longform-qa/eli5_utils.py

@@ -4,17 +4,17 @@
 from random import choice, randint
 from time import time
 
+import faiss  # noqa: F401
+import nlp  # noqa: F401
 import numpy as np
 import pandas as pd
 import torch
 import torch.utils.checkpoint as checkpoint
+from elasticsearch import Elasticsearch  # noqa: F401
+from elasticsearch.helpers import bulk, streaming_bulk  # noqa: F401
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from tqdm import tqdm
 
-import faiss  # noqa: F401
-import nlp  # noqa: F401
-from elasticsearch import Elasticsearch  # noqa: F401
-from elasticsearch.helpers import bulk, streaming_bulk  # noqa: F401
 from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
 
 

examples/requirements.txt

@@ -8,3 +8,8 @@ tensorflow_datasets
 pytorch-lightning==0.7.6
 matplotlib
 git-python==1.0.3
+faiss
+streamlit
+elasticsearch
+pandas
+nlp

setup.cfg

@@ -5,12 +5,15 @@ include_trailing_comma = True
 known_first_party = transformers
 known_third_party =
     absl
+    elasticsearch
     fairseq
+    faiss
     fastprogress
     git
     h5py
     matplotlib
     MeCab
+    nlp
     nltk
     numpy
     packaging

src/transformers/__init__.py

@@ -77,6 +77,9 @@
     add_end_docstrings,
     add_start_docstrings,
     cached_path,
+    is_apex_available,
+    is_psutil_available,
+    is_py3nvml_available,
     is_tf_available,
     is_torch_available,
     is_torch_tpu_available,
@@ -380,7 +383,8 @@
     from .data.datasets import GlueDataset, TextDataset, LineByLineTextDataset, GlueDataTrainingArguments
 
     # Benchmarks
-    from .benchmark import PyTorchBenchmark, PyTorchBenchmarkArguments
+    from .benchmark.benchmark import PyTorchBenchmark
+    from .benchmark.benchmark_args import PyTorchBenchmarkArguments
 
 # TensorFlow
 if is_tf_available():
@@ -576,6 +580,10 @@
     # Trainer
     from .trainer_tf import TFTrainer
 
+    # Benchmarks
+    from .benchmark.benchmark_tf import TensorflowBenchmark
+    from .benchmark.benchmark_args_tf import TensorflowBenchmarkArguments
+
 
 if not is_tf_available() and not is_torch_available():
     logger.warning(

src/transformers/benchmark/__init__.py

@@ -1,10 +0,0 @@
-# flake8: noqa
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-from ..file_utils import is_torch_available
-
-
-if is_torch_available():
-    from .benchmark_args import PyTorchBenchmarkArguments
-    from .benchmark import PyTorchBenchmark