SUMIN/HuggingFace_transformer
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logging + update copyright

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thomwolf
2018-11-04 12:07:38 +01:00
parent 1701291ef9
commit 834b485b2e
7 changed files with 38 additions and 36 deletions
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13
convert_tf_checkpoint_to_pytorch.py
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@@ -1,4 +1,17 @@
# coding=utf-8
# Copyright 2018 The HugginFace Inc. team.
#
# 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.
"""Convert BERT checkpoint."""
from __future__ import absolute_import

4
extract_features.py
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@@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
# 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.
"""Extract pre-computed feature vectors from BERT."""
"""Extract pre-computed feature vectors from a PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division

4
modeling.py
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@@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
# 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.
"""Common utility functions related to TensorFlow."""
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division

16
optimization.py
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@@ -1,3 +1,19 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# 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.
"""PyTorch optimization for BERT model."""
import math
import torch
from torch.optim import Optimizer

2
run_classifier.py
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@@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

29
run_squad.py
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@@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -720,22 +720,6 @@ def main():
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
# parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
# parser.add_argument("--tpu_name", default=None, type=str,
# help="The Cloud TPU to use for training. This should be either the name used when creating the "
# "Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
# parser.add_argument("--tpu_zone", default=None, type=str,
# help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
# "to automatically detect the GCE project from metadata.")
# parser.add_argument("--gcp_project", default=None, type=str,
# help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
# "to automatically detect the GCE project from metadata.")
# parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
# parser.add_argument("--num_tpu_cores", default=8, type=int, help="Only used if `use_tpu` is True. "
# "Total number of TPU cores to use.")
### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
parser.add_argument("--verbose_logging", default=False, action='store_true',
help="If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
@@ -871,7 +855,6 @@ def main():
loss.backward()
optimizer.step()
global_step += 1
logger.info("Done %s steps", global_step)
if args.do_predict:
eval_examples = read_squad_examples(
@@ -892,10 +875,8 @@ def main():
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
#all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
#eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_example_index)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
@@ -906,7 +887,6 @@ def main():
model.eval()
all_results = []
logger.info("Start evaluating")
#for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
for input_ids, input_mask, segment_ids, example_index in tqdm(eval_dataloader, descr="Evaluating"):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
@@ -918,9 +898,7 @@ def main():
start_logits, end_logits = model(input_ids, segment_ids, input_mask)
unique_id = [int(eval_features[e.item()].unique_id) for e in example_index]
#start_logits = [x.item() for x in start_logits]
start_logits = [x.view(-1).detach().cpu().numpy() for x in start_logits]
#end_logits = [x.item() for x in end_logits]
end_logits = [x.view(-1).detach().cpu().numpy() for x in end_logits]
for idx, i in enumerate(unique_id):
s = [float(x) for x in start_logits[idx]]
@@ -932,11 +910,6 @@ def main():
end_logits=e
)
)
# all_results.append(
# RawResult(
# unique_id=unique_id,
# start_logits=start_logits,
# end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")

2
tokenization.py
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@@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.