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updating GLUE utils for compatibility with XLNet

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thomwolf 2019-06-24 14:36:11 +02:00
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README.md
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@ -137,9 +137,9 @@ This package comprises the following classes that can be imported in Python and
The repository further comprises:
- Five examples on how to use **BERT** (in the [`examples` folder](./examples)):
- [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`,
- [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task,
- [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 and SQuAD v2.0 tasks.
- [`run_bert_extract_features.py`](./examples/run_bert_extract_features.py) - Show how to extract hidden states from an instance of `BertModel`,
- [`run_bert_classifier.py`](./examples/run_bert_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task,
- [`run_bert_squad.py`](./examples/run_bert_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 and SQuAD v2.0 tasks.
- [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task.
- [`simple_lm_finetuning.py`](./examples/lm_finetuning/simple_lm_finetuning.py) - Show how to fine-tune an instance of `BertForPretraining` on a target text corpus.
@ -541,7 +541,7 @@ where
- `bert-base-german-cased`: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters [Performance Evaluation](https://deepset.ai/german-bert)
- `bert-large-uncased-whole-word-masking`: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
- `bert-large-cased-whole-word-masking`: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
- `bert-large-uncased-whole-word-masking-finetuned-squad`: The `bert-large-uncased-whole-word-masking` model finetuned on SQuAD (using the `run_squad.py` examples). Results: *exact_match: 86.91579943235573, f1: 93.1532499015869*
- `bert-large-uncased-whole-word-masking-finetuned-squad`: The `bert-large-uncased-whole-word-masking` model finetuned on SQuAD (using the `run_bert_squad.py` examples). Results: *exact_match: 86.91579943235573, f1: 93.1532499015869*
- `openai-gpt`: OpenAI GPT English model, 12-layer, 768-hidden, 12-heads, 110M parameters
- `gpt2`: OpenAI GPT-2 English model, 12-layer, 768-hidden, 12-heads, 117M parameters
- `gpt2-medium`: OpenAI GPT-2 English model, 24-layer, 1024-hidden, 16-heads, 345M parameters
@ -720,7 +720,7 @@ The inputs and output are **identical to the TensorFlow model inputs and outputs
We detail them here. This model takes as *inputs*:
[`modeling.py`](./pytorch_pretrained_bert/modeling.py)
- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py)), and
- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`run_bert_extract_features.py`](./examples/run_bert_extract_features.py), [`run_bert_classifier.py`](./examples/run_bert_classifier.py) and [`run_bert_squad.py`](./examples/run_bert_squad.py)), and
- `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
- `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if some input sequence lengths are smaller than the max input sequence length of the current batch. It's the mask that we typically use for attention when a batch has varying length sentences.
- `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
@ -735,7 +735,7 @@ This model *outputs* a tuple composed of:
- `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
An example on how to use this class is given in the [`extract_features.py`](./examples/extract_features.py) script which can be used to extract the hidden states of the model for a given input.
An example on how to use this class is given in the [`run_bert_extract_features.py`](./examples/run_bert_extract_features.py) script which can be used to extract the hidden states of the model for a given input.
#### 2. `BertForPreTraining`
@ -792,7 +792,7 @@ An example on how to use this class is given in the [`run_lm_finetuning.py`](./e
The sequence-level classifier is a linear layer that takes as input the last hidden state of the first character in the input sequence (see Figures 3a and 3b in the BERT paper).
An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task.
An example on how to use this class is given in the [`run_bert_classifier.py`](./examples/run_bert_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task.
#### 6. `BertForMultipleChoice`
@ -816,7 +816,7 @@ The token-level classifier is a linear layer that takes as input the last hidden
The token-level classifier takes as input the full sequence of the last hidden state and compute several (e.g. two) scores for each tokens that can for example respectively be the score that a given token is a `start_span` and a `end_span` token (see Figures 3c and 3d in the BERT paper).
An example on how to use this class is given in the [`run_squad.py`](./examples/run_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task.
An example on how to use this class is given in the [`run_bert_squad.py`](./examples/run_bert_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task.
#### 9. `OpenAIGPTModel`
@ -1138,7 +1138,7 @@ An overview of the implemented schedules:
| Sub-section | Description |
|-|-|
| [Training large models: introduction, tools and examples](#Training-large-models-introduction,-tools-and-examples) | How to use gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training to train Bert models |
| [Fine-tuning with BERT: running the examples](#Fine-tuning-with-BERT-running-the-examples) | Running the examples in [`./examples`](./examples/): `extract_classif.py`, `run_classifier.py`, `run_squad.py` and `run_lm_finetuning.py` |
| [Fine-tuning with BERT: running the examples](#Fine-tuning-with-BERT-running-the-examples) | Running the examples in [`./examples`](./examples/): `extract_classif.py`, `run_bert_classifier.py`, `run_bert_squad.py` and `run_lm_finetuning.py` |
| [Fine-tuning with OpenAI GPT, Transformer-XL and GPT-2](#openai-gpt-transformer-xl-and-gpt-2-running-the-examples) | Running the examples in [`./examples`](./examples/): `run_openai_gpt.py`, `run_transfo_xl.py` and `run_gpt2.py` |
| [Fine-tuning BERT-large on GPUs](#Fine-tuning-BERT-large-on-GPUs) | How to fine tune `BERT large`|
@ -1146,7 +1146,7 @@ An overview of the implemented schedules:
BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32).
To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month.
To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts [`run_bert_classifier.py`](./examples/run_bert_classifier.py) and [`run_bert_squad.py`](./examples/run_bert_squad.py): gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month.
Here is how to use these techniques in our scripts:
@ -1159,7 +1159,7 @@ To use 16-bits training and distributed training, you need to install NVIDIA's a
Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details):
```bash
python -m torch.distributed.launch --nproc_per_node=4 --nnodes=2 --node_rank=$THIS_MACHINE_INDEX --master_addr="192.168.1.1" --master_port=1234 run_classifier.py (--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
python -m torch.distributed.launch --nproc_per_node=4 --nnodes=2 --node_rank=$THIS_MACHINE_INDEX --master_addr="192.168.1.1" --master_port=1234 run_bert_classifier.py (--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
```
Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP address `192.168.1.1` and an open port `1234`.
@ -1201,7 +1201,7 @@ and unpack it to some directory `$GLUE_DIR`.
export GLUE_DIR=/path/to/glue
export TASK_NAME=MRPC
python run_classifier.py \
python run_bert_classifier.py \
--task_name $TASK_NAME \
--do_train \
--do_eval \
@ -1234,7 +1234,7 @@ and unpack it to some directory `$GLUE_DIR`.
```shell
export GLUE_DIR=/path/to/glue
python run_classifier.py \
python run_bert_classifier.py \
--task_name MRPC \
--do_train \
--do_eval \
@ -1256,7 +1256,7 @@ Then run
```shell
export GLUE_DIR=/path/to/glue
python run_classifier.py \
python run_bert_classifier.py \
--task_name MRPC \
--do_train \
--do_eval \
@ -1275,7 +1275,7 @@ python run_classifier.py \
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking model to reach a F1 > 92 on MRPC:
```bash
python -m torch.distributed.launch --nproc_per_node 8 run_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name MRPC --do_train --do_eval --do_lower_case --data_dir $GLUE_DIR/MRPC/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir /tmp/mrpc_output/
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name MRPC --do_train --do_eval --do_lower_case --data_dir $GLUE_DIR/MRPC/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir /tmp/mrpc_output/
```
Training with these hyper-parameters gave us the following results:
@ -1291,7 +1291,7 @@ Training with these hyper-parameters gave us the following results:
Here is an example on MNLI:
```bash
python -m torch.distributed.launch --nproc_per_node 8 run_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --do_lower_case --data_dir /datadrive/bert_data/glue_data//MNLI/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir ../models/wwm-uncased-finetuned-mnli/ --overwrite_output_dir
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --do_lower_case --data_dir /datadrive/bert_data/glue_data//MNLI/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir ../models/wwm-uncased-finetuned-mnli/ --overwrite_output_dir
```
```bash
@ -1324,7 +1324,7 @@ The data for SQuAD can be downloaded with the following links and should be save
```shell
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
python run_bert_squad.py \
--bert_model bert-base-uncased \
--do_train \
--do_predict \
@ -1351,7 +1351,7 @@ Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word
```bash
python -m torch.distributed.launch --nproc_per_node=8 \
run_squad.py \
run_bert_squad.py \
--bert_model bert-large-uncased-whole-word-masking \
--do_train \
--do_predict \
@ -1378,7 +1378,7 @@ This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-s
And here is the model provided as `bert-large-cased-whole-word-masking-finetuned-squad`:
```bash
python -m torch.distributed.launch --nproc_per_node=8 run_squad.py --bert_model bert-large-cased-whole-word-masking --do_train --do_predict --do_lower_case --train_file $SQUAD_DIR/train-v1.1.json --predict_file $SQUAD_DIR/dev-v1.1.json --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir ../models/wwm_cased_finetuned_squad/ --train_batch_size 24 --gradient_accumulation_steps 12
python -m torch.distributed.launch --nproc_per_node=8 run_bert_squad.py --bert_model bert-large-cased-whole-word-masking --do_train --do_predict --do_lower_case --train_file $SQUAD_DIR/train-v1.1.json --predict_file $SQUAD_DIR/dev-v1.1.json --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir ../models/wwm_cased_finetuned_squad/ --train_batch_size 24 --gradient_accumulation_steps 12
```
Training with these hyper-parameters gave us the following results:
@ -1499,7 +1499,7 @@ Here is the full list of hyper-parameters for this run:
```bash
export SQUAD_DIR=/path/to/SQUAD
python ./run_squad.py \
python ./run_bert_squad.py \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
@ -1521,7 +1521,7 @@ Here is an example of hyper-parameters for a FP16 run we tried:
```bash
export SQUAD_DIR=/path/to/SQUAD
python ./run_squad.py \
python ./run_bert_squad.py \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
@ -1547,7 +1547,7 @@ Here is an example with the recent `bert-large-uncased-whole-word-masking`:
```bash
python -m torch.distributed.launch --nproc_per_node=8 \
run_squad.py \
run_bert_squad.py \
--bert_model bert-large-uncased-whole-word-masking \
--do_train \
--do_predict \
@ -1563,6 +1563,86 @@ python -m torch.distributed.launch --nproc_per_node=8 \
--gradient_accumulation_steps 2
```
## Fine-tuning XLNet
#### STS-B
This example code fine-tunes XLNet on the STS-B corpus.
Before running this example you should download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.
```shell
export GLUE_DIR=/path/to/glue
python run_xlnet_classifier.py \
--task_name STS-B \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/STS-B/ \
--max_seq_length 128 \
--train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
```
Our test ran on a few seeds with [the original implementation hyper-parameters](https://github.com/zihangdai/xlnet#1-sts-b-sentence-pair-relevance-regression-with-gpus) gave evaluation results between 84% and 88%.
**Distributed training**
Here is an example using distributed training on 8 V100 GPUs to reach XXXX:
```bash
python -m torch.distributed.launch --nproc_per_node 8 \
run_xlnet_classifier.py \
--task_name STS-B \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/STS-B/ \
--max_seq_length 128 \
--train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
```
Training with these hyper-parameters gave us the following results:
```bash
acc = 0.8823529411764706
acc_and_f1 = 0.901702786377709
eval_loss = 0.3418912578906332
f1 = 0.9210526315789473
global_step = 174
loss = 0.07231863956341798
```
Here is an example on MNLI:
```bash
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --data_dir /datadrive/bert_data/glue_data//MNLI/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir ../models/wwm-uncased-finetuned-mnli/ --overwrite_output_dir
```
```bash
***** Eval results *****
acc = 0.8679706601466992
eval_loss = 0.4911287787382479
global_step = 18408
loss = 0.04755385363816904
***** Eval results *****
acc = 0.8747965825874695
eval_loss = 0.45516540421714036
global_step = 18408
loss = 0.04755385363816904
```
This is the example of the `bert-large-uncased-whole-word-masking-finetuned-mnli` model
## BERTology
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are:
@ -1599,7 +1679,7 @@ A command-line interface is provided to convert a TensorFlow checkpoint in a PyT
You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the [`convert_tf_checkpoint_to_pytorch.py`](./pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py ) script.
This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py)).
This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`run_bert_extract_features.py`](./examples/run_bert_extract_features.py), [`run_bert_classifier.py`](./examples/run_bert_classifier.py) and [`run_bert_squad.py`](./examples/run_bert_squad.py)).
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too.

8
examples/run_xlnet_classifier.py
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@ -203,7 +203,9 @@ def main():
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
train_examples, label_list, args.max_seq_length, tokenizer, output_mode,
cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s", cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
@ -347,7 +349,9 @@ def main():
eval_features = pickle.load(reader)
except:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode,
cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving eval features into cached file %s", cached_eval_features_file)
with open(cached_eval_features_file, "wb") as writer:

22
examples/utils_glue.py
View File

@ -388,8 +388,15 @@ class WnliProcessor(DataProcessor):
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_mode):
"""Loads a data file into a list of `InputBatch`s."""
tokenizer, output_mode,
cls_token_at_end=False, cls_token='[CLS]',
sep_token='[SEP]', cls_token_segment_id=0):
""" Loads a data file into a list of `InputBatch`s
`cls_token_at_end` define the location of the CLS token:
- False (BERT pattern): [CLS] + A + [SEP] + B + [SEP]
- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
"""
label_map = {label : i for i, label in enumerate(label_list)}
@ -430,13 +437,20 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
tokens = tokens_a + [sep_token]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens += tokens_b + ["[SEP]"]
tokens += tokens_b + [sep_token]
segment_ids += [1] * (len(tokens_b) + 1)
if cls_token_at_end:
tokens = tokens + [cls_token]
segment_ids = segment_ids + [cls_token_segment_id]
else:
tokens = [cls_token] + tokens
segment_ids = [cls_token_segment_id] + segment_ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real

2
notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb
View File

@ -86,7 +86,7 @@
"spec.loader.exec_module(module)\n",
"sys.modules['modeling_tensorflow'] = module\n",
"\n",
"spec = importlib.util.spec_from_file_location('*', original_tf_inplem_dir + '/run_squad.py')\n",
"spec = importlib.util.spec_from_file_location('*', original_tf_inplem_dir + '/run_bert_squad.py')\n",
"module = importlib.util.module_from_spec(spec)\n",
"spec.loader.exec_module(module)\n",
"sys.modules['run_squad_tensorflow'] = module\n",

16
pytorch_pretrained_bert/modeling.py
View File

@ -778,7 +778,7 @@ class BertModel(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -905,7 +905,7 @@ class BertForPreTraining(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -986,7 +986,7 @@ class BertForMaskedLM(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -1064,7 +1064,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -1141,7 +1141,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary. Items in the batch should begin with the special "CLS" token. (see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -1219,7 +1219,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
@ -1300,7 +1300,7 @@ class BertForTokenClassification(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
@ -1384,7 +1384,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).

105
pytorch_pretrained_bert/modeling_xlnet.py
View File

@ -46,7 +46,7 @@ XLNET_CONFIG_NAME = 'xlnet_config.json'
TF_WEIGHTS_NAME = 'model.ckpt'
def build_tf_xlnet_to_pytorch_map(model, config):
def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None):
""" A map of modules from TF to PyTorch.
I use a map to keep the PyTorch model as
identical to the original PyTorch model as possible.
@ -55,8 +55,15 @@ def build_tf_xlnet_to_pytorch_map(model, config):
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading pre-trained weights in a XLNetLMHeadModel => we will load also the output bias
if hasattr(model, 'lm_loss'):
# We will load also the output bias
tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias
elif hasattr(model, 'sequence_summary') and 'model/sequnece_summary/summary/kernel' in tf_weights:
# We will load also the sequence summary
tf_to_pt_map['model/sequnece_summary/summary/kernel'] = model.sequence_summary.summary.weight
tf_to_pt_map['model/sequnece_summary/summary/bias'] = model.sequence_summary.summary.bias
elif hasattr(model, 'proj_loss') and any('model/regression' in name for name in tf_weights.keys()):
raise NotImplementedError
# Now load the rest of the transformer
model = model.transformer
@ -116,9 +123,6 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
# Build TF to PyTorch weights loading map
tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config)
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
tf_weights = {}
@ -127,9 +131,14 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
array = tf.train.load_variable(tf_path, name)
tf_weights[name] = array
# Build TF to PyTorch weights loading map
tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)
for name, pointer in tf_to_pt_map.items():
print("Importing {}".format(name))
assert name in tf_weights
if name not in tf_weights:
print("{} not in tf pre-trained weights, skipping".format(name))
continue
array = tf_weights[name]
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
@ -643,6 +652,11 @@ class XLNetPreTrainedModel(nn.Module):
elif isinstance(module, XLNetLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, XLNetRelativeAttention):
for param in [module.q, module.k, module.v, module.o, module.r,
module.r_r_bias, module.r_s_bias, module.r_w_bias,
module.seg_embed]:
param.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
@ -904,15 +918,19 @@ class XLNetModel(XLNetPreTrainedModel):
pos_emb = pos_emb.to(next(self.parameters()))
return pos_emb
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
output_all_encoded_layers=True, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
@ -946,6 +964,7 @@ class XLNetModel(XLNetPreTrainedModel):
# so we move here the first dimension (batch) to the end
inp_k = inp_k.transpose(0, 1).contiguous()
token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
@ -969,11 +988,15 @@ class XLNetModel(XLNetPreTrainedModel):
raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
# data mask: input mask & perm mask
if attention_mask is not None and perm_mask is not None:
data_mask = attention_mask[None] + perm_mask
elif attention_mask is not None and perm_mask is None:
data_mask = attention_mask[None]
elif attention_mask is None and perm_mask is not None:
assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
"or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."
if input_mask is None and attention_mask is not None:
input_mask = 1.0 - attention_mask
if input_mask is not None and perm_mask is not None:
data_mask = input_mask[None] + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = input_mask[None]
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
@ -1077,8 +1100,12 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
Inputs:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
@ -1112,14 +1139,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
n_layer=12, num_attention_heads=12, intermediate_size=3072)
model = modeling.XLNetModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
@ -1142,15 +1169,19 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
"""
self.lm_loss.weight = self.transformer.word_embedding.weight
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
target=None, output_all_encoded_layers=True, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
@ -1171,7 +1202,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
summary_type: str, "last", "first", "mean", or "attn". The method
to pool the input to get a vector representation.
"""
output, hidden_states, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
output, hidden_states, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q,
output_all_encoded_layers, head_mask)
@ -1242,8 +1273,12 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
Inputs:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
@ -1278,14 +1313,14 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
n_layer=12, num_attention_heads=12, intermediate_size=3072)
model = modeling.XLNetModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, summary_type="last", use_proj=True, num_labels=2,
@ -1306,15 +1341,19 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
self.loss_proj = nn.Linear(config.d_model, num_labels if not is_regression else 1)
self.apply(self.init_weights)
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
target=None, output_all_encoded_layers=True, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
@ -1332,7 +1371,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
Only used during pretraining for two-stream attention.
Set to None during finetuning.
"""
output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
output, _, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q,
output_all_encoded_layers, head_mask)
@ -1372,11 +1411,15 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see XLNet paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
`attention_mask`: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
but with 1 for real tokens and 0 for padding.
Added for easy compatibility with the BERT model (which uses this negative masking).
You can only uses one among `input_mask` and `attention_mask`
`input_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
@ -1400,14 +1443,14 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = XLNetConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = XLNetForQuestionAnswering(config)
start_logits, end_logits = model(input_ids, token_type_ids, attention_mask)
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
@ -1418,11 +1461,11 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.apply(self.init_weights)
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
start_positions=None, end_positions=None,
output_all_encoded_layers=True, head_mask=None):
output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
output, _, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q,
output_all_encoded_layers, head_mask)

41
pytorch_pretrained_bert/tokenization.py
View File

@ -58,7 +58,6 @@ PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
}
VOCAB_NAME = 'vocab.txt'
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
@ -116,6 +115,46 @@ class BertTokenizer(object):
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
@property
def UNK_TOKEN(self):
return "[UNK]"
@property
def SEP_TOKEN(self):
return "[SEP]"
@property
def PAD_TOKEN(self):
return "[PAD]"
@property
def CLS_TOKEN(self):
return "[CLS]"
@property
def MASK_TOKEN(self):
return "[MASK]"
@property
def UNK_ID(self):
return self.vocab["[UNK]"]
@property
def SEP_ID(self):
return self.vocab["[SEP]"]
@property
def PAD_ID(self):
return self.vocab["[PAD]"]
@property
def CLS_ID(self):
return self.vocab["[CLS]"]
@property
def MASK_ID(self):
return self.vocab["[MASK]"]
def tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:

72
pytorch_pretrained_bert/tokenization_xlnet.py
View File

@ -38,26 +38,6 @@ SPECIAL_TOKENS_NAME = 'special_tokens.txt'
SPIECE_UNDERLINE = u'▁'
# Tokens
special_symbols = {
"<unk>" : 0,
"<s>" : 1,
"</s>" : 2,
"<cls>" : 3,
"<sep>" : 4,
"<pad>" : 5,
"<mask>" : 6,
"<eod>" : 7,
"<eop>" : 8,
}
VOCAB_SIZE = 32000
UNK_ID = special_symbols["<unk>"]
CLS_ID = special_symbols["<cls>"]
SEP_ID = special_symbols["<sep>"]
MASK_ID = special_symbols["<mask>"]
EOD_ID = special_symbols["<eod>"]
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
@ -70,6 +50,18 @@ class XLNetTokenizer(object):
SentencePiece based tokenizer. Peculiarities:
- requires SentencePiece: https://github.com/google/sentencepiece
"""
# Tokens
special_symbols = {
"<unk>" : 0,
"<s>" : 1,
"</s>" : 2,
"<cls>" : 3,
"<sep>" : 4,
"<pad>" : 5,
"<mask>" : 6,
"<eod>" : 7,
"<eop>" : 8,
}
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
@ -147,6 +139,46 @@ class XLNetTokenizer(object):
self.special_tokens_decoder = {}
self.set_special_tokens(special_tokens)
@property
def UNK_TOKEN(self):
return "<unk>"
@property
def SEP_TOKEN(self):
return "<sep>"
@property
def PAD_TOKEN(self):
return "<pad>"
@property
def CLS_TOKEN(self):
return "<cls>"
@property
def MASK_TOKEN(self):
return "<mask>"
@property
def UNK_ID(self):
return self.special_symbols["<unk>"]
@property
def SEP_ID(self):
return self.special_symbols["<sep>"]
@property
def PAD_ID(self):
return self.special_symbols["<pad>"]
@property
def CLS_ID(self):
return self.special_symbols["<cls>"]
@property
def MASK_ID(self):
return self.special_symbols["<mask>"]
def __len__(self):
return len(self.encoder) + len(self.special_tokens)

10
tests/modeling_xlnet_test.py
View File

@ -86,7 +86,7 @@ class XLNetModelTest(unittest.TestCase):
inp_q = target_mapping[:, 0, :].clone() # predict last token
# inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
# seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
# token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
# input_mask: float32 Tensor in shape [bsz, len], the input mask.
# 0 for real tokens and 1 for padding.
# mems: a list of float32 Tensors in shape [bsz, mem_len, d_model], memory
@ -138,11 +138,11 @@ class XLNetModelTest(unittest.TestCase):
model = XLNetLMHeadModel(config)
model.eval()
loss_1, mems_1a = model(input_ids_1, seg_id=segment_ids, target=lm_labels)
all_logits_1, mems_1b = model(input_ids_1, seg_id=segment_ids)
loss_1, mems_1a = model(input_ids_1, token_type_ids=segment_ids, target=lm_labels)
all_logits_1, mems_1b = model(input_ids_1, token_type_ids=segment_ids)
loss_2, mems_2a = model(input_ids_2, seg_id=segment_ids, target=lm_labels, mems=mems_1a)
all_logits_2, mems_2b = model(input_ids_2, seg_id=segment_ids, mems=mems_1b)
loss_2, mems_2a = model(input_ids_2, token_type_ids=segment_ids, target=lm_labels, mems=mems_1a)
all_logits_2, mems_2b = model(input_ids_2, token_type_ids=segment_ids, mems=mems_1b)
logits, _ = model(input_ids_q,
perm_mask=perm_mask,