def train(
self,
data_dir: str,
train_filename: str,
dev_filename: Optional[str] = None,
test_filename: Optional[str] = None,
use_gpu: Optional[bool] = None,
batch_size: int = 10,
n_epochs: int = 2,
learning_rate: float = 1e-5,
max_seq_len: Optional[int] = None,
warmup_proportion: float = 0.2,
dev_split: float = 0,
evaluate_every: int = 300,
save_dir: Optional[str] = None,
num_processes: Optional[int] = None,
use_amp: str = None,
):
"""
Fine-tune a model on a QA dataset. Options:
- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
:param data_dir: Path to directory containing your training data in SQuAD style
:param train_filename: Filename of training data
:param dev_filename: Filename of dev / eval data
:param test_filename: Filename of test data
:param dev_split: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
that gets split off from training data for eval.
:param use_gpu: Whether to use GPU (if available)
:param batch_size: Number of samples the model receives in one batch for training
:param n_epochs: Number of iterations on the whole training data set
:param learning_rate: Learning rate of the optimizer
:param max_seq_len: Maximum text length (in tokens). Everything longer gets cut down.
:param warmup_proportion: Proportion of training steps until maximum learning rate is reached.
Until that point LR is increasing linearly. After that it's decreasing again linearly.
Options for different schedules are available in FARM.
:param evaluate_every: Evaluate the model every X steps on the hold-out eval dataset
:param save_dir: Path to store the final model
:param num_processes: The number of processes for `multiprocessing.Pool` during preprocessing.
Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
Set to None to use all CPU cores minus one.
:param use_amp: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
Available options:
None (Don't use AMP)
"O0" (Normal FP32 training)
"O1" (Mixed Precision => Recommended)
"O2" (Almost FP16)
"O3" (Pure FP16).
See details on: https://nvidia.github.io/apex/amp.html
:return: None
"""
if dev_filename:
dev_split = 0
if num_processes is None:
num_processes = multiprocessing.cpu_count() - 1 or 1
set_all_seeds(seed=42)
# For these variables, by default, we use the value set when initializing the FARMReader.
# These can also be manually set when train() is called if you want a different value at train vs inference
if use_gpu is None:
use_gpu = self.use_gpu
if max_seq_len is None:
max_seq_len = self.max_seq_len
device, n_gpu = initialize_device_settings(use_cuda=use_gpu,
use_amp=use_amp)
if not save_dir:
save_dir = f"../../saved_models/{self.inferencer.model.language_model.name}"
# 1. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
label_list = ["start_token", "end_token"]
metric = "squad"
processor = SquadProcessor(
tokenizer=self.inferencer.processor.tokenizer,
max_seq_len=max_seq_len,
label_list=label_list,
metric=metric,
train_filename=train_filename,
dev_filename=dev_filename,
dev_split=dev_split,
test_filename=test_filename,
data_dir=Path(data_dir),
)
# 2. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them
# and calculates a few descriptive statistics of our datasets
data_silo = DataSilo(processor=processor,
batch_size=batch_size,
distributed=False,
max_processes=num_processes)
# Quick-fix until this is fixed upstream in FARM:
# We must avoid applying DataParallel twice (once when loading the inferencer,
# once when calling initalize_optimizer)
self.inferencer.model.save("tmp_model")
model = BaseAdaptiveModel.load(load_dir="tmp_model",
device=device,
strict=True)
shutil.rmtree('tmp_model')
# 3. Create an optimizer and pass the already initialized model
model, optimizer, lr_schedule = initialize_optimizer(
model=model,
# model=self.inferencer.model,
learning_rate=learning_rate,
schedule_opts={
"name": "LinearWarmup",
"warmup_proportion": warmup_proportion
},
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs,
device=device,
use_amp=use_amp,
)
# 4. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
trainer = Trainer(
model=model,
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
lr_schedule=lr_schedule,
evaluate_every=evaluate_every,
device=device,
use_amp=use_amp,
)
# 5. Let it grow!
self.inferencer.model = trainer.train()
self.save(Path(save_dir))
def load(cls,
model_name_or_path,
batch_size=4,
gpu=False,
task_type=None,
return_class_probs=False,
strict=True,
max_seq_len=256,
doc_stride=128,
extraction_layer=None,
extraction_strategy=None,
s3e_stats=None,
num_processes=None,
disable_tqdm=False):
"""
Load an Inferencer incl. all relevant components (model, tokenizer, processor ...) either by
1. specifying a public name from transformers' model hub (https://huggingface.co/models)
2. or pointing to a local directory it is saved in.
:param model_name_or_path: Local directory or public name of the model to load.
:type model_name_or_path: str
:param batch_size: Number of samples computed once per batch
:type batch_size: int
:param gpu: If GPU shall be used
:type gpu: bool
:param task_type: Type of task the model should be used for. Currently supporting:
"embeddings", "question_answering", "text_classification", "ner". More coming soon...
:param task_type: str
:param strict: whether to strictly enforce that the keys loaded from saved model match the ones in
the PredictionHead (see torch.nn.module.load_state_dict()).
Set to `False` for backwards compatibility with PHs saved with older version of FARM.
:type strict: bool
:param max_seq_len: maximum length of one text sample
:type max_seq_len: int
:param doc_stride: Only QA: When input text is longer than max_seq_len it gets split into parts, strided by doc_stride
:type doc_stride: int
:param extraction_strategy: Strategy to extract vectors. Choices: 'cls_token' (sentence vector), 'reduce_mean'
(sentence vector), reduce_max (sentence vector), 'per_token' (individual token vectors)
:type extraction_strategy: str
:param extraction_layer: number of layer from which the embeddings shall be extracted. Default: -1 (very last layer).
:type extraction_layer: int
:param s3e_stats: Stats of a fitted S3E model as returned by `fit_s3e_on_corpus()`
(only needed for task_type="embeddings" and extraction_strategy = "s3e")
:type s3e_stats: dict
:param num_processes: the number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
multiprocessing. Set to None to let Inferencer use all CPU cores. If you want to
debug the Language Model, you might need to disable multiprocessing!
:type num_processes: int
:param disable_tqdm: Whether to disable tqdm logging (can get very verbose in multiprocessing)
:type disable_tqdm: bool
:return: An instance of the Inferencer.
"""
device, n_gpu = initialize_device_settings(use_cuda=gpu,
local_rank=-1,
use_amp=None)
name = os.path.basename(model_name_or_path)
# a) either from local dir
if os.path.exists(model_name_or_path):
model = BaseAdaptiveModel.load(load_dir=model_name_or_path,
device=device,
strict=strict)
if task_type == "embeddings":
processor = InferenceProcessor.load_from_dir(
model_name_or_path)
else:
processor = Processor.load_from_dir(model_name_or_path)
# override processor attributes loaded from config file with inferencer params
processor.max_seq_len = max_seq_len
if hasattr(processor, "doc_stride"):
processor.doc_stride = doc_stride
# b) or from remote transformers model hub
else:
logger.info(
f"Could not find `{model_name_or_path}` locally. Try to download from model hub ..."
)
if not task_type:
raise ValueError(
"Please specify the 'task_type' of the model you want to load from transformers. "
"Valid options for arg `task_type`:"
"'question_answering', 'embeddings', 'text_classification', 'ner'"
)
model = AdaptiveModel.convert_from_transformers(
model_name_or_path, device, task_type)
config = AutoConfig.from_pretrained(model_name_or_path)
tokenizer = Tokenizer.load(model_name_or_path)
# TODO infer task_type automatically from config (if possible)
if task_type == "question_answering":
processor = SquadProcessor(
tokenizer=tokenizer,
max_seq_len=max_seq_len,
label_list=["start_token", "end_token"],
metric="squad",
data_dir="data",
doc_stride=doc_stride)
elif task_type == "embeddings":
processor = InferenceProcessor(tokenizer=tokenizer,
max_seq_len=max_seq_len)
elif task_type == "text_classification":
label_list = list(config.id2label[id]
for id in range(len(config.id2label)))
processor = TextClassificationProcessor(
tokenizer=tokenizer,
max_seq_len=max_seq_len,
data_dir="data",
label_list=label_list,
label_column_name="label",
metric="acc",
quote_char='"',
)
elif task_type == "ner":
label_list = list(config.label2id.keys())
processor = NERProcessor(tokenizer=tokenizer,
max_seq_len=max_seq_len,
data_dir="data",
metric="seq_f1",
label_list=label_list)
else:
raise ValueError(
f"`task_type` {task_type} is not supported yet. "
f"Valid options for arg `task_type`: 'question_answering', "
f"'embeddings', 'text_classification', 'ner'")
return cls(model,
processor,
task_type=task_type,
batch_size=batch_size,
gpu=gpu,
name=name,
return_class_probs=return_class_probs,
extraction_strategy=extraction_strategy,
extraction_layer=extraction_layer,
s3e_stats=s3e_stats,
num_processes=num_processes,
disable_tqdm=disable_tqdm)
def load(
cls,
model_name_or_path,
revision=None,
batch_size=4,
gpu=False,
task_type=None,
return_class_probs=False,
strict=True,
max_seq_len=256,
doc_stride=128,
extraction_layer=None,
extraction_strategy=None,
s3e_stats=None,
num_processes=None,
disable_tqdm=False,
tokenizer_class=None,
use_fast=True,
tokenizer_args=None,
multithreading_rust=True,
dummy_ph=False,
benchmarking=False,
):
"""
Load an Inferencer incl. all relevant components (model, tokenizer, processor ...) either by
1. specifying a public name from transformers' model hub (https://huggingface.co/models)
2. or pointing to a local directory it is saved in.
:param model_name_or_path: Local directory or public name of the model to load.
:type model_name_or_path: str
:param revision: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
:type revision: str
:param batch_size: Number of samples computed once per batch
:type batch_size: int
:param gpu: If GPU shall be used
:type gpu: bool
:param task_type: Type of task the model should be used for. Currently supporting:
"embeddings", "question_answering", "text_classification", "ner". More coming soon...
:param task_type: str
:param strict: whether to strictly enforce that the keys loaded from saved model match the ones in
the PredictionHead (see torch.nn.module.load_state_dict()).
Set to `False` for backwards compatibility with PHs saved with older version of FARM.
:type strict: bool
:param max_seq_len: maximum length of one text sample
:type max_seq_len: int
:param doc_stride: Only QA: When input text is longer than max_seq_len it gets split into parts, strided by doc_stride
:type doc_stride: int
:param extraction_strategy: Strategy to extract vectors. Choices: 'cls_token' (sentence vector), 'reduce_mean'
(sentence vector), reduce_max (sentence vector), 'per_token' (individual token vectors)
:type extraction_strategy: str
:param extraction_layer: number of layer from which the embeddings shall be extracted. Default: -1 (very last layer).
:type extraction_layer: int
:param s3e_stats: Stats of a fitted S3E model as returned by `fit_s3e_on_corpus()`
(only needed for task_type="embeddings" and extraction_strategy = "s3e")
:type s3e_stats: dict
:param num_processes: the number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
multiprocessing. Set to None to let Inferencer use all CPU cores minus one. If you want to
debug the Language Model, you might need to disable multiprocessing!
**Warning!** If you use multiprocessing you have to close the
`multiprocessing.Pool` again! To do so call
:func:`~farm.infer.Inferencer.close_multiprocessing_pool` after you are
done using this class. The garbage collector will not do this for you!
:type num_processes: int
:param disable_tqdm: Whether to disable tqdm logging (can get very verbose in multiprocessing)
:type disable_tqdm: bool
:param tokenizer_class: (Optional) Name of the tokenizer class to load (e.g. `BertTokenizer`)
:type tokenizer_class: str
:param use_fast: (Optional, True by default) Indicate if FARM should try to load the fast version of the tokenizer (True) or
use the Python one (False).
:type use_fast: bool
:param tokenizer_args: (Optional) Will be passed to the Tokenizer ``__init__`` method.
See https://huggingface.co/transformers/main_classes/tokenizer.html and detailed tokenizer documentation
on `Hugging Face Transformers <https://huggingface.co/transformers/>`_.
:type tokenizer_args: dict
:param multithreading_rust: Whether to allow multithreading in Rust, e.g. for FastTokenizers.
Note: Enabling multithreading in Rust AND multiprocessing in python might cause
deadlocks.
:type multithreading_rust: bool
:param dummy_ph: If True, methods of the prediction head will be replaced
with a dummy method. This is used to isolate lm run time from ph run time.
:type dummy_ph: bool
:param benchmarking: If True, a benchmarking object will be initialised within the class and
certain parts of the code will be timed for benchmarking. Should be kept
False if not benchmarking since these timing checkpoints require synchronization
of the asynchronous Pytorch operations and may slow down the model.
:type benchmarking: bool
:return: An instance of the Inferencer.
"""
if tokenizer_args is None:
tokenizer_args = {}
device, n_gpu = initialize_device_settings(use_cuda=gpu,
local_rank=-1,
use_amp=None)
name = os.path.basename(model_name_or_path)
# a) either from local dir
if os.path.exists(model_name_or_path):
model = BaseAdaptiveModel.load(load_dir=model_name_or_path,
device=device,
strict=strict)
if task_type == "embeddings":
processor = InferenceProcessor.load_from_dir(
model_name_or_path)
else:
processor = Processor.load_from_dir(model_name_or_path)
# b) or from remote transformers model hub
else:
if not task_type:
raise ValueError(
"Please specify the 'task_type' of the model you want to load from transformers. "
"Valid options for arg `task_type`:"
"'question_answering', 'embeddings', 'text_classification', 'ner'"
)
model = AdaptiveModel.convert_from_transformers(
model_name_or_path,
revision=revision,
device=device,
task_type=task_type)
processor = Processor.convert_from_transformers(
model_name_or_path,
revision=revision,
task_type=task_type,
max_seq_len=max_seq_len,
doc_stride=doc_stride,
tokenizer_class=tokenizer_class,
tokenizer_args=tokenizer_args,
use_fast=use_fast)
# override processor attributes loaded from config or HF with inferencer params
processor.max_seq_len = max_seq_len
processor.multithreading_rust = multithreading_rust
if hasattr(processor, "doc_stride"):
assert doc_stride < max_seq_len, "doc_stride is longer than max_seq_len. This means that there will be gaps " \
"as the passage windows slide, causing the model to skip over parts of the document. " \
"Please set a lower value for doc_stride (Suggestions: doc_stride=128, max_seq_len=384) "
processor.doc_stride = doc_stride
return cls(model,
processor,
task_type=task_type,
batch_size=batch_size,
gpu=gpu,
name=name,
return_class_probs=return_class_probs,
extraction_strategy=extraction_strategy,
extraction_layer=extraction_layer,
s3e_stats=s3e_stats,
num_processes=num_processes,
disable_tqdm=disable_tqdm,
benchmarking=benchmarking,
dummy_ph=dummy_ph)
def load(
cls,
model_name_or_path,
batch_size=4,
gpu=False,
task_type=None,
return_class_probs=False,
strict=True,
max_seq_len=256,
doc_stride=128,
extraction_layer=None,
extraction_strategy=None,
s3e_stats=None,
num_processes=None,
disable_tqdm=False,
tokenizer_class=None,
use_fast=False,
tokenizer_args=None,
dummy_ph=False,
benchmarking=False,
):
"""
Load an Inferencer incl. all relevant components (model, tokenizer, processor ...) either by
1. specifying a public name from transformers' model hub (https://huggingface.co/models)
2. or pointing to a local directory it is saved in.
:param model_name_or_path: Local directory or public name of the model to load.
:type model_name_or_path: str
:param batch_size: Number of samples computed once per batch
:type batch_size: int
:param gpu: If GPU shall be used
:type gpu: bool
:param task_type: Type of task the model should be used for. Currently supporting:
"embeddings", "question_answering", "text_classification", "ner". More coming soon...
:param task_type: str
:param strict: whether to strictly enforce that the keys loaded from saved model match the ones in
the PredictionHead (see torch.nn.module.load_state_dict()).
Set to `False` for backwards compatibility with PHs saved with older version of FARM.
:type strict: bool
:param max_seq_len: maximum length of one text sample
:type max_seq_len: int
:param doc_stride: Only QA: When input text is longer than max_seq_len it gets split into parts, strided by doc_stride
:type doc_stride: int
:param extraction_strategy: Strategy to extract vectors. Choices: 'cls_token' (sentence vector), 'reduce_mean'
(sentence vector), reduce_max (sentence vector), 'per_token' (individual token vectors)
:type extraction_strategy: str
:param extraction_layer: number of layer from which the embeddings shall be extracted. Default: -1 (very last layer).
:type extraction_layer: int
:param s3e_stats: Stats of a fitted S3E model as returned by `fit_s3e_on_corpus()`
(only needed for task_type="embeddings" and extraction_strategy = "s3e")
:type s3e_stats: dict
:param num_processes: the number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
multiprocessing. Set to None to let Inferencer use all CPU cores minus one. If you want to
debug the Language Model, you might need to disable multiprocessing!
**Warning!** If you use multiprocessing you have to close the
`multiprocessing.Pool` again! To do so call
:func:`~farm.infer.Inferencer.close_multiprocessing_pool` after you are
done using this class. The garbage collector will not do this for you!
:type num_processes: int
:param disable_tqdm: Whether to disable tqdm logging (can get very verbose in multiprocessing)
:type disable_tqdm: bool
:param tokenizer_class: (Optional) Name of the tokenizer class to load (e.g. `BertTokenizer`)
:type tokenizer_class: str
:param use_fast: (Optional, False by default) Indicate if FARM should try to load the fast version of the tokenizer (True) or
use the Python one (False).
:param tokenizer_args: (Optional) Will be passed to the Tokenizer ``__init__`` method.
See https://huggingface.co/transformers/main_classes/tokenizer.html and detailed tokenizer documentation
on `Hugging Face Transformers <https://huggingface.co/transformers/>`_.
:type tokenizer_args: dict
:type use_fast: bool
:param dummy_ph: If True, methods of the prediction head will be replaced
with a dummy method. This is used to isolate lm run time from ph run time.
:type dummy_ph: bool
:param benchmarking: If True, a benchmarking object will be initialised within the class and
certain parts of the code will be timed for benchmarking. Should be kept
False if not benchmarking since these timing checkpoints require synchronization
of the asynchronous Pytorch operations and may slow down the model.
:type benchmarking: bool
:return: An instance of the Inferencer.
"""
if tokenizer_args is None:
tokenizer_args = {}
device, n_gpu = initialize_device_settings(use_cuda=gpu,
local_rank=-1,
use_amp=None)
name = os.path.basename(model_name_or_path)
# a) either from local dir
if os.path.exists(model_name_or_path):
model = BaseAdaptiveModel.load(load_dir=model_name_or_path,
device=device,
strict=strict)
if task_type == "embeddings":
processor = InferenceProcessor.load_from_dir(
model_name_or_path)
else:
processor = Processor.load_from_dir(model_name_or_path)
# override processor attributes loaded from config file with inferencer params
processor.max_seq_len = max_seq_len
if hasattr(processor, "doc_stride"):
processor.doc_stride = doc_stride
# b) or from remote transformers model hub
else:
logger.info(
f"Could not find `{model_name_or_path}` locally. Try to download from model hub ..."
)
if not task_type:
raise ValueError(
"Please specify the 'task_type' of the model you want to load from transformers. "
"Valid options for arg `task_type`:"
"'question_answering', 'embeddings', 'text_classification', 'ner'"
)
model = AdaptiveModel.convert_from_transformers(
model_name_or_path, device, task_type)
config = AutoConfig.from_pretrained(model_name_or_path)
tokenizer = Tokenizer.load(
model_name_or_path,
tokenizer_class=tokenizer_class,
use_fast=use_fast,
**tokenizer_args,
)
# TODO infer task_type automatically from config (if possible)
if task_type == "question_answering":
processor = SquadProcessor(
tokenizer=tokenizer,
max_seq_len=max_seq_len,
label_list=["start_token", "end_token"],
metric="squad",
data_dir="data",
doc_stride=doc_stride)
elif task_type == "embeddings":
processor = InferenceProcessor(tokenizer=tokenizer,
max_seq_len=max_seq_len)
elif task_type == "text_classification":
label_list = list(config.id2label[id]
for id in range(len(config.id2label)))
processor = TextClassificationProcessor(
tokenizer=tokenizer,
max_seq_len=max_seq_len,
data_dir="data",
label_list=label_list,
label_column_name="label",
metric="acc",
quote_char='"',
)
elif task_type == "ner":
label_list = list(config.label2id.keys())
processor = NERProcessor(tokenizer=tokenizer,
max_seq_len=max_seq_len,
data_dir="data",
metric="seq_f1",
label_list=label_list)
else:
raise ValueError(
f"`task_type` {task_type} is not supported yet. "
f"Valid options for arg `task_type`: 'question_answering', "
f"'embeddings', 'text_classification', 'ner'")
if not isinstance(model, ONNXAdaptiveModel):
model, _ = optimize_model(model=model,
device=device,
local_rank=-1,
optimizer=None)
return cls(model,
processor,
task_type=task_type,
batch_size=batch_size,
gpu=gpu,
name=name,
return_class_probs=return_class_probs,
extraction_strategy=extraction_strategy,
extraction_layer=extraction_layer,
s3e_stats=s3e_stats,
num_processes=num_processes,
disable_tqdm=disable_tqdm,
benchmarking=benchmarking,
dummy_ph=dummy_ph)
