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Commit ca6bdb28 authored by thomwolf's avatar thomwolf
Browse files

fix pipelines and rename model_card => modelcard

parent 61d9ee45
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Showing with 117 additions and 82 deletions
transformers/__init__.py
View file @ ca6bdb28
......@@ -35,7 +35,7 @@ if is_sklearn_available():
from .data import glue_compute_metrics, xnli_compute_metrics
# Model Cards
from .model_card import ModelCard
from .modelcard import ModelCard
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
......
transformers/file_utils.py
View file @ ca6bdb28
......@@ -81,7 +81,7 @@ WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = 'tf_model.h5'
TF_WEIGHTS_NAME = 'model.ckpt'
CONFIG_NAME = "config.json"
MODEL_CARD_NAME = "model_card.json"
MODEL_CARD_NAME = "modelcard.json"
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
......@@ -339,7 +339,7 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag
temp_file_manager = tempfile.NamedTemporaryFile
resume_size = 0
if not os.path.exists(cache_path) or force_download:
if etag is not None and (not os.path.exists(cache_path) or force_download):
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
......
transformers/model_card.py → transformers/modelcard.py
View file @ ca6bdb28
......@@ -25,7 +25,8 @@ from io import open
from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .file_utils import CONFIG_NAME, MODEL_CARD_NAME, cached_path, is_remote_url, hf_bucket_url
from .file_utils import CONFIG_NAME, MODEL_CARD_NAME, WEIGHTS_NAME, TF2_WEIGHTS_NAME, \
cached_path, is_remote_url, hf_bucket_url
logger = logging.getLogger(__name__)
......@@ -89,7 +90,7 @@ class ModelCard(object):
- a string with the `shortcut name` of a pre-trained model card to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model card that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a mode card file saved using the :func:`~transformers.ModelCard.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/model_card.json``.
- a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/modelcard.json``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
......@@ -100,16 +101,14 @@ class ModelCard(object):
- The values in kwargs of any keys which are model card attributes will be used to override the loaded values.
- Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the `return_unused_kwargs` keyword parameter.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model card file and override the cached version if it exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
find_from_standard_name: (`optional`) boolean, default True:
If the pretrained_model_name_or_path ends with our standard model or config filenames, replace them with our standard modelcard filename.
Can be used to directly feed a model/config url and access the colocated modelcard.
return_unused_kwargs: (`optional`) bool:
- If False, then this function returns just the final model card object.
......@@ -117,22 +116,21 @@ class ModelCard(object):
Examples::
model_card = ModelCard.from_pretrained('bert-base-uncased') # Download model card from S3 and cache.
model_card = ModelCard.from_pretrained('./test/saved_model/') # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
model_card = ModelCard.from_pretrained('./test/saved_model/model_card.json')
model_card = ModelCard.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
modelcard = ModelCard.from_pretrained('bert-base-uncased') # Download model card from S3 and cache.
modelcard = ModelCard.from_pretrained('./test/saved_model/') # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
modelcard = ModelCard.from_pretrained('./test/saved_model/modelcard.json')
modelcard = ModelCard.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
"""
cache_dir = kwargs.pop('cache_dir', None)
force_download = kwargs.pop('force_download', False)
resume_download = kwargs.pop('resume_download', False)
proxies = kwargs.pop('proxies', None)
find_from_standard_name = kwargs.pop('find_from_standard_name', True)
return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
# For simplicity we use the same pretrained url than the configuration files but with a different suffix (model_card.json)
# For simplicity we use the same pretrained url than the configuration files
# but with a different suffix (modelcard.json). This suffix is replaced below.
model_card_file = ALL_PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
model_card_file = model_card_file.replace(CONFIG_NAME, MODEL_CARD_NAME)
elif os.path.isdir(pretrained_model_name_or_path):
model_card_file = os.path.join(pretrained_model_name_or_path, MODEL_CARD_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
......@@ -140,17 +138,22 @@ class ModelCard(object):
else:
model_card_file = hf_bucket_url(pretrained_model_name_or_path, postfix=MODEL_CARD_NAME)
if find_from_standard_name or pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
model_card_file = model_card_file.replace(CONFIG_NAME, MODEL_CARD_NAME)
model_card_file = model_card_file.replace(WEIGHTS_NAME, MODEL_CARD_NAME)
model_card_file = model_card_file.replace(TF2_WEIGHTS_NAME, MODEL_CARD_NAME)
try:
# Load from URL or cache if already cached
resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, force_download=force_download,
proxies=proxies, resume_download=resume_download)
resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, force_download=True,
proxies=proxies, resume_download=False)
if resolved_model_card_file == model_card_file:
logger.info("loading model card file {}".format(model_card_file))
else:
logger.info("loading model card file {} from cache at {}".format(
model_card_file, resolved_model_card_file))
# Load model card
model_card = cls.from_json_file(resolved_model_card_file)
modelcard = cls.from_json_file(resolved_model_card_file)
except EnvironmentError:
if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
......@@ -166,7 +169,7 @@ class ModelCard(object):
logger.warning("Creating an empty model card.")
# We fall back on creating an empty model card
model_card = cls()
modelcard = cls()
except json.JSONDecodeError:
logger.warning("Couldn't reach server at '{}' to download model card file or "
......@@ -175,22 +178,22 @@ class ModelCard(object):
logger.warning("Creating an empty model card.")
# We fall back on creating an empty model card
model_card = cls()
modelcard = cls()
# Update model card with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(model_card, key):
setattr(model_card, key, value)
if hasattr(modelcard, key):
setattr(modelcard, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info("Model card: %s", str(model_card))
logger.info("Model card: %s", str(modelcard))
if return_unused_kwargs:
return model_card, kwargs
return modelcard, kwargs
else:
return model_card
return modelcard
@classmethod
def from_dict(cls, json_object):
......
transformers/pipelines.py
View file @ ca6bdb28
......@@ -18,6 +18,8 @@ import csv
import json
import os
import pickle
import logging
import six
from abc import ABC, abstractmethod
from contextlib import contextmanager
from itertools import groupby
......@@ -26,8 +28,12 @@ from typing import Union, Optional, Tuple, List, Dict
import numpy as np
from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer, PretrainedConfig, \
SquadExample, squad_convert_examples_to_features, is_tf_available, is_torch_available, logger, BasicTokenizer
from transformers import (AutoConfig, AutoTokenizer, PreTrainedTokenizer,
PretrainedConfig, ModelCard, SquadExample,
squad_convert_examples_to_features, is_tf_available,
is_torch_available, BasicTokenizer,
ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
ALL_PRETRAINED_CONFIG_ARCHIVE_MAP)
if is_tf_available():
import tensorflow as tf
......@@ -40,6 +46,8 @@ if is_torch_available():
AutoModelForQuestionAnswering, AutoModelForTokenClassification
logger = logging.getLogger(__name__)
class ArgumentHandler(ABC):
"""
Base interface for handling varargs for each Pipeline
......@@ -271,11 +279,13 @@ class Pipeline(_ScikitCompat):
nlp = QuestionAnsweringPipeline(model=AutoModel.from_pretrained('...'), tokenizer='...')
"""
def __init__(self, model, tokenizer: PreTrainedTokenizer = None,
modelcard: ModelCard = None,
args_parser: ArgumentHandler = None, device: int = -1,
binary_output: bool = False):
self.model = model
self.tokenizer = tokenizer
self.modelcard = modelcard
self.device = device
self.binary_output = binary_output
self._args_parser = args_parser or DefaultArgumentHandler()
......@@ -294,6 +304,7 @@ class Pipeline(_ScikitCompat):
self.model.save_pretrained(save_directory)
self.tokenizer.save_pretrained(save_directory)
self.modelcard.save_pretrained(save_directory)
def transform(self, X):
"""
......@@ -393,9 +404,10 @@ class FeatureExtractionPipeline(Pipeline):
def __init__(self, model,
tokenizer: PreTrainedTokenizer = None,
modelcard: ModelCard = None,
args_parser: ArgumentHandler = None,
device: int = -1):
super().__init__(model, tokenizer, args_parser, device, binary_output=True)
super().__init__(model, tokenizer, modelcard, args_parser, device, binary_output=True)
def __call__(self, *args, **kwargs):
return super().__call__(*args, **kwargs).tolist()
......@@ -418,9 +430,10 @@ class NerPipeline(Pipeline):
"""
def __init__(self, model, tokenizer: PreTrainedTokenizer = None,
modelcard: ModelCard = None,
args_parser: ArgumentHandler = None, device: int = -1,
binary_output: bool = False):
super().__init__(model, tokenizer, args_parser, device, binary_output)
super().__init__(model, tokenizer, modelcard, args_parser, device, binary_output)
self._basic_tokenizer = BasicTokenizer(do_lower_case=False)
......@@ -554,8 +567,10 @@ class QuestionAnsweringPipeline(Pipeline):
else:
return SquadExample(None, question, context, None, None, None)
def __init__(self, model, tokenizer: Optional[PreTrainedTokenizer], device: int = -1, **kwargs):
super().__init__(model, tokenizer, args_parser=QuestionAnsweringArgumentHandler(),
def __init__(self, model, tokenizer: Optional[PreTrainedTokenizer],
modelcard: Optional[ModelCard],
device: int = -1, **kwargs):
super().__init__(model, tokenizer, modelcard, args_parser=QuestionAnsweringArgumentHandler(),
device=device, **kwargs)
def __call__(self, *texts, **kwargs):
......@@ -725,7 +740,7 @@ SUPPORTED_TASKS = {
'default': {
'model': 'distilbert-base-uncased',
'config': None,
'tokenizer': 'bert-base-uncased'
'tokenizer': 'distilbert-base-uncased'
}
},
'sentiment-analysis': {
......@@ -735,7 +750,7 @@ SUPPORTED_TASKS = {
'default': {
'model': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-pytorch_model.bin',
'config': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-config.json',
'tokenizer': 'bert-base-uncased'
'tokenizer': 'distilbert-base-uncased'
}
},
'ner': {
......@@ -745,7 +760,7 @@ SUPPORTED_TASKS = {
'default': {
'model': 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-pytorch_model.bin',
'config': 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-config.json',
'tokenizer': 'bert-base-cased'
'tokenizer': 'bert-large-cased'
}
},
'question-answering': {
......@@ -755,7 +770,7 @@ SUPPORTED_TASKS = {
'default': {
'model': 'distilbert-base-uncased-distilled-squad',
'config': None,
'tokenizer': 'bert-base-uncased'
'tokenizer': 'distilbert-base-uncased'
}
}
}
......@@ -763,7 +778,9 @@ SUPPORTED_TASKS = {
def pipeline(task: str, model: Optional = None,
config: Optional[Union[str, PretrainedConfig]] = None,
tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, **kwargs) -> Pipeline:
tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
modelcard: Optional[Union[str, ModelCard]] = None,
**kwargs) -> Pipeline:
"""
Utility factory method to build a pipeline.
Pipeline are made of:
......@@ -777,48 +794,63 @@ def pipeline(task: str, model: Optional = None,
pipeline('ner', model=AutoModel.from_pretrained(...), tokenizer=AutoTokenizer.from_pretrained(...)
pipeline('ner', model='https://...pytorch-model.bin', config='https://...config.json', tokenizer='bert-base-cased')
"""
# Try to infer tokenizer from model name (if provided as str)
if tokenizer is None:
if model is not None and not isinstance(model, str):
# Impossible to guest what is the right tokenizer here
raise Exception('Tokenizer cannot be None if provided model is a PreTrainedModel instance')
else:
tokenizer = model
# Retrieve the task
if task not in SUPPORTED_TASKS:
raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys())))
pipeline_framework = 'tf' if is_tf_available() else ('pt' if is_torch_available() else None)
if pipeline_framework is None:
raise ImportError("At least one of TensorFlow 2.0 or PyTorch should be installed. "
"To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
"To install PyTorch, read the instructions at https://pytorch.org/.")
targeted_task = SUPPORTED_TASKS[task]
task, allocator = targeted_task['impl'], targeted_task['tf'] if is_tf_available() else targeted_task['pt']
task, model_class = targeted_task['impl'], targeted_task[pipeline_framework]
# Handling for default model for the task
# Use default model/config/tokenizer for the task if no model is provided
if model is None:
model, config, tokenizer = tuple(targeted_task['default'].values())
# Allocate tokenizer
tokenizer = tokenizer if isinstance(tokenizer, PreTrainedTokenizer) else AutoTokenizer.from_pretrained(tokenizer)
# Try to infer tokenizer from model or config name (if provided as str)
if tokenizer is None:
if isinstance(model, str) and model in ALL_PRETRAINED_MODEL_ARCHIVE_MAP:
tokenizer = model
elif isinstance(config, str) and model in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
tokenizer = config
else:
# Impossible to guest what is the right tokenizer here
raise Exception("Impossible to guess which tokenizer to use. "
"Please provided a PretrainedTokenizer class or a path/url/shortcut name to a pretrained tokenizer.")
# Special handling for model conversion
# Try to infer modelcard from model or config name (if provided as str)
if modelcard is None:
# Try to fallback on one of the provided string for model or config (will replace the suffix)
if isinstance(model, str):
from_tf = model.endswith('.h5') and not is_tf_available()
from_pt = model.endswith('.bin') and not is_torch_available()
modelcard = model
elif isinstance(config, str):
modelcard = config
if from_tf:
logger.warning('Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. '
'Trying to load the model with PyTorch.')
elif from_pt:
logger.warning('Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. '
'Trying to load the model with Tensorflow.')
else:
from_tf = from_pt = False
# Instantiate tokenizer if needed
if isinstance(tokenizer, six.string_types):
tokenizer = AutoTokenizer.from_pretrained(tokenizer)
# Instantiate config if needed
if isinstance(config, str):
config = AutoConfig.from_pretrained(config)
# Instantiate model if needed
if isinstance(model, str):
if allocator.__name__.startswith('TF'):
model = allocator.from_pretrained(model, config=config, from_pt=from_pt)
else:
model = allocator.from_pretrained(model, config=config, from_tf=from_tf)
# Handle transparent TF/PT model conversion
model_kwargs = {}
if pipeline_framework == 'pt' and model.endswith('.h5'):
model_kwargs['from_tf'] = True
logger.warning('Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. '
'Trying to load the model with PyTorch.')
elif pipeline_framework == 'tf' and model.endswith('.bin'):
model_kwargs['from_pt'] = True
logger.warning('Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. '
'Trying to load the model with Tensorflow.')
model = model_class.from_pretrained(model, config=config, **model_kwargs)
return task(model, tokenizer, **kwargs)
transformers/tests/model_card_test.py
View file @ ca6bdb28
......@@ -18,7 +18,7 @@ import os
import json
import unittest
from transformers.model_card import ModelCard
from transformers.modelcard import ModelCard
from .tokenization_tests_commons import TemporaryDirectory
class ModelCardTester(unittest.TestCase):
......@@ -49,20 +49,20 @@ class ModelCardTester(unittest.TestCase):
}
def test_model_card_common_properties(self):
model_card = ModelCard.from_dict(self.inputs_dict)
self.assertTrue(hasattr(model_card, 'model_details'))
self.assertTrue(hasattr(model_card, 'intended_use'))
self.assertTrue(hasattr(model_card, 'factors'))
self.assertTrue(hasattr(model_card, 'metrics'))
self.assertTrue(hasattr(model_card, 'evaluation_data'))
self.assertTrue(hasattr(model_card, 'training_data'))
self.assertTrue(hasattr(model_card, 'quantitative_analyses'))
self.assertTrue(hasattr(model_card, 'ethical_considerations'))
self.assertTrue(hasattr(model_card, 'caveats_and_recommendations'))
modelcard = ModelCard.from_dict(self.inputs_dict)
self.assertTrue(hasattr(modelcard, 'model_details'))
self.assertTrue(hasattr(modelcard, 'intended_use'))
self.assertTrue(hasattr(modelcard, 'factors'))
self.assertTrue(hasattr(modelcard, 'metrics'))
self.assertTrue(hasattr(modelcard, 'evaluation_data'))
self.assertTrue(hasattr(modelcard, 'training_data'))
self.assertTrue(hasattr(modelcard, 'quantitative_analyses'))
self.assertTrue(hasattr(modelcard, 'ethical_considerations'))
self.assertTrue(hasattr(modelcard, 'caveats_and_recommendations'))
def test_model_card_to_json_string(self):
model_card = ModelCard.from_dict(self.inputs_dict)
obj = json.loads(model_card.to_json_string())
modelcard = ModelCard.from_dict(self.inputs_dict)
obj = json.loads(modelcard.to_json_string())
for key, value in self.inputs_dict.items():
self.assertEqual(obj[key], value)
......@@ -70,7 +70,7 @@ class ModelCardTester(unittest.TestCase):
model_card_first = ModelCard.from_dict(self.inputs_dict)
with TemporaryDirectory() as tmpdirname:
filename = os.path.join(tmpdirname, u"model_card.json")
filename = os.path.join(tmpdirname, u"modelcard.json")
model_card_first.to_json_file(filename)
model_card_second = ModelCard.from_json_file(filename)
......
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