import json
import logging
import pathlib
import pandas as pd
from typing import Union, List, Optional, Dict, Any, NamedTuple
import yaml
import mlflow
from mlflow import pyfunc
from mlflow.exceptions import MlflowException
from mlflow.models import ModelInputExample, Model, infer_pip_requirements
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import ModelSignature, infer_signature
from mlflow.models.utils import _save_example
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, BAD_REQUEST
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.types.schema import Schema, ColSpec
from mlflow.types.utils import _validate_input_dictionary_contains_only_strings_and_lists_of_strings
from mlflow.utils.annotations import experimental
from mlflow.utils.docstring_utils import (
format_docstring,
LOG_MODEL_PARAM_DOCS,
docstring_version_compatibility_warning,
)
from mlflow.utils.environment import (
_mlflow_conda_env,
_validate_env_arguments,
_CONDA_ENV_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_process_conda_env,
_process_pip_requirements,
_CONSTRAINTS_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_PythonEnv,
)
from mlflow.utils.file_utils import write_to
from mlflow.utils.model_utils import (
_validate_and_copy_code_paths,
_validate_and_prepare_target_save_path,
_download_artifact_from_uri,
_get_flavor_configuration,
_get_flavor_configuration_from_uri,
_add_code_from_conf_to_system_path,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement
FLAVOR_NAME = "transformers"
_PIPELINE_BINARY_KEY = "pipeline"
_PIPELINE_BINARY_FILE_NAME = "pipeline"
_COMPONENTS_BINARY_KEY = "components"
_INFERENCE_CONFIG_BINARY_KEY = "inference_config.txt"
_MODEL_KEY = "model"
_TOKENIZER_KEY = "tokenizer"
_FEATURE_EXTRACTOR_KEY = "feature_extractor"
_IMAGE_PROCESSOR_KEY = "image_processor"
_PROCESSOR_KEY = "processor"
_TOKENIZER_TYPE_KEY = "tokenizer_type"
_FEATURE_EXTRACTOR_TYPE_KEY = "feature_extractor_type"
_IMAGE_PROCESSOR_TYPE_KEY = "image_processor_type"
_PROCESSOR_TYPE_KEY = "processor_type"
_CARD_TEXT_FILE_NAME = "model_card.md"
_CARD_DATA_FILE_NAME = "model_card_data.yaml"
_TASK_KEY = "task"
_INSTANCE_TYPE_KEY = "instance_type"
_PIPELINE_MODEL_TYPE_KEY = "pipeline_model_type"
_MODEL_PATH_OR_NAME_KEY = "source_model_name"
_SUPPORTED_SAVE_KEYS = {_MODEL_KEY, _TOKENIZER_KEY, _FEATURE_EXTRACTOR_KEY, _IMAGE_PROCESSOR_KEY}
_SUPPORTED_RETURN_TYPES = {"pipeline", "components"}
_logger = logging.getLogger(__name__)
def _model_packages(model) -> List[str]:
"""
Determines which pip libraries should be included based on the base model engine
type.
:param model: The model instance to be saved in order to provide the required underlying
deep learning execution framework dependency requirements.
:return: A list of strings representing the underlying engine-specific dependencies
"""
engine = _get_engine_type(model)
if engine == "torch":
return ["torch", "torchvision"]
else:
return [engine]
[docs]@experimental
def get_default_pip_requirements(model) -> List[str]:
"""
:param model: The model instance to be saved in order to provide the required underlying
deep learning execution framework dependency requirements. Note that this must
be the actual model instance and not a Pipeline.
:return: A list of default pip requirements for MLflow Models that have been produced with the
``transformers`` flavor. Calls to :py:func:`save_model()` and :py:func:`log_model()`
produce a pip environment that contain these requirements at a minimum.
"""
from transformers import TFPreTrainedModel, FlaxPreTrainedModel, PreTrainedModel
if not isinstance(model, (TFPreTrainedModel, FlaxPreTrainedModel, PreTrainedModel)):
raise MlflowException(
"The supplied model type is unsupported. The model must be one of: "
"PreTrainedModel, TFPreTrainedModel, or FlaxPreTrainedModel",
error_code=INVALID_PARAMETER_VALUE,
)
try:
base_reqs = ["transformers", *_model_packages(model)]
return [_get_pinned_requirement(module) for module in base_reqs]
except Exception as e:
dependencies = [
_get_pinned_requirement(module)
for module in ["transformers", "torch", "torchvision", "tensorflow"]
]
_logger.warning(
"Could not infer model execution engine type due to huggingface_hub not "
"being installed or unable to connect in online mode. Adding full "
f"dependency chain: {dependencies}. \nFailure cause: {str(e)}"
)
return dependencies
def _validate_transformers_model_dict(transformers_model):
"""
Validator for a submitted save dictionary for the transformers model. If any additional keys
are provided, raise to indicate which invalid keys were submitted.
"""
if isinstance(transformers_model, dict):
invalid_keys = [key for key in transformers_model.keys() if key not in _SUPPORTED_SAVE_KEYS]
if invalid_keys:
raise MlflowException(
"Invalid dictionary submitted for 'transformers_model'. The "
f"key(s) {invalid_keys} are not permitted. Must be one of: "
f"{_SUPPORTED_SAVE_KEYS}",
error_code=INVALID_PARAMETER_VALUE,
)
if _MODEL_KEY not in transformers_model:
raise MlflowException(
f"The 'transformers_model' dictionary must have an entry for {_MODEL_KEY}",
error_code=INVALID_PARAMETER_VALUE,
)
model = transformers_model[_MODEL_KEY]
else:
model = transformers_model.model
if not hasattr(model, "name_or_path"):
raise MlflowException(
f"The submitted model type {type(model).__name__} does not inherit "
"from a transformers pre-trained model. It is missing the attribute "
"'name_or_path'. Please verify that the model is a supported "
"transformers model.",
error_code=INVALID_PARAMETER_VALUE,
)
[docs]@experimental
def get_default_conda_env(model):
"""
:return: The default Conda environment for MLflow Models produced with the ``transformers``
flavor, based on the model instance framework type of the model to be logged.
"""
return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements(model))
[docs]@experimental
@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME)
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
def save_model(
transformers_model,
path: str,
processor=None,
task: Optional[str] = None,
model_card=None,
inference_config: Optional[Dict[str, Any]] = None,
code_paths: Optional[List[str]] = None,
mlflow_model: Optional[Model] = None,
signature: Optional[ModelSignature] = None,
input_example: Optional[ModelInputExample] = None,
pip_requirements: Optional[Union[List[str], str]] = None,
extra_pip_requirements: Optional[Union[List[str], str]] = None,
conda_env=None,
metadata: Dict[str, Any] = None,
**kwargs,
) -> None:
"""
Save a trained transformers model to a path on the local file system.
:param transformers_model:
A trained transformers `Pipeline` or a dictionary that maps required components of a
pipeline to the named keys of ["model", "image_processor", "tokenizer",
"feature_extractor"]. The `model` key in the dictionary must map to a value that inherits
from `PreTrainedModel`, `TFPreTrainedModel`, or `FlaxPreTrainedModel`.
All other component entries in the dictionary must support the defined task type that is
associated with the base model type configuration.
An example of supplying component-level parts of a transformers model is shown below:
.. code-block:: python
from transformers import MobileBertForQuestionAnswering, AutoTokenizer
architecture = "csarron/mobilebert-uncased-squad-v2"
tokenizer = AutoTokenizer.from_pretrained(architecture)
model = MobileBertForQuestionAnswering.from_pretrained(architecture)
with mlflow.start_run():
components = {
"model": model,
"tokenizer": tokenizer,
}
mlflow.transformers.save_model(
transformers_model=components,
path="path/to/save/model",
)
An example of submitting a `Pipeline` from a default pipeline instantiation:
.. code-block:: python
from transformers import pipeline
qa_pipe = pipeline("question-answering", "csarron/mobilebert-uncased-squad-v2")
with mlflow.start_run():
mlflow.transformers.save_model(
transformers_model=qa_pipe,
path="path/to/save/model",
)
:param path: Local path destination for the serialized model to be saved.
:param processor: An optional ``Processor`` subclass object. Some model architectures,
particularly multi-modal types, utilize Processors to combine text
encoding and image or audio encoding in a single entrypoint.
.. Note:: If a processor is supplied when saving a model, the
model will be unavailable for loading as a ``Pipeline`` or for
usage with pyfunc inference.
:param task: The transformers-specific task type of the model. These strings are utilized so
that a pipeline can be created with the appropriate internal call architecture
to meet the needs of a given model. If this argument is not specified, the
pipeline utilities within the transformers library will be used to infer the
correct task type. If the value specified is not a supported type within the
version of transformers that is currently installed, an Exception will be thrown.
:param model_card: An Optional `ModelCard` instance from `huggingface-hub`. If provided, the
contents of the model card will be saved along with the provided
`transformers_model`. If not provided, an attempt will be made to fetch
the card from the base pretrained model that is provided (or the one that is
included within a provided `Pipeline`).
.. Note:: In order for a ModelCard to be fetched (if not provided),
the huggingface_hub package must be installed and the version
must be >=0.10.0
:param inference_config:
A dict of valid overrides that can be applied to a pipeline instance during inference.
These arguments are used exclusively for the case of loading the model as a ``pyfunc``
Model or for use in Spark.
These values are not applied to a returned Pipeline from a call to
``mlflow.transformers.load_model()``
.. Warning:: If the key provided is not compatible with either the
Pipeline instance for the task provided or is not a valid
override to any arguments available in the Model, an
Exception will be raised at runtime. It is very important
to validate the entries in this dictionary to ensure
that they are valid prior to saving or logging.
An example of providing overrides for a question generation model:
.. code-block:: python
from transformers import pipeline, AutoTokenizer
task = "text-generation"
architecture = "gpt2"
sentence_pipeline = pipeline(
task=task, tokenizer=AutoTokenizer.from_pretrained(architecture), model=architecture
)
# Validate that the overrides function
prompts = ["Generative models are", "I'd like a coconut so that I can"]
# validation of config prior to save or log
inference_config = {
"top_k": 2,
"num_beams": 5,
"max_length": 30,
"temperature": 0.62,
"top_p": 0.85,
"repetition_penalty": 1.15,
}
# Verify that no exceptions are thrown
sentence_pipeline(prompts, **inference_config)
mlflow.transformers.save_model(
transformers_model=sentence_pipeline,
path="/path/for/model",
task=task,
inference_config=inference_config,
)
:param code_paths: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files are *prepended* to the system
path when the model is loaded.
:param mlflow_model: An MLflow model object that specifies the flavor that this model is being
added to.
:param signature: A Model Signature object that describes the input and output Schema of the
model. The model signature can be inferred using `infer_signature` function
of `mlflow.models.signature`.
Example:
.. code-block:: python
from mlflow.models.signature import infer_signature
from mlflow.transformers import generate_signature_output
from transformers import pipeline
en_to_de = pipeline("translation_en_to_de")
data = "MLflow is great!"
output = generate_signature_output(en_to_de, data)
signature = infer_signature(data, output)
mlflow.transformers.save_model(
transformers_model=en_to_de,
path="/path/to/save/model",
signature=signature,
input_example=data,
)
loaded = mlflow.pyfunc.load_model("/path/to/save/model")
print(loaded.predict(data))
# MLflow ist großartig!
If an input_example is provided and the signature is not, a signature will
be inferred automatically and applied to the MLmodel file iff the
pipeline type is a text-based model (NLP). If the pipeline type is not
a supported type, this inference functionality will not function correctly
and a warning will be issued. In order to ensure that a precise signature
is logged, it is recommended to explicitly provide one.
:param input_example: An example of valid input that the model can accept. The example can be
used as a hint of what data to feed the model. The given example will be
converted to a `Pandas DataFrame` and then serialized to JSON using the
`Pandas` split-oriented format. Bytes are base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
:param conda_env: {{ conda_env }}
:param metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.
.. Note:: Experimental: This parameter may change or be removed in a future
release without warning.
:param kwargs: Optional additional configurations for transformers serialization.
:return: None
"""
import transformers
_validate_transformers_model_dict(transformers_model)
if isinstance(transformers_model, dict):
transformers_model = _TransformersModel.from_dict(**transformers_model)
_validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
path = pathlib.Path(path).absolute()
_validate_and_prepare_target_save_path(str(path))
code_dir_subpath = _validate_and_copy_code_paths(code_paths, str(path))
resolved_task = _get_or_infer_task_type(transformers_model, task)
if not isinstance(transformers_model, transformers.Pipeline):
built_pipeline = _build_pipeline_from_model_input(transformers_model, resolved_task)
else:
built_pipeline = transformers_model
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
input_example = _format_input_example_for_special_cases(input_example, built_pipeline)
_save_example(mlflow_model, input_example, str(path))
if metadata is not None:
mlflow_model.metadata = metadata
flavor_conf = _generate_base_flavor_configuration(built_pipeline, resolved_task)
components = _record_pipeline_components(built_pipeline)
if components:
flavor_conf.update(**components)
if processor:
flavor_conf.update({_PROCESSOR_TYPE_KEY: _get_instance_type(processor)})
# Save the pipeline object
built_pipeline.save_pretrained(save_directory=path.joinpath(_PIPELINE_BINARY_FILE_NAME))
# Save the components explicitly to the components directory
if components:
_save_components(
root_path=path.joinpath(_COMPONENTS_BINARY_KEY),
component_config=components,
pipeline=built_pipeline,
processor=processor,
inference_config=inference_config,
)
# Get the model card from either the argument or the HuggingFace marketplace
card_data = model_card if model_card is not None else _fetch_model_card(transformers_model)
# If the card data can be acquired, save the text and the data separately
if card_data:
path.joinpath(_CARD_TEXT_FILE_NAME).write_text(card_data.text)
with path.joinpath(_CARD_DATA_FILE_NAME).open("w") as file:
yaml.safe_dump(card_data.data.to_dict(), stream=file, default_flow_style=False)
model_bin_kwargs = {_PIPELINE_BINARY_KEY: _PIPELINE_BINARY_FILE_NAME}
# Only allow a subset of task types to have a pyfunc definition.
# Currently supported types are NLP-based language tasks which have a pipeline definition
# consisting exclusively of a Model and a Tokenizer.
if _should_add_pyfunc_to_model(built_pipeline):
# For pyfunc supported models, if a signature is not supplied, infer the signature
# from the input_example if provided, otherwise, apply a generic signature.
if not signature:
mlflow_model.signature = _get_default_pipeline_signature(built_pipeline, input_example)
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.transformers",
conda_env=_CONDA_ENV_FILE_NAME,
python_env=_PYTHON_ENV_FILE_NAME,
code=code_dir_subpath,
**model_bin_kwargs,
)
else:
if processor:
reason = "the model has been saved with a 'processor' argument supplied."
else:
reason = (
"the model is not a language-based model and requires a complex input type "
"that is currently not supported."
)
_logger.warning(
f"This model is unable to be used for pyfunc prediction because {reason} "
f"The pyfunc flavor will not be added to the Model."
)
flavor_conf.update(**model_bin_kwargs)
mlflow_model.add_flavor(
FLAVOR_NAME,
transformers_version=transformers.__version__,
code=code_dir_subpath,
**flavor_conf,
)
mlflow_model.save(str(path.joinpath(MLMODEL_FILE_NAME)))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements(transformers_model.model)
inferred_reqs = infer_pip_requirements(str(path), FLAVOR_NAME, fallback=default_reqs)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs, pip_requirements, extra_pip_requirements
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env)
with path.joinpath(_CONDA_ENV_FILE_NAME).open("w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
if pip_constraints:
write_to(str(path.joinpath(_CONSTRAINTS_FILE_NAME)), "\n".join(pip_constraints))
write_to(str(path.joinpath(_REQUIREMENTS_FILE_NAME)), "\n".join(pip_requirements))
_PythonEnv.current().to_yaml(str(path.joinpath(_PYTHON_ENV_FILE_NAME)))
[docs]@experimental
@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME)
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
def log_model(
transformers_model,
artifact_path: str,
processor=None,
task: Optional[str] = None,
model_card=None,
inference_config: Optional[Dict[str, Any]] = None,
code_paths: Optional[List[str]] = None,
registered_model_name: str = None,
signature: Optional[ModelSignature] = None,
input_example: Optional[ModelInputExample] = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requirements: Optional[Union[List[str], str]] = None,
extra_pip_requirements: Optional[Union[List[str], str]] = None,
conda_env=None,
metadata: Dict[str, Any] = None,
**kwargs,
):
"""
Log a ``transformers`` object as an MLflow artifact for the current run.
:param transformers_model:
A trained transformers `Pipeline` or a dictionary that maps required components of a
pipeline to the named keys of ["model", "image_processor", "tokenizer",
"feature_extractor"]. The `model` key in the dictionary must map to a value that inherits
from `PreTrainedModel`, `TFPreTrainedModel`, or `FlaxPreTrainedModel`.
All other component entries in the dictionary must support the defined task type that is
associated with the base model type configuration.
An example of supplying component-level parts of a transformers model is shown below:
.. code-block:: python
from transformers import MobileBertForQuestionAnswering, AutoTokenizer
architecture = "csarron/mobilebert-uncased-squad-v2"
tokenizer = AutoTokenizer.from_pretrained(architecture)
model = MobileBertForQuestionAnswering.from_pretrained(architecture)
with mlflow.start_run():
components = {
"model": model,
"tokenizer": tokenizer,
}
mlflow.transformers.log_model(
transformers_model=components,
artifact_path="my_model",
)
An example of submitting a `Pipeline` from a default pipeline instantiation:
.. code-block:: python
from transformers import pipeline
qa_pipe = pipeline("question-answering", "csarron/mobilebert-uncased-squad-v2")
with mlflow.start_run():
mlflow.transformers.log_model(
transformers_model=qa_pipe,
artifact_path="my_pipeline",
)
:param artifact_path: Local path destination for the serialized model to be saved.
:param processor: An optional ``Processor`` subclass object. Some model architectures,
particularly multi-modal types, utilize Processors to combine text
encoding and image or audio encoding in a single entrypoint.
.. Note:: If a processor is supplied when logging a model, the
model will be unavailable for loading as a ``Pipeline`` or for usage
with pyfunc inference.
:param task: The transformers-specific task type of the model. These strings are utilized so
that a pipeline can be created with the appropriate internal call architecture
to meet the needs of a given model. If this argument is not specified, the
pipeline utilities within the transformers library will be used to infer the
correct task type. If the value specified is not a supported type within the
version of transformers that is currently installed, an Exception will be thrown.
:param model_card: An Optional `ModelCard` instance from `huggingface-hub`. If provided, the
contents of the model card will be saved along with the provided
`transformers_model`. If not provided, an attempt will be made to fetch
the card from the base pretrained model that is provided (or the one that is
included within a provided `Pipeline`).
.. Note:: In order for a ModelCard to be fetched (if not provided),
the huggingface_hub package must be installed and the version
must be >=0.10.0
:param inference_config:
A dict of valid overrides that can be applied to a pipeline instance during inference.
These arguments are used exclusively for the case of loading the model as a ``pyfunc``
Model or for use in Spark.
These values are not applied to a returned Pipeline from a call to
``mlflow.transformers.load_model()``
.. Warning:: If the key provided is not compatible with either the
Pipeline instance for the task provided or is not a valid
override to any arguments available in the Model, an
Exception will be raised at runtime. It is very important
to validate the entries in this dictionary to ensure
that they are valid prior to saving or logging.
An example of providing overrides for a question generation model:
.. code-block:: python
from transformers import pipeline, AutoTokenizer
task = "text-generation"
architecture = "gpt2"
sentence_pipeline = pipeline(
task=task, tokenizer=AutoTokenizer.from_pretrained(architecture), model=architecture
)
# Validate that the overrides function
prompts = ["Generative models are", "I'd like a coconut so that I can"]
# validation of config prior to save or log
inference_config = {
"top_k": 2,
"num_beams": 5,
"max_length": 30,
"temperature": 0.62,
"top_p": 0.85,
"repetition_penalty": 1.15,
}
# Verify that no exceptions are thrown
sentence_pipeline(prompts, **inference_config)
with mlflow.start_run():
mlflow.transformers.log_model(
transformers_model=sentence_pipeline,
artifact_path="my_sentence_generator",
task=task,
inference_config=inference_config,
)
:param code_paths: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files are *prepended* to the system
path when the model is loaded.
:param registered_model_name: This argument may change or be removed in a
future release without warning. If given, create a model
version under ``registered_model_name``, also creating a
registered model if one with the given name does not exist.
:param signature: A Model Signature object that describes the input and output Schema of the
model. The model signature can be inferred using `infer_signature` function
of `mlflow.models.signature`.
Example:
.. code-block:: python
from mlflow.models.signature import infer_signature
from mlflow.transformers import generate_signature_output
from transformers import pipeline
en_to_de = pipeline("translation_en_to_de")
data = "MLflow is great!"
output = generate_signature_output(en_to_de, data)
signature = infer_signature(data, output)
with mlflow.start_run() as run:
mlflow.transformers.log_model(
transformers_model=en_to_de,
artifact_path="english_to_german_translator",
signature=signature,
input_example=data,
)
model_uri = f"runs:/{run.info.run_id}/english_to_german_translator"
loaded = mlflow.pyfunc.load_model(model_uri)
print(loaded.predict(data))
# MLflow ist großartig!
If an input_example is provided and the signature is not, a signature will
be inferred automatically and applied to the MLmodel file iff the
pipeline type is a text-based model (NLP). If the pipeline type is not
a supported type, this inference functionality will not function correctly
and a warning will be issued. In order to ensure that a precise signature
is logged, it is recommended to explicitly provide one.
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a ``Pandas DataFrame`` and
then serialized to json using the ``Pandas`` split-oriented format.
Bytes are base64-encoded.
:param await_registration_for: Number of seconds to wait for the model version
to finish being created and is in ``READY`` status.
By default, the function waits for five minutes.
Specify 0 or None to skip waiting.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
:param conda_env: {{ conda_env }}
:param metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.
.. Note:: Experimental: This parameter may change or be removed in a future
release without warning.
:param kwargs: Additional arguments for :py:class:`mlflow.models.model.Model`
"""
return Model.log(
artifact_path=artifact_path,
flavor=mlflow.transformers,
registered_model_name=registered_model_name,
await_registration_for=await_registration_for,
metadata=metadata,
transformers_model=transformers_model,
processor=processor,
task=task,
model_card=model_card,
inference_config=inference_config,
conda_env=conda_env,
code_paths=code_paths,
signature=signature,
input_example=input_example,
pip_requirements=pip_requirements,
extra_pip_requirements=extra_pip_requirements,
**kwargs,
)
[docs]@experimental
@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME)
def load_model(model_uri: str, dst_path: str = None, return_type="pipeline", **kwargs):
"""
Load a ``transformers`` object from a local file or a run.
:param model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
- ``mlflow-artifacts:/path/to/model``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/tracking.html#
artifact-locations>`_.
:param dst_path: The local filesystem path to utilize for downloading the model artifact.
This directory must already exist if provided. If unspecified, a local output
path will be created.
:param return_type: A return type modifier for the stored ``transformers`` object.
If set as "components", the return type will be a dictionary of the saved
individual components of either the ``Pipeline`` or the pre-trained model.
The components for NLP-focused models will typically consist of a
return representation as shown below with a text-classification example:
.. code-block:: python
{"model": BertForSequenceClassification, "tokenizer": BertTokenizerFast}
Vision models will return an ``ImageProcessor`` instance of the appropriate
type, while multi-modal models will return both a ``FeatureExtractor`` and
a ``Tokenizer`` along with the model.
Returning "components" can be useful for certain model types that do not
have the desired pipeline return types for certain use cases.
If set as "pipeline", the model, along with any and all required
``Tokenizer``, ``FeatureExtractor``, ``Processor``, or ``ImageProcessor``
objects will be returned within a ``Pipeline`` object of the appropriate
type defined by the ``task`` set by the model instance type. To override
this behavior, supply a valid ``task`` argument during model logging or
saving. Default is "pipeline".
:param kwargs: Optional configuration options for loading of a ``transformers`` object.
For information on parameters and their usage, see
`transformers documentation <https://huggingface.co/docs/transformers/index>`_.
:return: A ``transformers`` model instance or a dictionary of components
"""
if return_type not in _SUPPORTED_RETURN_TYPES:
raise MlflowException(
f"The specified return_type mode '{return_type}' is unsupported. "
"Please select one of: 'pipeline' or 'components'.",
error_code=INVALID_PARAMETER_VALUE,
)
model_uri = str(model_uri)
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path)
flavor_config = _get_flavor_configuration_from_uri(model_uri, FLAVOR_NAME, _logger)
if return_type == "pipeline" and _PROCESSOR_TYPE_KEY in flavor_config:
raise MlflowException(
"This model has been saved with a processor. Processor objects are "
"not compatible with Pipelines. Please load this model by specifying "
"the 'return_type'='components'.",
error_code=BAD_REQUEST,
)
_add_code_from_conf_to_system_path(local_model_path, flavor_config)
return _load_model(local_model_path, flavor_config, return_type, **kwargs)
def _load_model(path: str, flavor_config, return_type: str, **kwargs):
"""
Loads components from a locally serialized ``Pipeline`` object.
"""
import transformers
local_path = pathlib.Path(path)
pipeline_path = local_path.joinpath(
flavor_config.get(_PIPELINE_BINARY_KEY, _PIPELINE_BINARY_FILE_NAME)
)
model_instance = getattr(transformers, flavor_config[_PIPELINE_MODEL_TYPE_KEY])
conf = {
"task": flavor_config[_TASK_KEY],
"model": model_instance.from_pretrained(pipeline_path),
}
if _PROCESSOR_TYPE_KEY in flavor_config:
conf[_PROCESSOR_KEY] = _load_component(
local_path, _PROCESSOR_KEY, flavor_config[_PROCESSOR_TYPE_KEY]
)
for component_key in flavor_config[_COMPONENTS_BINARY_KEY]:
component_type_key = f"{component_key}_type"
component_type = flavor_config[component_type_key]
conf[component_key] = _load_component(local_path, component_key, component_type)
if return_type == "pipeline":
conf.update(**kwargs)
return transformers.pipeline(**conf)
elif return_type == "components":
return conf
def _fetch_model_card(model_or_pipeline):
"""
Attempts to retrieve the model card for the specified model architecture iff the
`huggingface_hub` library is installed. If a card cannot be found in the registry or
the library is not installed, returns None.
"""
try:
import huggingface_hub as hub
except ImportError:
_logger.warning(
"Unable to store ModelCard data with the saved artifact. In order to "
"preserve this information, please install the huggingface_hub package "
"by running 'pip install huggingingface_hub>0.10.0'"
)
return
model = model_or_pipeline.model
if hasattr(hub, "ModelCard"):
try:
return hub.ModelCard.load(model.name_or_path)
except Exception as e:
_logger.warning(f"The model card could not be retrieved from the hub due to {e}")
else:
_logger.warning(
f"The version of huggingface_hub that is installed does not provide "
f"ModelCard functionality. You have version {hub.__version__} installed. "
f"Update huggingface_hub to >= '0.10.0' to retrieve the ModelCard data."
)
def _build_pipeline_from_model_input(model, task: str):
"""
Utility for generating a pipeline from component parts. If required components are not
specified, use the transformers library pipeline component validation to force raising an
exception. The underlying Exception thrown in transformers is verbose enough for diagnosis.
"""
from transformers import pipeline
pipeline_config = model.to_dict()
pipeline_config.update({"task": task})
try:
return pipeline(**pipeline_config)
except Exception as e:
raise MlflowException(
"The provided model configuration cannot be created as a Pipeline. "
"Please verify that all required and compatible components are "
"specified with the correct keys.",
error_code=INVALID_PARAMETER_VALUE,
) from e
def _record_pipeline_components(pipeline) -> Dict[str, Any]:
"""
Utility for recording which components are present in either the generated pipeline iff the
supplied save object is not a pipeline or the components of the supplied pipeline object.
"""
components_conf = {}
components = []
for attr, key in [
("feature_extractor", _FEATURE_EXTRACTOR_TYPE_KEY),
("tokenizer", _TOKENIZER_TYPE_KEY),
("image_processor", _IMAGE_PROCESSOR_TYPE_KEY),
]:
component = getattr(pipeline, attr, None)
if component is not None:
components_conf.update({key: _get_instance_type(component)})
components.append(attr)
if components:
components_conf.update({_COMPONENTS_BINARY_KEY: components})
return components_conf
def _save_components(
root_path: pathlib.Path, component_config: Dict[str, Any], pipeline, processor, inference_config
):
"""
Saves non-model pipeline components explicitly to a separate directory path for compatibility
with certain older model types. In earlier versions of ``transformers``, inferred feature
extractor types could be resolved to their correct processor types. This approach ensures
compatibility with the expected pipeline configuration argument assignments in later versions
of the ``Pipeline`` class.
"""
component_types = component_config.get(_COMPONENTS_BINARY_KEY, [])
for component_name in component_types:
component = getattr(pipeline, component_name)
component.save_pretrained(root_path.joinpath(component_name))
if processor:
processor.save_pretrained(root_path.joinpath(_PROCESSOR_KEY))
if inference_config:
root_path.joinpath(_INFERENCE_CONFIG_BINARY_KEY).write_text(json.dumps(inference_config))
def _load_component(root_path: pathlib.Path, component_key: str, component_type):
"""
Loads an individual component object from local disk.
"""
import transformers
components_dir = root_path.joinpath(_COMPONENTS_BINARY_KEY)
component_path = components_dir.joinpath(component_key)
component_instance = getattr(transformers, component_type)
return component_instance.from_pretrained(component_path)
def _generate_base_flavor_configuration(
model,
task: str,
) -> Dict[str, str]:
"""
Generates the base flavor metadata needed for reconstructing a pipeline from saved
components. This is important because the ``Pipeline`` class does not have a loader
functionality. The serialization of a Pipeline saves the model, configurations, and
metadata for ``FeatureExtractor``s, ``Processor``s, and ``Tokenizer``s exclusively.
This function extracts key information from the submitted model object so that the precise
instance types can be loaded correctly.
"""
_validate_transformers_task_type(task)
flavor_configuration = {
_TASK_KEY: task,
_INSTANCE_TYPE_KEY: _get_instance_type(model),
_MODEL_PATH_OR_NAME_KEY: _get_base_model_architecture(model),
_PIPELINE_MODEL_TYPE_KEY: _get_instance_type(model.model),
}
return flavor_configuration
def _get_or_infer_task_type(model, task: Optional[str] = None) -> str:
"""
Validates that a supplied task type is supported by the ``transformers`` library if supplied,
else, if not supplied, infers the appropriate task type based on the model type.
"""
if task:
_validate_transformers_task_type(task)
else:
task = _infer_transformers_task_type(model)
return task
def _infer_transformers_task_type(model) -> str:
"""
Performs inference of the task type, used in generating a pipeline object based on the
underlying model's intended use case. This utility relies on the definitions within the
transformers pipeline construction utility functions.
:param model: Either the model or the Pipeline object that the task will be extracted or
inferred from
:return: The task type string
"""
from transformers import Pipeline
from transformers.pipelines import get_task
if isinstance(model, Pipeline):
return model.task
elif isinstance(model, _TransformersModel):
try:
return get_task(model.model.name_or_path)
except Exception as e:
raise MlflowException(
"The task type cannot be inferred from the submitted Pipeline or dictionary of "
"model components. Please provide the task type explicitly when saving or logging "
"this submitted Pipeline or dictionary of components.",
error_code=BAD_REQUEST,
) from e
else:
raise MlflowException(
f"The provided model type: {type(model)} is not supported. "
"Supported model types are: Pipeline or a dictionary with specific named keys. "
"Run `help(mlflow.transformers.save_model)` to see details of supported types.",
error_code=BAD_REQUEST,
)
def _validate_transformers_task_type(task: str) -> None:
"""
Validates that a given ``task`` type is supported by the ``transformers`` library and has been
registered in the hub.
"""
from transformers.pipelines import get_supported_tasks
valid_tasks = get_supported_tasks()
if task not in valid_tasks and not task.startswith("translation"):
raise MlflowException(
f"The task provided is invalid. '{task}' is not a supported task. "
f"Must be one of the registered tasks: {valid_tasks}",
error_code=BAD_REQUEST,
)
def _get_engine_type(model):
"""
Determines the underlying execution engine for the model based on the 3 currently supported
deep learning framework backends: ``tensorflow``, ``torch``, or ``flax``.
"""
from transformers import PreTrainedModel, TFPreTrainedModel, FlaxPreTrainedModel
for cls in model.__class__.__mro__:
if issubclass(cls, TFPreTrainedModel):
return "tensorflow"
elif issubclass(cls, PreTrainedModel):
return "torch"
elif issubclass(cls, FlaxPreTrainedModel):
return "flax"
def _get_base_model_architecture(model_or_pipeline):
"""
Extracts the base model architecture type from a submitted model.
"""
from transformers import Pipeline
if isinstance(model_or_pipeline, Pipeline):
return model_or_pipeline.model.name_or_path
else:
return model_or_pipeline[_MODEL_KEY].name_or_path
def _get_instance_type(obj):
"""
Utility for extracting the saved object type or, if the `base` argument is set to `True`,
the base ABC type of the model.
"""
return obj.__class__.__name__
def _should_add_pyfunc_to_model(pipeline) -> bool:
"""
Discriminator for determining whether a particular task type and model instance from within
a ``Pipeline`` is currently supported for the pyfunc flavor.
Currently, the only tasks that are supported are NLP-based tasks wherein a Pipeline consists
solely of a ``Tokenizer`` and a pre-trained model.
Audio, Image, and Video pipelines can still be logged and used, but are not available for
loading as pyfunc.
Similarly, esoteric model types (Graph Models, Timeseries Models, and Reinforcement Learning
Models) are not permitted for loading as pyfunc.
"""
import transformers
exclusion_model_types = {
"GraphormerPreTrainedModel",
"InformerPreTrainedModel",
"TimeSeriesTransformerPreTrainedModel",
"DecisionTransformerPreTrainedModel",
}
impermissible_attrs = {"feature_extractor", "image_processor"}
for attr in impermissible_attrs:
if getattr(pipeline, attr, None) is not None:
return False
for model_type in exclusion_model_types:
if hasattr(transformers, model_type):
if isinstance(pipeline.model, getattr(transformers, model_type)):
return False
return True
def _format_input_example_for_special_cases(input_example, pipeline):
"""
Handles special formatting for specific types of Pipelines so that the displayed example
reflects the correct example input structure that mirrors the behavior of the input parsing
for pyfunc.
"""
import transformers
if (
isinstance(pipeline, transformers.ZeroShotClassificationPipeline)
and isinstance(input_example, dict)
and isinstance(input_example["candidate_labels"], list)
):
input_example["candidate_labels"] = json.dumps(input_example["candidate_labels"])
return input_example
def _get_default_pipeline_signature(pipeline, example=None) -> ModelSignature:
"""
Assigns a default ModelSignature for a given Pipeline type that has pyfunc support. These
default signatures should only be generated and assigned when saving a model iff the user
has not supplied a signature.
For signature inference in some Pipelines that support complex input types, an input example
is needed.
"""
import transformers
if example:
try:
inference_pyfunc = _TransformersWrapper(pipeline)
return infer_signature(example, inference_pyfunc.predict(example))
except Exception as e:
_logger.warning(
"Attempted to generate a signature for the saved model or pipeline "
f"but encountered an error: {e}"
)
else:
if isinstance(
pipeline,
(
transformers.TokenClassificationPipeline,
transformers.ConversationalPipeline,
transformers.TranslationPipeline,
transformers.TextClassificationPipeline,
transformers.FillMaskPipeline,
transformers.TextGenerationPipeline,
),
):
return ModelSignature(
inputs=Schema([ColSpec("string")]), outputs=Schema([ColSpec("string")])
)
elif isinstance(pipeline, transformers.ZeroShotClassificationPipeline):
return ModelSignature(
inputs=Schema(
[
ColSpec("string", name="sequences"),
ColSpec("string", name="candidate_labels"),
ColSpec("string", name="hypothesis_template"),
]
),
outputs=Schema([ColSpec("string")]),
)
elif isinstance(
pipeline,
(
transformers.TableQuestionAnsweringPipeline,
transformers.Text2TextGenerationPipeline,
transformers.QuestionAnsweringPipeline,
),
):
column_1 = None
column_2 = None
if isinstance(pipeline, transformers.TableQuestionAnsweringPipeline):
column_1 = "query"
column_2 = "table"
elif isinstance(pipeline, transformers.Text2TextGenerationPipeline):
column_1 = "answer"
column_2 = "context"
elif isinstance(pipeline, transformers.QuestionAnsweringPipeline):
column_1 = "question"
column_2 = "context"
return ModelSignature(
inputs=Schema(
[
ColSpec("string", name=column_1),
ColSpec("string", name=column_2),
]
),
outputs=Schema([ColSpec("string")]),
)
else:
_logger.warning(
"An unsupported Pipeline type was supplied for signature inference. "
"Either provide an `input_example` or generate a signature manually "
"via `infer_signature` if you would like to have a signature recorded "
"in the MLmodel file."
)
class _TransformersModel(NamedTuple):
"""
Type validator class for models that are submitted as a dictionary for saving and logging.
Usage of this class should always leverage the type-checking from the class method
'from_dict()' instead of the instance-based configuration that is utilized with instantiating
a NamedTuple instance (it uses '__new__()' instead of an '__init__()' dunder method, making
type validation on instantiation overly complex if we were to support that approach).
"""
# NB: Assigning Any type here to eliminate local imports. Type validation is performed when
# calling the `from_dict` class method.
model: Any
tokenizer: Any = None
feature_extractor: Any = None
image_processor: Any = None
processor: Any = None
def to_dict(self):
dict_repr = self._asdict()
# NB: due to breaking changes in APIs, newer pipeline-supported argument keys are not
# backwards compatible. If there isn't an instance present, do not return an empty
# key to value mapping.
return {name: obj for name, obj in dict_repr.items() if obj}
@staticmethod
def _build_exception_msg(obj, obj_name, valid_types):
type_msg = (
"one of: " + ", ".join([valid_type.__name__ for valid_type in valid_types])
if isinstance(valid_types, tuple)
else valid_types.__name__
)
return (
f"The {obj_name} type submitted is not compatible with the transformers flavor: "
f"'{type(obj).__name__}'. "
f"The allowed types must inherit from {type_msg}."
)
@classmethod
def _validate_submitted_types(
cls, model, tokenizer, feature_extractor, image_processor, processor
):
from transformers import (
PreTrainedModel,
TFPreTrainedModel,
FlaxPreTrainedModel,
PreTrainedTokenizerBase,
FeatureExtractionMixin,
ImageFeatureExtractionMixin,
ImageProcessingMixin,
ProcessorMixin,
)
validation = [
(model, "model", (PreTrainedModel, TFPreTrainedModel, FlaxPreTrainedModel)),
(tokenizer, "tokenizer", PreTrainedTokenizerBase),
(
feature_extractor,
"feature_extractor",
(
FeatureExtractionMixin,
ImageFeatureExtractionMixin,
ProcessorMixin,
ImageProcessingMixin,
),
),
(image_processor, "image_processor", ImageProcessingMixin),
(processor, "processor", ProcessorMixin),
]
invalid_types = []
for arg, name, types in validation:
if arg and not isinstance(arg, types):
invalid_types.append(cls._build_exception_msg(arg, name, types))
if invalid_types:
raise MlflowException("\n".join(invalid_types), error_code=BAD_REQUEST)
@classmethod
def from_dict(
cls,
model,
tokenizer=None,
feature_extractor=None,
image_processor=None,
processor=None,
**kwargs,
):
cls._validate_submitted_types(
model, tokenizer, feature_extractor, image_processor, processor
)
return _TransformersModel(model, tokenizer, feature_extractor, image_processor, processor)
def _get_inference_config(local_path):
"""
Load the inference config if it was provided for use in the `_TransformersWrapper` pyfunc
Model wrapper.
"""
config_path = local_path.joinpath("inference_config.txt")
if config_path.exists():
return json.loads(config_path.read_text())
def _load_pyfunc(path):
"""
Loads the model as pyfunc model
"""
local_path = pathlib.Path(path)
flavor_configuration = _get_flavor_configuration(local_path, FLAVOR_NAME)
inference_config = _get_inference_config(local_path.joinpath(_COMPONENTS_BINARY_KEY))
return _TransformersWrapper(
_load_model(str(local_path), flavor_configuration, "pipeline"),
flavor_configuration,
inference_config,
)
[docs]@experimental
def generate_signature_output(pipeline, data):
"""
Utility for generating the response output for the purposes of extracting an output signature
for model saving and logging. This function simulates loading of a saved model or pipeline
as a ``pyfunc`` model without having to incur a write to disk.
:param pipeline: A ``transformers`` pipeline object. Note that component-level or model-level
inputs are not permitted for extracting an output example.
:param data: An example input that is compatible with the given pipeline
:return: The output from the ``pyfunc`` pipeline wrapper's ``predict`` method
"""
import transformers
if not isinstance(pipeline, transformers.Pipeline):
raise MlflowException(
f"The pipeline type submitted is not a valid transformers Pipeline. "
f"The type {type(pipeline).__name__} is not supported.",
error_code=INVALID_PARAMETER_VALUE,
)
return _TransformersWrapper(pipeline).predict(data)
class _TransformersWrapper:
def __init__(self, pipeline, flavor_config=None, inference_config=None):
self.pipeline = pipeline
self.flavor_config = flavor_config
self.inference_config = inference_config
self._conversation = None
# NB: Current special-case custom pipeline types that have not been added to
# the native-supported transformers package but require custom parsing:
# InstructionTextGenerationPipeline [Dolly] https://huggingface.co/databricks/dolly-v2-12b
# (and all variants)
self._supported_custom_generator_types = {"InstructionTextGenerationPipeline"}
def _convert_pandas_to_dict(self, data):
import transformers
if not isinstance(self.pipeline, transformers.ZeroShotClassificationPipeline):
return data.to_dict(orient="records")
else:
# NB: The ZeroShotClassificationPipeline requires an input in the form of
# Dict[str, Union[str, List[str]]] and will throw if an additional nested
# List is present within the List value (which is what the duplicated values
# within the orient="list" conversion in Pandas will do. This parser will
# deduplicate label lists to a single list.
unpacked = data.to_dict(orient="list")
parsed = {}
for key, value in unpacked.items():
if isinstance(value, list):
contents = []
for item in value:
# Deduplication logic
if item not in contents:
contents.append(item)
# Collapse nested lists to return the correct data structure for the
# ZeroShotClassificationPipeline input structure
parsed[key] = (
contents
if all(isinstance(item, str) for item in contents) and len(contents) > 1
else contents[0]
)
return parsed
def predict(self, data):
if isinstance(data, pd.DataFrame):
input_data = self._convert_pandas_to_dict(data)
elif isinstance(data, dict):
input_data = data
elif isinstance(data, list):
if not all(isinstance(entry, (str, dict)) for entry in data):
raise MlflowException(
"Invalid data submission. Ensure all elements in the list are strings "
"or dictionaries. If dictionaries are supplied, all keys in the "
"dictionaries must be strings and values must be either str or List[str].",
error_code=INVALID_PARAMETER_VALUE,
)
input_data = data
elif isinstance(data, str):
input_data = data
else:
raise MlflowException(
"Input data must be either a pandas.DataFrame, a string, List[str], "
"List[Dict[str, str]], List[Dict[str, Union[str, List[str]]]], "
"or Dict[str, Union[str, List[str]]].",
error_code=INVALID_PARAMETER_VALUE,
)
input_data = self._parse_raw_pipeline_input(input_data)
# Validate resolved or input dict types
if isinstance(input_data, dict):
_validate_input_dictionary_contains_only_strings_and_lists_of_strings(input_data)
elif isinstance(input_data, list) and all(isinstance(entry, dict) for entry in input_data):
# Validate each dict inside an input List[Dict]
all(
_validate_input_dictionary_contains_only_strings_and_lists_of_strings(x)
for x in input_data
)
predictions = self._predict(input_data)
return predictions
def _predict(self, data):
import transformers
# NB: the ordering of these conditional statements matters. TranslationPipeline and
# SummarizationPipeline both inherit from TextGenerationPipeline (they are subclasses)
# in which the return data structure from their __call__ implementation is modified.
if isinstance(self.pipeline, transformers.TranslationPipeline):
self._validate_str_or_list_str(data)
output_key = "translation_text"
elif isinstance(self.pipeline, transformers.SummarizationPipeline):
self._validate_str_or_list_str(data)
output_key = "summary_text"
elif isinstance(self.pipeline, transformers.Text2TextGenerationPipeline):
data = self._parse_text2text_input(data)
output_key = "generated_text"
elif isinstance(self.pipeline, transformers.TextGenerationPipeline):
self._validate_str_or_list_str(data)
output_key = "generated_text"
elif isinstance(self.pipeline, transformers.QuestionAnsweringPipeline):
data = self._parse_question_answer_input(data)
output_key = "answer"
elif isinstance(self.pipeline, transformers.FillMaskPipeline):
self._validate_str_or_list_str(data)
output_key = "token_str"
elif isinstance(self.pipeline, transformers.TextClassificationPipeline):
output_key = "label"
elif isinstance(self.pipeline, transformers.ZeroShotClassificationPipeline):
output_key = "labels"
data = self._parse_json_encoded_list(data, "candidate_labels")
elif isinstance(self.pipeline, transformers.TableQuestionAnsweringPipeline):
output_key = "answer"
data = self._parse_json_encoded_dict_payload_to_dict(data, "table")
elif isinstance(self.pipeline, transformers.TokenClassificationPipeline):
output_key = {"entity_group", "entity"}
elif isinstance(self.pipeline, transformers.ConversationalPipeline):
output_key = None
if not self._conversation:
self._conversation = transformers.Conversation()
self._conversation.add_user_input(data)
elif type(self.pipeline).__name__ in self._supported_custom_generator_types:
self._validate_str_or_list_str(data)
output_key = "generated_text"
else:
raise MlflowException(
f"The loaded pipeline type {type(self.pipeline).__name__} is "
"not enabled for pyfunc predict functionality.",
error_code=BAD_REQUEST,
)
# Optional input preservation for specific pipeline types
include_prompt = (
self.inference_config.pop("include_prompt", False) if self.inference_config else False
)
# Generate inference data with the pipeline object
if isinstance(self.pipeline, transformers.ConversationalPipeline):
conversation_output = self.pipeline(self._conversation)
return conversation_output.generated_responses[-1]
elif isinstance(data, dict):
if self.inference_config:
raw_output = self.pipeline(**data, **self.inference_config)
else:
raw_output = self.pipeline(**data)
else:
if self.inference_config:
raw_output = self.pipeline(data, **self.inference_config)
else:
raw_output = self.pipeline(data)
# Handle the pipeline outputs
if type(self.pipeline).__name__ in self._supported_custom_generator_types or isinstance(
self.pipeline, transformers.TextGenerationPipeline
):
output = self._strip_input_from_response_in_instruction_pipelines(
data, raw_output, output_key, self.flavor_config, include_prompt
)
elif isinstance(self.pipeline, transformers.FillMaskPipeline):
output = self._parse_list_of_multiple_dicts(raw_output, output_key)
elif isinstance(self.pipeline, transformers.ZeroShotClassificationPipeline):
interim_output = self._parse_lists_of_dict_to_list_of_str(raw_output, output_key)
output = self._parse_list_output_for_multiple_candidate_pipelines(interim_output)
elif isinstance(self.pipeline, transformers.TokenClassificationPipeline):
output = self._parse_tokenizer_output(raw_output, output_key)
else:
output = self._parse_lists_of_dict_to_list_of_str(raw_output, output_key)
return self._sanitize_output(output, data)
def _parse_raw_pipeline_input(self, data):
"""
Converts inputs to the expected types for specific Pipeline types.
Specific logic for individual pipeline types are called via their respective methods if
the input isn't a basic str or List[str] input type of Pipeline.
These parsers are required due to the conversion that occurs within schema validation to
a Pandas DataFrame encapsulation, a format which is unsupported for the `transformers`
library.
"""
import transformers
data = self._coerce_exploded_dict_to_single_dict(data)
data = self._parse_input_for_table_question_answering(data)
data = self._parse_conversation_input(data)
if (
isinstance(
self.pipeline,
(
transformers.FillMaskPipeline,
transformers.TextGenerationPipeline,
transformers.TranslationPipeline,
transformers.TextClassificationPipeline,
transformers.SummarizationPipeline,
transformers.TokenClassificationPipeline,
),
)
and isinstance(data, list)
and all(isinstance(entry, dict) for entry in data)
):
return [list(entry.values())[0] for entry in data]
else:
return data
def _parse_conversation_input(self, data):
import transformers
if not isinstance(self.pipeline, transformers.ConversationalPipeline):
return data
elif isinstance(data, str):
return data
elif isinstance(data, list) and all(isinstance(elem, dict) for elem in data):
return next(iter(data[0].values()))
elif isinstance(data, dict):
# The conversation pipeline can only accept a single string at a time
return next(iter(data.values()))
def _parse_input_for_table_question_answering(self, data):
import transformers
if not isinstance(self.pipeline, transformers.TableQuestionAnsweringPipeline):
return data
if "table" not in data:
raise MlflowException(
"The input dictionary must have the 'table' key.",
error_code=INVALID_PARAMETER_VALUE,
)
elif isinstance(data["table"], dict):
data["table"] = json.dumps(data["table"])
return data
else:
return data
def _coerce_exploded_dict_to_single_dict(self, data):
"""
Parses the result of Pandas DataFrame.to_dict(orient="records") from pyfunc
signature validation to coerce the output to the required format for a
Pipeline that requires a single dict with list elements such as
ZeroShotClassifierPipeline.
Example input:
[
{'sequences': 'My dog loves to eat spaghetti',
'candidate_labels': ['happy', 'sad'],
'hypothesis_template': 'This example talks about how the dog is {}'},
{'sequences': 'My dog hates going to the vet',
'candidate_labels': ['happy', 'sad'],
'hypothesis_template': 'This example talks about how the dog is {}'},
]
Output:
{'sequences': ['My dog loves to eat spaghetti',
'My dog hates going to the vet'],
'candidate_labels': ['happy', 'sad'],
'hypothesis_template': 'This example talks about how the dog is {}'}
"""
import transformers
if not isinstance(
self.pipeline,
(
transformers.ZeroShotClassificationPipeline,
transformers.TableQuestionAnsweringPipeline,
),
):
return data
elif isinstance(data, list) and all(isinstance(item, dict) for item in data):
collection = data.copy()
parsed = collection[0]
for coll in collection:
for key, value in coll.items():
if key not in parsed:
raise MlflowException(
"Unable to parse the input. The keys within each "
"dictionary of the parsed input are not consistent"
"among the dictionaries.",
error_code=INVALID_PARAMETER_VALUE,
)
if value != parsed[key]:
value_type = type(parsed[key])
if value_type == str:
parsed[key] = [parsed[key], value]
elif value_type == list:
parsed[key] = parsed[key].append(value)
else:
parsed[key] = value
return parsed
else:
return data
def _strip_input_from_response_in_instruction_pipelines(
self, input_data, output, output_key, flavor_config, include_prompt
):
"""
Parse the output from instruction pipelines to conform with other text generator
pipeline types and remove line feed characters and other confusing outputs
"""
replacements = {"\n\n": " "}
def extract_response_data(data_out):
if all(isinstance(x, dict) for x in data_out):
return [elem[output_key] for elem in data_out][0]
elif all(isinstance(x, list) for x in data_out):
return [elem[output_key] for coll in data_out for elem in coll]
else:
raise MlflowException(
"Unable to parse the pipeline output. Expected List[Dict[str,str]] or "
f"List[List[Dict[str,str]]] but got {type(data_out)} instead."
)
output = extract_response_data(output)
def trim_input(data_in, data_out):
# NB: the '\n\n' pattern is exclusive to specific InstructionalTextGenerationPipeline
# types that have been loaded as a plain TextGenerator. The structure of these
# pipelines will precisely repeat the input question immediately followed by 2 carriage
# return statements, followed by the start of the response to the prompt. We only
# want to left-trim these types of pipelines output values if the user hasn't disabled
# the removal action of the input prompt in the returned str or List[str]
if (
data_out.startswith(data_in + "\n\n")
and flavor_config[_INSTANCE_TYPE_KEY] in self._supported_custom_generator_types
):
# If the user has indicated to preserve the prompt input in the response, do not
# split the response output or trim any portions of the response string
if not include_prompt:
data_out = data_out[len(data_in) :].lstrip()
if data_out.startswith("A:"):
data_out = data_out[2:].lstrip()
for to_replace, replace in replacements.items():
data_out = data_out.replace(to_replace, replace)
return data_out
else:
return data_out
if isinstance(input_data, list) and isinstance(output, list):
return [trim_input(data_in, data_out) for data_in, data_out in zip(input_data, output)]
elif isinstance(input_data, str) and isinstance(output, str):
return trim_input(input_data, output)
else:
raise MlflowException(
"Unknown data structure after parsing output. Expected str or List[str]. "
f"Got {type(output)} instead."
)
def _sanitize_output(self, output, input_data):
# Some pipelines and their underlying models leave leading or trailing whitespace.
# This method removes that whitespace.
import transformers
if (
not isinstance(self.pipeline, transformers.TokenClassificationPipeline)
and isinstance(input_data, str)
and isinstance(output, list)
):
# Retrieve the first output for return types that are List[str] of only a single
# element.
output = output[0]
if isinstance(output, str):
return output.strip()
elif isinstance(output, list):
if all(isinstance(elem, str) for elem in output):
cleaned = [text.strip() for text in output]
# If the list has only a single string, return as string.
return cleaned if len(cleaned) > 1 else cleaned[0]
else:
return [self._sanitize_output(coll, input_data) for coll in output]
elif isinstance(output, dict) and all(
isinstance(key, str) and isinstance(value, str) for key, value in output.items()
):
return {k: v.strip() for k, v in output.items()}
else:
return output
def _parse_lists_of_dict_to_list_of_str(self, output_data, target_dict_key) -> List[str]:
"""
Parses the output results from select Pipeline types to extract specific values from a
target key.
Examples (with "a" as the `target_dict_key`):
Input: [{"a": "valid", "b": "invalid"}, {"a": "another valid", "c": invalid"}]
Output: ["valid", "another_valid"]
Input: [{"a": "valid", "b": [{"a": "another valid"}, {"b": "invalid"}]},
{"a": "valid 2", "b": [{"a": "another valid 2"}, {"c": "invalid"}]}]
Output: ["valid", "another valid", "valid 2", "another valid 2"]
"""
if isinstance(output_data, list):
output_coll = []
for output in output_data:
if isinstance(output, dict):
for key, value in output.items():
if key == target_dict_key:
output_coll.append(output[target_dict_key])
elif isinstance(value, list) and all(
isinstance(elem, dict) for elem in value
):
output_coll.append(
self._parse_lists_of_dict_to_list_of_str(value, target_dict_key)[0]
)
elif isinstance(output, list):
output_coll.append(
self._parse_lists_of_dict_to_list_of_str(output, target_dict_key)[0]
)
return output_coll
else:
return output_data[target_dict_key]
def _parse_tokenizer_output(self, output_data, target_set):
"""
Parses the tokenizer pipeline output.
Examples:
Input: [{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}]
Output: "PRON,NOUN"
Input: [[{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}],
[{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}]]
Output: ["PRON,NOUN", "PRON,NOUN"]
"""
# NB: We're collapsing the results here to a comma separated string for each inference
# input string. This is to simplify having to otherwise make extensive changes to
# ColSpec in order to support schema enforcement of List[List[str]]
if isinstance(output_data[0], list):
return [self._parse_tokenizer_output(coll, target_set) for coll in output_data]
else:
# NB: Since there are no attributes accessible from the pipeline object that determine
# what the characteristics of the return structure names are within the dictionaries,
# Determine which one is present in the output to extract the correct entries.
target = target_set.intersection(output_data[0].keys()).pop()
return ",".join([coll[target] for coll in output_data])
@staticmethod
def _parse_list_of_multiple_dicts(output_data, target_dict_key):
"""
Returns the first value of the `target_dict_key` that matches in the first dictionary in a
list of dictionaries.
"""
if isinstance(output_data[0], list):
return [collection[0][target_dict_key] for collection in output_data]
else:
return [output_data[0][target_dict_key]]
def _parse_list_output_for_multiple_candidate_pipelines(self, output_data):
# NB: This will not continue to parse nested lists. Pipelines do not output complex
# types that are greater than 2 levels deep so there is no need for more complex
# traversal for outputs.
if isinstance(output_data, list) and len(output_data) < 1:
raise MlflowException(
"The output of the pipeline contains no data.", error_code=BAD_REQUEST
)
if isinstance(output_data[0], list):
return [
self._parse_list_output_for_multiple_candidate_pipelines(x) for x in output_data
]
else:
return output_data[0]
def _parse_question_answer_input(self, data):
"""
Parses the single string input representation for a question answer pipeline into the
required dict format for a `question-answering` pipeline.
"""
if isinstance(data, list):
return [self._parse_question_answer_input(entry) for entry in data]
elif isinstance(data, dict):
expected_keys = {"question", "context"}
if not expected_keys.intersection(set(data.keys())) == expected_keys:
raise MlflowException(
f"Invalid keys were submitted. Keys must be exclusively {expected_keys}"
)
return data
else:
raise MlflowException(
"An invalid type has been supplied. Must be either List[Dict[str, str]] or "
f"Dict[str, str]. {type(data)} is not supported.",
error_code=INVALID_PARAMETER_VALUE,
)
def _parse_text2text_input(self, data):
"""
Parses the mixed input types that can be submitted into a text2text Pipeline.
Valid examples:
Input:
{"context": "abc", "answer": "def"}
Output:
"context: abc answer: def"
Input:
[{"context": "abc", "answer": "def"}, {"context": "ghi", "answer": "jkl"}]
Output:
["context: abc answer: def", "context: ghi answer: jkl"]
Input:
"abc"
Output:
"abc"
Input:
["abc", "def"]
Output:
["abc", "def"]
"""
if isinstance(data, dict) and all(isinstance(value, str) for value in data.values()):
if all(isinstance(key, str) for key in data) and "inputs" not in data:
# NB: Text2Text Pipelines require submission of text in a pseudo-string based dict
# formatting.
# As an example, for the input of:
# data = {"context": "The sky is blue", "answer": "blue"}
# This method will return the Pipeline-required format of:
# "context: The sky is blue. answer: blue"
return " ".join(f"{key}: {value}" for key, value in data.items())
else:
return list(data.values())
elif isinstance(data, list) and all(isinstance(value, dict) for value in data):
return [self._parse_text2text_input(entry) for entry in data]
elif isinstance(data, str):
return data
elif isinstance(data, list) and all(isinstance(value, str) for value in data):
return data
else:
raise MlflowException(
"An invalid type has been supplied. Please supply a Dict[str, str], str, "
"List[str], or a List[Dict[str, str]] for a Text2Text Pipeline.",
error_code=INVALID_PARAMETER_VALUE,
)
def _parse_json_encoded_list(self, data, key_to_unpack):
"""
Parses the complex input types for pipelines such as ZeroShotClassification in which
the required input type is Dict[str, Union[str, List[str]]] wherein the list
provided is encoded as JSON. This method unpacks that string to the required
elements.
"""
from json import JSONDecodeError
if isinstance(data, list):
return [self._parse_json_encoded_list(entry, key_to_unpack) for entry in data]
elif isinstance(data, dict):
if key_to_unpack not in data:
raise MlflowException(
"Invalid key in inference payload. The expected inference data key "
f"is: {key_to_unpack}",
error_code=INVALID_PARAMETER_VALUE,
)
if isinstance(data[key_to_unpack], str):
try:
return {
k: (json.loads(v) if k == key_to_unpack else v) for k, v in data.items()
}
except JSONDecodeError:
return data
elif isinstance(data[key_to_unpack], list):
return data
@staticmethod
def _parse_json_encoded_dict_payload_to_dict(data, key_to_unpack):
"""
Parses complex dict input types that have been json encoded. Pipelines like
TableQuestionAnswering require such input types.
"""
if isinstance(data, list):
return [
{
key: (
json.loads(value)
if key == key_to_unpack and isinstance(value, str)
else value
)
for key, value in entry.items()
}
for entry in data
]
else:
return {
key: (
json.loads(value) if key == key_to_unpack and isinstance(value, str) else value
)
for key, value in data.items()
}
@staticmethod
def _validate_str_or_list_str(data):
if not isinstance(data, (str, list)):
raise MlflowException(
f"The input data is of an incorrect type. {type(data)} is invalid. "
"Must be either string or List[str]",
error_code=INVALID_PARAMETER_VALUE,
)
elif isinstance(data, list) and not all(isinstance(entry, str) for entry in data):
raise MlflowException(
"If supplying a list, all values must be of string type.",
error_code=INVALID_PARAMETER_VALUE,
)