"""MLFlow module for HuggingFace/transformer support."""
import ast
import base64
import binascii
import contextlib
import functools
import json
import logging
import os
import pathlib
import re
import sys
from functools import lru_cache
from typing import Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from mlflow import pyfunc
from mlflow.environment_variables import (
MLFLOW_DEFAULT_PREDICTION_DEVICE,
MLFLOW_HUGGINGFACE_DEVICE_MAP_STRATEGY,
MLFLOW_HUGGINGFACE_DISABLE_ACCELERATE_FEATURES,
MLFLOW_HUGGINGFACE_MODEL_MAX_SHARD_SIZE,
MLFLOW_HUGGINGFACE_USE_DEVICE_MAP,
MLFLOW_HUGGINGFACE_USE_LOW_CPU_MEM_USAGE,
)
from mlflow.exceptions import MlflowException
from mlflow.models import (
Model,
ModelInputExample,
ModelSignature,
infer_pip_requirements,
infer_signature,
)
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.utils import _contains_params, _save_example
from mlflow.protos.databricks_pb2 import BAD_REQUEST, INVALID_PARAMETER_VALUE
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.types.schema import ColSpec, Schema, TensorSpec
from mlflow.types.utils import _validate_input_dictionary_contains_only_strings_and_lists_of_strings
from mlflow.utils.annotations import experimental
from mlflow.utils.autologging_utils import (
autologging_integration,
disable_discrete_autologging,
safe_patch,
)
from mlflow.utils.docstring_utils import (
LOG_MODEL_PARAM_DOCS,
docstring_version_compatibility_warning,
format_docstring,
)
from mlflow.utils.environment import (
_CONDA_ENV_FILE_NAME,
_CONSTRAINTS_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_find_duplicate_requirements,
_mlflow_conda_env,
_process_conda_env,
_process_pip_requirements,
_PythonEnv,
_validate_env_arguments,
)
from mlflow.utils.file_utils import get_total_file_size, write_to
from mlflow.utils.model_utils import (
_add_code_from_conf_to_system_path,
_download_artifact_from_uri,
_get_flavor_configuration,
_get_flavor_configuration_from_uri,
_validate_and_copy_code_paths,
_validate_and_prepare_target_save_path,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement
FLAVOR_NAME = "transformers"
_CARD_TEXT_FILE_NAME = "model_card.md"
_CARD_DATA_FILE_NAME = "model_card_data.yaml"
_COMPONENTS_BINARY_KEY = "components"
_FEATURE_EXTRACTOR_KEY = "feature_extractor"
_FEATURE_EXTRACTOR_TYPE_KEY = "feature_extractor_type"
_FRAMEWORK_KEY = "framework"
_IMAGE_PROCESSOR_KEY = "image_processor"
_IMAGE_PROCESSOR_TYPE_KEY = "image_processor_type"
_INFERENCE_CONFIG_BINARY_KEY = "inference_config.txt"
_INSTANCE_TYPE_KEY = "instance_type"
_MODEL_KEY = "model"
_MODEL_BINARY_KEY = "model_binary"
_MODEL_BINARY_FILE_NAME = "model"
_MODEL_PATH_OR_NAME_KEY = "source_model_name"
_PIPELINE_MODEL_TYPE_KEY = "pipeline_model_type"
_PROCESSOR_KEY = "processor"
_PROCESSOR_TYPE_KEY = "processor_type"
_SUPPORTED_RETURN_TYPES = {"pipeline", "components"}
# The default device id for CPU is -1 and GPU IDs are ordinal starting at 0, as documented here:
# https://huggingface.co/transformers/v4.7.0/main_classes/pipelines.html
_TRANSFORMERS_DEFAULT_CPU_DEVICE_ID = -1
_TRANSFORMERS_DEFAULT_GPU_DEVICE_ID = 0
_TASK_KEY = "task"
_TOKENIZER_KEY = "tokenizer"
_TOKENIZER_TYPE_KEY = "tokenizer_type"
_TORCH_DTYPE_KEY = "torch_dtype"
_METADATA_PIPELINE_SCALAR_CONFIG_KEYS = {_FRAMEWORK_KEY}
_SUPPORTED_SAVE_KEYS = {_MODEL_KEY, _TOKENIZER_KEY, _FEATURE_EXTRACTOR_KEY, _IMAGE_PROCESSOR_KEY}
_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":
packages = ["torch", "torchvision"]
try:
import accelerate # noqa: F401
packages.append("accelerate")
except ImportError:
pass
return packages
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 FlaxPreTrainedModel, PreTrainedModel, TFPreTrainedModel
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: {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: Optional[Dict[str, Any]] = None,
model_config: Optional[Dict[str, Any]] = None,
**kwargs, # pylint: disable=unused-argument
) -> 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:
.. Warning:: Deprecated. `inference_config` is deprecated in favor of `model_config`.
:param model_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
model_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, **model_config)
mlflow.transformers.save_model(
transformers_model=sentence_pipeline,
path="/path/for/model",
task=task,
model_config=model_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 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: {{ input_example }}
: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
# Verify that the model has not been loaded to distributed memory
# NB: transformers does not correctly save a model whose weights have been loaded
# using accelerate iff the model weights have been loaded using a device_map that is
# heterogeneous. There is a distinct possibility for a partial write to occur, causing an
# invalid state of the model's weights in this scenario. Hence, we raise.
if _is_model_distributed_in_memory(built_pipeline.model):
raise MlflowException(
"The model that is attempting to be saved has been loaded into memory "
"with an incompatible configuration. If you are using the accelerate "
"library to load your model, please ensure that it is saved only after "
"loading with the default device mapping. Do not specify `device_map` "
"and please try again."
)
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 model object
built_pipeline.model.save_pretrained(
save_directory=path.joinpath(_MODEL_BINARY_FILE_NAME),
max_shard_size=MLFLOW_HUGGINGFACE_MODEL_MAX_SHARD_SIZE.get(),
)
if model_config and inference_config:
raise MlflowException(
"Using both `model_config` and `inference_config` is not allowed. Use `model_config` "
"to indicate any model configuration you need to use for inference."
)
# 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
_write_card_data(card_data, path)
model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_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 mlflow_model.signature is None:
mlflow_model.signature = _get_default_pipeline_signature(
built_pipeline, input_example, model_config or inference_config
)
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_config=model_config,
**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,
)
if size := get_total_file_size(path):
mlflow_model.model_size_bytes = size
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)
if duplicates := _find_duplicate_requirements(pip_requirements):
_logger.warning(
"Duplicate packages are present within the pip requirements. Duplicate packages: "
f"{duplicates}. Please manually specify the requirements by using the "
"`pip_requirements` argument in order to prevent unexpected installation "
"issues for this model."
)
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: Optional[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: Optional[Dict[str, Any]] = None,
model_config: Optional[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:
.. Warning:: Deprecated. `inference_config` is deprecated in favor of `model_config`.
:param model_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
model_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, **model_config)
with mlflow.start_run():
mlflow.transformers.log_model(
transformers_model=sentence_pipeline,
artifact_path="my_sentence_generator",
task=task,
model_config=model_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 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 }}
: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=sys.modules[__name__], # Get the current module.
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,
model_config=model_config,
**kwargs,
)
[docs]@experimental
@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME)
def load_model(
model_uri: str, dst_path: Optional[str] = None, return_type="pipeline", device=None, **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 device: The device on which to load the model. Default is None. Use 0 to
load to the default GPU.
: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, device, **kwargs)
def _is_model_distributed_in_memory(transformers_model):
"""Check if the model is distributed across multiple devices in memory."""
# Check if the model attribute exists. If not, accelerate was not used and the model can
# be safely saved
if not hasattr(transformers_model, "hf_device_map"):
return False
# If the device map has more than one unique value entry, then the weights are not within
# a contiguous memory system (VRAM, SYS, or DISK) and thus cannot be safely saved.
return len(set(transformers_model.hf_device_map.values())) > 1
# This function attempts to determine if a GPU is available for the PyTorch and TensorFlow libraries
[docs]def is_gpu_available():
# try pytorch and if it fails, try tf
is_gpu = None
try:
import torch
is_gpu = torch.cuda.is_available()
except ImportError:
pass
if is_gpu is None:
try:
import tensorflow as tf
is_gpu = tf.test.is_gpu_available()
except ImportError:
pass
if is_gpu is None:
is_gpu = False
return is_gpu
def _try_load_model_with_device(model_instance, model_path, device, conf):
load_model_conf = {}
# Assume if torch_dtype was specified in the conf, then it must be with a
# pipeline for which it's compatible.
if _TORCH_DTYPE_KEY in conf:
load_model_conf[_TORCH_DTYPE_KEY] = conf[_TORCH_DTYPE_KEY]
try:
load_model_conf["device"] = device
model = model_instance.from_pretrained(model_path, **load_model_conf)
except (ValueError, TypeError, NotImplementedError):
_logger.warning("Could not specify device parameter for this pipeline type")
load_model_conf.pop("device", None)
model = model_instance.from_pretrained(model_path, **load_model_conf)
return model
def _load_model(path: str, flavor_config, return_type: str, device=None, **kwargs):
"""
Loads components from a locally serialized ``Pipeline`` object.
"""
import transformers
model_instance = getattr(transformers, flavor_config[_PIPELINE_MODEL_TYPE_KEY])
local_path = pathlib.Path(path)
# NB: Path resolution for models that were saved prior to 2.4.1 release when the pathing for
# the saved pipeline or component artifacts was handled by duplicate entries for components
# (artifacts/pipeline/* and artifacts/components/*) and pipelines were saved via the
# "artifacts/pipeline/*" path. In order to load the older formats after the change, the
# presence of the new path key is checked.
model_path = local_path.joinpath(flavor_config.get(_MODEL_BINARY_KEY, "pipeline"))
conf = {
"task": flavor_config[_TASK_KEY],
}
if device is None:
if MLFLOW_DEFAULT_PREDICTION_DEVICE.get():
try:
device = int(MLFLOW_DEFAULT_PREDICTION_DEVICE.get())
except ValueError:
device = _TRANSFORMERS_DEFAULT_CPU_DEVICE_ID
elif is_gpu_available():
device = _TRANSFORMERS_DEFAULT_GPU_DEVICE_ID
# Note that we don't set the device in the conf yet because device is
# incompatible with device_map.
accelerate_model_conf = {}
if MLFLOW_HUGGINGFACE_USE_DEVICE_MAP.get():
device_map_strategy = MLFLOW_HUGGINGFACE_DEVICE_MAP_STRATEGY.get()
conf["device_map"] = device_map_strategy
accelerate_model_conf["device_map"] = device_map_strategy
# Cannot use device with device_map
device = None
if device is not None:
conf["device"] = device
accelerate_model_conf["device"] = device
if _TORCH_DTYPE_KEY in flavor_config or _TORCH_DTYPE_KEY in kwargs:
if _TORCH_DTYPE_KEY in kwargs:
dtype_val = kwargs[_TORCH_DTYPE_KEY]
else:
dtype_val = _deserialize_torch_dtype_if_exists(flavor_config)
conf[_TORCH_DTYPE_KEY] = dtype_val
accelerate_model_conf[_TORCH_DTYPE_KEY] = dtype_val
accelerate_model_conf["low_cpu_mem_usage"] = MLFLOW_HUGGINGFACE_USE_LOW_CPU_MEM_USAGE.get()
if not MLFLOW_HUGGINGFACE_DISABLE_ACCELERATE_FEATURES.get():
try:
model = model_instance.from_pretrained(model_path, **accelerate_model_conf)
except (ValueError, TypeError, NotImplementedError, ImportError):
# NB: ImportError is caught here in the event that `accelerate` is not installed
# on the system, which will raise if `low_cpu_mem_usage` is set or the argument
# `device_map` is set and accelerate is not installed.
model = _try_load_model_with_device(model_instance, model_path, device, conf)
else:
model = _try_load_model_with_device(model_instance, model_path, device, conf)
conf["model"] = model
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)
for key in _METADATA_PIPELINE_SCALAR_CONFIG_KEYS:
if key in flavor_config:
conf[key] = flavor_config[key]
if return_type == "pipeline":
conf.update(**kwargs)
return transformers.pipeline(**conf)
elif return_type == "components":
return conf
@lru_cache
def _torch_dype_mapping():
"""
Memoized torch data type mapping from the torch primary datatypes for use in deserializing the
saved pipeline parameter `torch_dtype`
"""
try:
import torch
return {
str(dtype): dtype
for name, dtype in torch.__dict__.items()
if isinstance(dtype, torch.dtype)
}
except ImportError as e:
raise MlflowException(
"Unable to determine if the value supplied by the argument "
"torch_dtype is valid since torch is not installed.",
error_code=INVALID_PARAMETER_VALUE,
) from e
def _deserialize_torch_dtype_if_exists(flavor_config):
"""
Convert the string-encoded `torch_dtype` pipeline argument back to the correct `torch.dtype`
instance value for applying to a loaded pipeline instance.
"""
return _torch_dype_mapping()[flavor_config["torch_dtype"]]
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 _write_card_data(card_data, path):
"""
Writes the card data, if specified or available, to the provided path in two separate files
"""
if card_data:
try:
path.joinpath(_CARD_TEXT_FILE_NAME).write_text(card_data.text, encoding="utf-8")
except UnicodeError as e:
_logger.warning(f"Unable to save the model card text due to: {e}")
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, encoding="utf-8"
)
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=None,
):
"""
Saves non-model pipeline components.
"""
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:
_logger.warning(
"Indicating `inference_config` is deprecated and will be removed in a future version "
"of MLflow. Use `model_config` instead."
)
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(
pipeline,
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(pipeline),
_MODEL_PATH_OR_NAME_KEY: _get_base_model_architecture(pipeline),
_PIPELINE_MODEL_TYPE_KEY: _get_instance_type(pipeline.model),
}
# Extract and add to the configuration the scalar serializable arguments for pipeline args
for arg_key in _METADATA_PIPELINE_SCALAR_CONFIG_KEYS:
if entry := _get_scalar_argument_from_pipeline(pipeline, arg_key):
flavor_configuration[arg_key] = entry
# Extract a serialized representation of torch_dtype if provided
if torch_dtype := _extract_torch_dtype_if_set(pipeline):
flavor_configuration[_TORCH_DTYPE_KEY] = torch_dtype
return flavor_configuration
def _get_scalar_argument_from_pipeline(pipeline, arg_key):
"""
Retrieve provided pipeline arguments for the purposes of instantiating a pipeline object upon
loading.
"""
return getattr(pipeline, arg_key, None)
def _extract_torch_dtype_if_set(pipeline):
"""
Extract the torch datatype argument if set and return as a string encoded value.
"""
if torch_dtype := getattr(pipeline, _TORCH_DTYPE_KEY, None):
return str(torch_dtype)
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 FlaxPreTrainedModel, PreTrainedModel, TFPreTrainedModel
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.
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 due to the complex input types that, in
order to support, will require significant modifications (breaking changes) to the pyfunc
contract.
"""
import transformers
exclusion_model_types = {
"GraphormerPreTrainedModel",
"InformerPreTrainedModel",
"TimeSeriesTransformerPreTrainedModel",
"DecisionTransformerPreTrainedModel",
}
# NB: When pyfunc functionality is added for these pipeline types over time, remove the
# entries from the following list.
exclusion_pipeline_types = [
"DocumentQuestionAnsweringPipeline",
"ImageToTextPipeline",
"VisualQuestionAnsweringPipeline",
"ImageClassificationPipeline",
"ImageSegmentationPipeline",
"DepthEstimationPipeline",
"ObjectDetectionPipeline",
"VideoClassificationPipeline",
"ZeroShotImageClassificationPipeline",
"ZeroShotObjectDetectionPipeline",
"ZeroShotAudioClassificationPipeline",
]
impermissible_attrs = {"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
if type(pipeline).__name__ in exclusion_pipeline_types:
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
input_data = input_example[0] if isinstance(input_example, tuple) else input_example
if (
isinstance(pipeline, transformers.ZeroShotClassificationPipeline)
and isinstance(input_data, dict)
and isinstance(input_data["candidate_labels"], list)
):
input_data["candidate_labels"] = json.dumps(input_data["candidate_labels"])
return input_data if not isinstance(input_example, tuple) else (input_data, input_example[1])
def _get_default_pipeline_signature(pipeline, example=None, model_config=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:
params = None
if _contains_params(example):
example, params = example
prediction = generate_signature_output(pipeline, example, model_config, params)
return infer_signature(example, prediction, params)
except Exception as e:
_logger.warning(
"Attempted to generate a signature for the saved model or pipeline "
f"but encountered an error: {e}"
)
raise
else:
if isinstance(
pipeline,
(
transformers.TokenClassificationPipeline,
transformers.ConversationalPipeline,
transformers.TranslationPipeline,
transformers.FillMaskPipeline,
transformers.TextGenerationPipeline,
transformers.Text2TextGenerationPipeline,
),
):
return ModelSignature(
inputs=Schema([ColSpec("string")]), outputs=Schema([ColSpec("string")])
)
elif isinstance(pipeline, transformers.TextClassificationPipeline):
return ModelSignature(
inputs=Schema([ColSpec("string")]),
outputs=Schema([ColSpec("string", name="label"), ColSpec("double", name="score")]),
)
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", name="sequence"),
ColSpec("string", name="labels"),
ColSpec("double", name="scores"),
]
),
)
elif isinstance(pipeline, transformers.AutomaticSpeechRecognitionPipeline):
return ModelSignature(
inputs=Schema([ColSpec("binary")]),
outputs=Schema([ColSpec("string")]),
)
elif isinstance(pipeline, transformers.AudioClassificationPipeline):
return ModelSignature(
inputs=Schema([ColSpec("binary")]),
outputs=Schema([ColSpec("double", name="score"), ColSpec("string", name="label")]),
)
elif isinstance(
pipeline,
(
transformers.TableQuestionAnsweringPipeline,
transformers.QuestionAnsweringPipeline,
),
):
column_1 = None
column_2 = None
if isinstance(pipeline, transformers.TableQuestionAnsweringPipeline):
column_1 = "query"
column_2 = "table"
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")]),
)
elif isinstance(pipeline, transformers.FeatureExtractionPipeline):
return ModelSignature(
inputs=Schema([ColSpec("string")]),
outputs=Schema([TensorSpec(np.dtype("float64"), [-1], "double")]),
)
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 (
FeatureExtractionMixin,
FlaxPreTrainedModel,
ImageFeatureExtractionMixin,
ImageProcessingMixin,
PreTrainedModel,
PreTrainedTokenizerBase,
ProcessorMixin,
TFPreTrainedModel,
)
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, # pylint: disable=unused-argument
):
cls._validate_submitted_types(
model, tokenizer, feature_extractor, image_processor, processor
)
return _TransformersModel(model, tokenizer, feature_extractor, image_processor, processor)
def _get_model_config(local_path, pyfunc_config):
"""
Load the model configuration if it was provided for use in the `_TransformersWrapper` pyfunc
Model wrapper.
"""
config_path = local_path.joinpath("inference_config.txt")
if config_path.exists():
_logger.warning(
"Inference config stored in file ``inference_config.txt`` is deprecated. New logged "
"models will store the model configuration in the ``pyfunc`` flavor configuration."
)
return json.loads(config_path.read_text())
else:
return pyfunc_config or {}
def _load_pyfunc(path, model_config: Optional[Dict[str, Any]] = None):
"""
Loads the model as pyfunc model
"""
local_path = pathlib.Path(path)
flavor_configuration = _get_flavor_configuration(local_path, FLAVOR_NAME)
model_config = _get_model_config(local_path.joinpath(_COMPONENTS_BINARY_KEY), model_config)
return _TransformersWrapper(
_load_model(str(local_path), flavor_configuration, "pipeline"),
flavor_configuration,
model_config,
)
[docs]@experimental
def generate_signature_output(pipeline, data, model_config=None, params=None):
"""
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
:param model_config: Any additional model configuration, provided as kwargs, to inform
the format of the output type from a pipeline inference call.
:param params: A dictionary of additional parameters to pass to the pipeline for inference.
: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=pipeline, model_config=model_config).predict(
data, params=params
)
class _TransformersWrapper:
def __init__(self, pipeline, flavor_config=None, model_config=None):
self.pipeline = pipeline
self.flavor_config = flavor_config
self.model_config = model_config or {}
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 _override_model_config(self, params):
if params:
_logger.warning(
"params provided to the `predict` method will override the inference "
"configuration saved with the model. If the params provided are not "
"valid for the pipeline, MlflowException will be raised."
)
# Override the inference configuration with any additional kwargs provided by the user.
self.model_config.update(params)
def _validate_model_config_and_return_output(self, data):
import transformers
try:
if isinstance(data, dict):
return self.pipeline(**data, **self.model_config)
return self.pipeline(data, **self.model_config)
except ValueError as e:
if "The following `model_kwargs` are not used by the model" in str(e):
raise MlflowException.invalid_parameter_value(
"The params provided to the `predict` method are not valid "
f"for pipeline {type(self.pipeline).__name__}.",
) from e
if isinstance(
self.pipeline,
(
transformers.AutomaticSpeechRecognitionPipeline,
transformers.AudioClassificationPipeline,
),
) and (
# transformers <= 4.33.3
"Malformed soundfile" in str(e)
# transformers > 4.33.3
or "Soundfile is either not in the correct format or is malformed" in str(e)
):
raise MlflowException.invalid_parameter_value(
"Failed to process the input audio data. Either the audio file is "
"corrupted or a uri was passed in without overriding the default model "
"signature. If submitting a string uri, please ensure that the model has "
"been saved with a signature that defines a string input type.",
) from e
raise
def predict(self, data, params: Optional[Dict[str, Any]] = None):
"""
:param data: Model input data.
:param params: Additional parameters to pass to the model for inference.
.. Note:: Experimental: This parameter may change or be removed in a future
release without warning.
:return: Model predictions.
"""
self._override_model_config(params)
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, bytes, np.ndarray)):
input_data = data
else:
raise MlflowException(
"Input data must be either a pandas.DataFrame, a string, bytes, 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
)
return self._predict(input_data)
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.FeatureExtractionPipeline):
output_key = None
data = self._parse_feature_extraction_input(data)
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"
elif isinstance(self.pipeline, transformers.AutomaticSpeechRecognitionPipeline):
if self.model_config.get("return_timestamps", None) in ["word", "char"]:
output_key = None
else:
output_key = "text"
data = self._convert_audio_input(data)
elif isinstance(self.pipeline, transformers.AudioClassificationPipeline):
data = self._convert_audio_input(data)
output_key = None
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. This is True (include raw
# formatting output), but if `include_prompt` is set to False in the `model_config`
# option during model saving, excess newline characters and the fed-in prompt will be
# trimmed out from the start of the response.
include_prompt = self.model_config.pop("include_prompt", True)
# Optional stripping out of `\n` for specific generator pipelines.
collapse_whitespace = self.model_config.pop("collapse_whitespace", False)
data = self._convert_cast_lists_from_np_back_to_list(data)
# 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]
else:
raw_output = self._validate_model_config_and_return_output(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,
collapse_whitespace,
)
elif isinstance(self.pipeline, transformers.FeatureExtractionPipeline):
return self._parse_feature_extraction_output(raw_output)
elif isinstance(self.pipeline, transformers.FillMaskPipeline):
output = self._parse_list_of_multiple_dicts(raw_output, output_key)
elif isinstance(self.pipeline, transformers.ZeroShotClassificationPipeline):
return self._flatten_zero_shot_text_classifier_output_to_df(raw_output)
elif isinstance(self.pipeline, transformers.TokenClassificationPipeline):
output = self._parse_tokenizer_output(raw_output, output_key)
elif isinstance(
self.pipeline, transformers.AutomaticSpeechRecognitionPipeline
) and self.model_config.get("return_timestamps", None) in ["word", "char"]:
output = json.dumps(raw_output)
elif isinstance(
self.pipeline,
(transformers.AudioClassificationPipeline, transformers.TextClassificationPipeline),
):
return pd.DataFrame(raw_output)
else:
output = self._parse_lists_of_dict_to_list_of_str(raw_output, output_key)
sanitized = self._sanitize_output(output, data)
return self._wrap_strings_as_list_if_scalar(sanitized)
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.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]
elif isinstance(self.pipeline, transformers.TextClassificationPipeline):
return self._validate_text_classification_input(data)
else:
return data
@staticmethod
def _validate_text_classification_input(data):
"""
Perform input type validation for TextClassification pipelines and casting of data
that is manipulated internally by the MLflow model server back to a structure that
can be used for pipeline inference.
To illustrate the input and outputs of this function, for the following inputs to
the pyfunc.predict() call for this pipeline type:
"text to classify"
["text to classify", "other text to classify"]
{"text": "text to classify", "text_pair": "pair text"}
[{"text": "text", "text_pair": "pair"}, {"text": "t", "text_pair": "tp" }]
Pyfunc processing will convert these to the following structures:
[{0: "text to classify"}]
[{0: "text to classify"}, {0: "other text to classify"}]
[{"text": "text to classify", "text_pair": "pair text"}]
[{"text": "text", "text_pair": "pair"}, {"text": "t", "text_pair": "tp" }]
The purpose of this function is to convert them into the correct format for input
to the pipeline (wrapping as a list has no bearing on the correctness of the
inferred classifications):
["text to classify"]
["text to classify", "other text to classify"]
[{"text": "text to classify", "text_pair": "pair text"}]
[{"text": "text", "text_pair": "pair"}, {"text": "t", "text_pair": "tp" }]
Additionally, for dict input types (the 'text' & 'text_pair' input example), the dict
input will be JSON stringified within MLflow model serving. In order to reconvert this
structure back into the appropriate type, we use ast.literal_eval() to convert back
to a dict. We avoid using JSON.loads() due to pandas DataFrame conversions that invert
single and double quotes with escape sequences that are not consistent if the string
contains escaped quotes.
"""
def _check_keys(payload):
"""Check if a dictionary contains only allowable keys."""
allowable_str_keys = {"text", "text_pair"}
if set(payload) - allowable_str_keys and not all(
isinstance(key, int) for key in payload.keys()
):
raise MlflowException(
"Text Classification pipelines may only define dictionary inputs with keys "
f"defined as {allowable_str_keys}"
)
if isinstance(data, str):
return data
elif isinstance(data, dict):
_check_keys(data)
return data
elif isinstance(data, list):
if all(isinstance(item, str) for item in data):
return data
elif all(isinstance(item, dict) for item in data):
for payload in data:
_check_keys(payload)
if list(data[0].keys())[0] == 0:
data = [item[0] for item in data]
try:
# NB: To support MLflow serving signature validation, the value within dict
# inputs is JSON encoded. In order for the proper data structure input support
# for a {"text": "a", "text_pair": "b"} (or the list of such a structure) as
# an input, we have to convert the string encoded dict back to a dict.
# Due to how unescaped characters (such as "'") are encoded, using an explicit
# json.loads() attempted cast can result in invalid input data to the pipeline.
# ast.literal_eval() shows correct conversion, as validated in unit tests.
return [ast.literal_eval(s) for s in data]
except (ValueError, SyntaxError):
return data
else:
raise MlflowException(
"An unsupported data type has been passed for Text Classification inference. "
"Only str, list of str, dict, and list of dict are supported."
)
else:
raise MlflowException(
"An unsupported data type has been passed for Text Classification inference. "
"Only str, list of str, dict, and list of dict are supported."
)
def _parse_conversation_input(self, data):
import transformers
if not isinstance(self.pipeline, transformers.ConversationalPipeline) or 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
TableQuestionAnsweringPipeline.
Example input:
[
{"answer": "We should order more pizzas to meet the demand."},
{"answer": "The venue size should be updated to handle the number of guests."},
]
Output:
[
"We should order more pizzas to meet the demand.",
"The venue size should be updated to handle the number of guests.",
]
"""
import transformers
if not isinstance(
self.pipeline,
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:
if all(len(entry) == 1 for entry in value):
# This conversion is required solely for model serving.
# In the parsing logic that occurs internally, strings that
# contain single quotes `'` result in casting to a List[char]
# instead of a str type. Attempting to append a List[char]
# to a List[str] as would happen in the `else` block here
# results in the entire List being overwritten as `None` without
# an Exception being raised. By checking for single value entries
# and subsequently converting to list and extracting the first
# element reconstructs the original input string.
parsed[key].append([str(value)][0])
else:
parsed[key] = parsed[key].append(value)
else:
parsed[key] = value
return parsed
else:
return data
def _flatten_zero_shot_text_classifier_output_to_df(self, data):
"""
Converts the output of sequences, labels, and scores to a Pandas DataFrame output.
Example input:
[{'sequence': 'My dog loves to eat spaghetti',
'labels': ['happy', 'sad'],
'scores': [0.9896970987319946, 0.010302911512553692]},
{'sequence': 'My dog hates going to the vet',
'labels': ['sad', 'happy'],
'scores': [0.957074761390686, 0.042925238609313965]}]
Output:
pd.DataFrame in a fully normalized (flattened) format with each sequence, label, and score
having a row entry.
For example, here is the DataFrame output:
sequence labels scores
0 My dog loves to eat spaghetti happy 0.989697
1 My dog loves to eat spaghetti sad 0.010303
2 My dog hates going to the vet sad 0.957075
3 My dog hates going to the vet happy 0.042925
"""
if isinstance(data, list) and not all(isinstance(item, dict) for item in data):
raise MlflowException(
"Encountered an unknown return type from the pipeline type "
f"{type(self.pipeline).__name__}. Expecting a List[Dict]",
error_code=BAD_REQUEST,
)
if isinstance(data, dict):
data = [data]
flattened_data = []
for entry in data:
for label, score in zip(entry["labels"], entry["scores"]):
flattened_data.append(
{"sequence": entry["sequence"], "labels": label, "scores": score}
)
return pd.DataFrame(flattened_data)
def _strip_input_from_response_in_instruction_pipelines(
self,
input_data,
output,
output_key,
flavor_config,
include_prompt=True,
collapse_whitespace=False,
):
"""
Parse the output from instruction pipelines to conform with other text generator
pipeline types and remove line feed characters and other confusing outputs
"""
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 has indicated
# the removal action of the input prompt in the returned str or List[str] by applying
# the optional model_config entry of `{"include_prompt": False}`.
# By default, the prompt is included in the response.
# Stripping out additional carriage returns (\n) is another additional optional flag
# that can be set for these generator pipelines. It is off by default (False).
if (
not include_prompt
and flavor_config[_INSTANCE_TYPE_KEY] in self._supported_custom_generator_types
and data_out.startswith(data_in + "\n\n")
):
# If the user has indicated to not preserve the prompt input in the response,
# split the response output and trim the input prompt from the response.
data_out = data_out[len(data_in) :].lstrip()
if data_out.startswith("A:"):
data_out = data_out[2:].lstrip()
# If the user has indicated to remove newlines and extra spaces from the generated
# text, replace them with a single space.
if collapse_whitespace:
data_out = re.sub(r"\s+", " ", data_out).strip()
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
@staticmethod
def _wrap_strings_as_list_if_scalar(output_data):
"""
Wraps single string outputs in a list to support batch processing logic in serving.
Scalar values are not supported for processing in batch logic as they cannot be coerced
to DataFrame representations.
"""
if isinstance(output_data, str):
return [output_data]
else:
return output_data
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.extend(
self._parse_lists_of_dict_to_list_of_str(value, target_dict_key)
)
elif isinstance(output, list):
output_coll.extend(
self._parse_lists_of_dict_to_list_of_str(output, target_dict_key)
)
return output_coll
elif target_dict_key:
return output_data[target_dict_key]
else:
return output_data
@staticmethod
def _parse_feature_extraction_input(input_data):
if isinstance(input_data, list) and isinstance(input_data[0], dict):
return [list(data.values())[0] for data in input_data]
else:
return input_data
@staticmethod
def _parse_feature_extraction_output(output_data):
"""
Parse the return type from a FeatureExtractionPipeline output. The mixed types for
input are present depending on how the pyfunc is instantiated. For model serving usage,
the returned type from MLServer will be a numpy.ndarray type, otherwise, the return
within a manually executed pyfunc (i.e., for udf usage), the return will be a collection
of nested lists.
Examples:
Input: [[[0.11, 0.98, 0.76]]] or np.array([0.11, 0.98, 0.76])
Output: np.array([0.11, 0.98, 0.76])
Input: [[[[0.1, 0.2], [0.3, 0.4]]]] or
np.array([np.array([0.1, 0.2]), np.array([0.3, 0.4])])
Output: np.array([np.array([0.1, 0.2]), np.array([0.3, 0.4])])
"""
if isinstance(output_data, np.ndarray):
return output_data
else:
return np.array(output_data[0][0])
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.
"""
def fetch_target_key_value(data, key):
if isinstance(data[0], dict):
return data[0][key]
return [item[0][key] for item in data]
if isinstance(output_data[0], list):
return [
fetch_target_key_value(collection, 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) or (
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.
"""
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 json.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
]
elif isinstance(data, dict):
# This is to handle serving use cases as the DataFrame encapsulation converts
# collections within rows to np.array type. In order to process this data through
# the transformers.Pipeline API, we need to cast these arrays back to lists
# and replace the single quotes with double quotes after extracting the
# json-encoded `table` (a pandas DF) in order to convert it to a dict that
# the TableQuestionAnsweringPipeline can accept and cast to a Pandas DataFrame.
#
# An example casting that occurs for this case when input to model serving is the
# conversion of a user input of:
# '{"inputs": {"query": "What is the longest distance?",
# "table": {"Distance": ["1000", "10", "1"]}}}'
# is converted to:
# [{'query': array('What is the longest distance?', dtype='<U29'),
# 'table': array('{\'Distance\': [\'1000\', \'10\', \'1\']}', dtype='U<204')}]
# which is an invalid input to the pipeline.
# this method converts the input to:
# {'query': 'What is the longest distance?',
# 'table': {'Distance': ['1000', '10', '1']}}
# which is a valid input to the TableQuestionAnsweringPipeline.
output = {}
for key, value in data.items():
if key == key_to_unpack:
if isinstance(value, np.ndarray):
output[key] = ast.literal_eval(value.item())
else:
output[key] = ast.literal_eval(value)
else:
if isinstance(value, np.ndarray):
# This cast to np.ndarray occurs when more than one question is asked.
output[key] = value.item()
else:
# Otherwise, the entry does not need casting from a np.ndarray type to
# list as it is already a scalar string.
output[key] = value
return output
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,
)
@staticmethod
def _convert_cast_lists_from_np_back_to_list(data):
"""
This handles the casting of dicts within lists from Pandas DF conversion within model
serving back into the required Dict[str, List[str]] if this type matching occurs.
Otherwise, it's a noop.
"""
if not isinstance(data, list):
# NB: applying a short-circuit return here to not incur runtime overhead with
# type validation if the input is not a list
return data
elif not all(isinstance(value, dict) for value in data):
return data
else:
parsed_data = []
for entry in data:
if all(isinstance(value, np.ndarray) for value in entry.values()):
parsed_data.append({key: value.tolist() for key, value in entry.items()})
else:
parsed_data.append(entry)
return parsed_data
def _convert_audio_input(self, data):
"""
Conversion utility for decoding the base64 encoded bytes data of a raw soundfile when
parsed through model serving, if applicable. Direct usage of the pyfunc implementation
outside of model serving will treat this utility as a noop.
For reference, the expected encoding for input to Model Serving will be:
import requests
import base64
response = requests.get("https://www.my.sound/a/sound/file.wav")
encoded_audio = base64.b64encode(response.content).decode("ascii")
inference_data = json.dumps({"inputs": [encoded_audio]})
or
inference_df = pd.DataFrame(
pd.Series([encoded_audio], name="audio_file")
)
split_dict = {"dataframe_split": inference_df.to_dict(orient="split")}
split_json = json.dumps(split_dict)
or
records_dict = {"dataframe_records": inference_df.to_dict(orient="records")}
records_json = json.dumps(records_dict)
This utility will convert this JSON encoded, base64 encoded text back into bytes for
input into the AutomaticSpeechRecognitionPipeline for inference.
"""
def is_base64(s):
try:
return base64.b64encode(base64.b64decode(s)) == s
except binascii.Error:
return False
def decode_audio(encoded):
if isinstance(encoded, str):
# This is to support blob style passing of uri locations to process audio files
# on disk or object store. Note that if a uri is passed, a signature *must be*
# provided for serving to function as the default signature uses bytes.
return encoded
elif isinstance(encoded, bytes):
# For input types 'dataframe_split' and 'dataframe_records', the encoding
# conversion to bytes is handled.
if not is_base64(encoded):
return encoded
else:
# For input type 'inputs', explicit decoding of the b64encoded audio is needed.
return base64.b64decode(encoded)
else:
try:
return base64.b64decode(encoded)
except binascii.Error as e:
raise MlflowException(
"The encoded soundfile that was passed has not been properly base64 "
"encoded. Please ensure that the raw sound bytes have been processed with "
"`base64.b64encode(<audio data bytes>).decode('ascii')`"
) from e
# The example input data that is processed by this logic is from the pd.DataFrame
# conversion that happens within serving wherein the bytes input data is cast to
# a pd.DataFrame(pd.Series([raw_bytes])) and then cast to JSON serializable data in the
# format:
# {[0]: [{[0]: <audio data>}]}
# In the inputs format, due to the modification of how types are not enforced, the
# logic that is present in processing `records` and `split` format orientation when casting
# back to dictionary does not do the automatic decoding of the data from base64 encoded
# back to bytes. This is the reason for the conditional logic within `decode_audio` based
# on whether the bytes data is base64 encoded or standard bytes format.
# The output of the conversion present in the conditional structural validation below is
# to return the only input format that the audio transcription pipeline permits:
# a bytes input of a single element.
if isinstance(data, list) and all(isinstance(element, dict) for element in data):
encoded_audio = list(data[0].values())[0]
if isinstance(encoded_audio, str):
self._validate_str_input_uri_or_file(encoded_audio)
return decode_audio(encoded_audio)
elif isinstance(data, str):
self._validate_str_input_uri_or_file(data)
return data
@staticmethod
def _validate_str_input_uri_or_file(input_str):
"""
Validation of blob references to audio files, if a string is input to the ``predict``
method, perform validation of the string contents by checking for a valid uri or
filesystem reference instead of surfacing the cryptic stack trace that is otherwise raised
for an invalid uri input.
"""
def is_uri(s):
try:
result = urlparse(s)
return all([result.scheme, result.netloc])
except ValueError:
return False
valid_uri = os.path.isfile(input_str) or is_uri(input_str)
if not valid_uri:
raise MlflowException(
"An invalid string input was provided. String inputs to "
"audio files must be either a file location or a uri.",
error_code=BAD_REQUEST,
)
[docs]@experimental
@autologging_integration(FLAVOR_NAME)
def autolog(
log_input_examples=False,
log_model_signatures=False,
log_models=False,
log_datasets=False,
disable=False,
exclusive=False,
disable_for_unsupported_versions=False,
silent=False,
extra_tags=None,
): # pylint: disable=unused-argument
"""
This autologging integration is solely used for disabling spurious autologging of irrelevant
sub-models that are created during the training and evaluation of transformers-based models.
Autologging functionality is not implemented fully for the transformers flavor.
"""
# A list of other flavors whose base autologging config would be automatically logged due to
# training a model that would otherwise create a run and be logged internally within the
# transformers-supported trainer calls.
DISABLED_ANCILLARY_FLAVOR_AUTOLOGGING = ["sklearn", "tensorflow", "pytorch"]
def train(original, *args, **kwargs):
with disable_discrete_autologging(DISABLED_ANCILLARY_FLAVOR_AUTOLOGGING):
return original(*args, **kwargs)
with contextlib.suppress(ImportError):
import setfit
safe_patch(
FLAVOR_NAME, setfit.SetFitTrainer, "train", functools.partial(train), manage_run=False
)
with contextlib.suppress(ImportError):
import transformers
classes = [transformers.Trainer, transformers.Seq2SeqTrainer]
methods = ["train"]
for clazz in classes:
for method in methods:
safe_patch(FLAVOR_NAME, clazz, method, functools.partial(train), manage_run=False)